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As the share of photovoltaic （PV） power generation grows increasingly within electric power systems， the accurate probabilistic forecasting of PV output can be help for grid regulation and operation. To enhance forecasting precision， a probabilistic forecasting method for ultra-short-term photovoltaic output using the improved grey wolf optimization （GWO）， temporal convolutional neural networks and quantile random forests （TCN-QRF） is proposed. Firstly， the preprocessed time series dataset for PV output is converted into a supervised learning dataset. Then， the TCN is used to extract the temporal features of PV output as the input to the QRF， constructing the TCN-QRF model. Finally， the GWO is improved using the nonlinear convergence factor and Gaussian mutation strategy. The improved GWO efficiently selects hyperparameters for TCN-QRF， enabling a more precise probabilistic forecasting of PV output.

BACKGROUND The fast detection of Cu, Pb, Zn and Ni in geochemical samples helps to determine the level of heavy metal pollution, thus assisting in the employment of efficient treatments. OBJECTIVES To establish a method for the simultaneous determination of Cu, Pb, Zn and Ni in geochemical samples by AC Arc direct reading atomic emission spectrometry with solid sampling technique. METHODS With sodium fluoride, alumina, silicon dioxide and carbon powder as buffer agents and germanium as the internal standard element, the element analysis line pairs were optimized, and the exposure time was also optimized at 25s. The standard curve of synthetic silicate was corrected using national first-class reference materials (soil, rock and sediment), so as to reduce the interference of the matrix. Sensitive line and hyposensitive line were selected to enhance the accuracy as well as expand the measurable linear range of elements. Average value of two parallel analyses was adopted for further improved analytical precision. RESULTS The detection limits of Cu, Pb, Zn and Ni were 0.45μg/g, 1.46μg/g, 2.40μg/g and 1.58μg/g, respectively. The precision ranges of the method were 1.36%-6.25% for Cu, 1.17%-8.06% for Pb, 1.64%-9.68% for Zn and 0.91%-5.31% for Ni. Measured values with this method for the national first-class reference materials of soil, sediment and rock were consistent with the certified values (|△logC| ≤ 0.05), qualification rates of the actual samples and external quality control samples were all above 90%. CONCLUSIONS With simple operation, direct solid sampling technique is applied. The method reported here is more environmentally friendly compared to the acid dissolving method, sample contamination caused by reagent blank and vessels used is avoided at the same time. The average results of twice spectral acquisitions are calculated, which is more accurate and precise than the single analysis reported in the literature. All indicators of this method are better than the requirements of regulation (1:250000) DZ/T 0130.5-2006.

Lithium (Li) is vital to the world economy as an irreplaceable element in new-generation sustainable energy applications. Quantifying Li levels in clay minerals, a key new resource, supports extensive mineralization surveys. The major limitation in these analyses is complete decomposition, which often requires the use of strong acids and is labor-intensive, particularly in batch processing. This paper presents a partial extraction strategy for quantifying Li and multiple elements, employing ammonium hydrogen fluoride to extract from clay minerals while avoiding the use of various strong acids. Following extraction, the efficiency for Li reaches 98.61%, with values ranging from 62.68% to 91.43% for multiple elements including three major elements (Ca, Fe, Mg) and eleven trace elements (Be, Co, Cr, Cu, Ga, Mn, Ni, P, Rb, Sr, V), demonstrating favorable precision (RSD below 7.34%). This approach offers a promising tool for a simple and efficient batch analysis in the survey of mineralization in Li-bearing clay deposits. [ABSTRACT FROM AUTHOR]

In recent years, the issues of global warming and CO2 emission reduction have garnered increasing global attention. In the 21st Conference of the Parties (convened in Paris in 2015), 179 nations and the European Union signed a pivotal agreement to limit the global temperature increase of this century to well below 2 K above preindustrial levels. To fulfill this objective, extensive research has been conducted to use renewable energy sources as potential replacements for traditional fossil fuels. Among them, the production of hydrocarbon transportation fuels from CO2-neutral and renewable biomass has proven to be a particularly promising solution due to its compatibility with existing infrastructure. This review systematically summarizes research progress in the synthesis of liquid hydrocarbon biofuels from lignocellulose during the past two decades. Based on the chemical structure (including n-paraffins, iso-paraffins, aromatics, and cycloalkanes) of hydrocarbon transportation fuels, the synthesis pathways of these biofuels are discussed in four separate sections. Furthermore, this review proposes three guiding principles for the design of practical hydrocarbon biofuels, providing insights into future directions for the development of viable biomass-derived liquid fuels. [ABSTRACT FROM AUTHOR]

Olefins are important feedstocks in the production of fuels and many useful chemicals. Herein, cobalt molybdate (CoMoO4) was first synthesized by an environmentally friendly evaporation method and it exhibited excellent catalytic performance for the production of olefins by the selective hydrodeoxygenation (HDO) of ketones that can be derived from lignocellulose. On the basis of the characterization results, the excellent catalytic performance of CoMoO4 was attributed to its larger specific surface area, better pore structure, higher oxygen vacancy (or Mo5+ species) concentration, higher acid strength and higher concentration of acid sites. Under the optimized reaction conditions, 4-heptanone was almost completely converted to heptene, and a high carbon yield of heptene (96%) was achieved over a CoMoO4 catalyst with a Co/Mo atomic ratio of 0.8 (denoted as CoMoO4-0.8). The CoMoO4 catalyst is also active for the selective HDO of other lignocellulosic ketones (such as acetone, butanone, 2-pentanone, 3-pentanone, cyclopentanone, cyclohexanone, 5-nonanone and acetophenone) to their corresponding olefins. [ABSTRACT FROM AUTHOR]

The integration of vast quantities of clean energy causes intense impact on the power grid reliability. Digital Twin can be used to handle the high‐growth data flow generated in the power grid, and thus accelerate its digitalization process to fulfill the clean energy consumption target. As the traditional central cloud network structure can no longer effectively support the operation of digital twin power grid, the mobile edge network can be used as a reasonable substitute. To support the information interaction process of the mobile edge network, considering total profit, time delay and energy consumption, this paper proposes the mobile edge network communication and computing resource joint allocation model. On the basis, the GA‐PSO algorithm is proposed to solve the resource allocation optimization function. The feasibility and effectiveness of the proposed model is verified by the related simulation experiment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Osteocytes are the terminally differentiated bone cells resulted from bone formation. Although there are two distinct processes of bone formation, intramembranous and endochondral ossifications contributing to the formation of calvarial and long bones, it is not clear whether the distinct pathways determine the differences between calvaria and femoral cortical bone derived osteocytes. In the present study, we employed confocal structured illumination microscopy and mRNA‐sequencing analysis to characterize the morphologic and transcriptomic expression of osteocytes from murine calvaria and mid‐shaft femoral cortical bone. Structured illumination microscopy and geometric modelling showed round shaped and irregularly scattered calvarial osteocytes compared to spindle shaped and orderly arrayed cortical osteocytes. mRNA‐sequencing analysis indicated different transcriptomic profiles between calvarial and cortical osteocytes and provided evidence that mechanical response of osteocytes may contribute to geometrical differences. Furthermore, transcriptomic analysis showed that these two groups of osteocytes come from distinct pathways with 121 ossification‐related genes differentially expressed. Analysis of correlation between ossification and osteocyte geometries via a Venn diagram showed that several genes related to ossification, cytoskeleton organization and dendrite development were differentially expressed between calvarial and cortical osteocytes. Finally, we demonstrated that aging disrupted the organization of dendrites and cortical osteocytes but had no significant effects on calvarial osteocytes. Together, we conclude that calvarial and cortical osteocytes are different in various aspects, which is probably the consequence of their distinct pathways of ossification. [ABSTRACT FROM AUTHOR]

C28H34Cl2N2O2, orthorhombic, Pbcn (no. 60), a = 30.5739(5) Å, b = 8.00310(10) Å, c = 10.5257(2) Å, V = 2575.49(7) Å3, Z = 4, Rgt (F) = 0.0374, wRref (F2) = 0.1029, T = 100.01(10) K. [ABSTRACT FROM AUTHOR]

As a leading branch of deep learning, the convolutional neural network (CNN) is inspired by the natural visual perceptron mechanism of living things, showing great application in image recognition, language processing, and other fields. Photonics technology provides a new route for intelligent signal processing with the dramatic potential of its ultralarge bandwidth and ultralow power consumption, which automatically completes the computing process after the signal propagates through the processor with an analog computing architecture. In this paper, we focus on the key enabling technology of optical CNN, including reviewing the recent advances in the research hotspots, overviewing the current challenges and limitations that need to be further overcome, and discussing its potential application. [ABSTRACT FROM AUTHOR]