Antique wood preparation by inorganic salts treatment and its performance

onservation of historic timber structures is of great importance for cultural inheritance and community identity promotion. However, most of the current methods available for ancient architecture protection significantly affect their original appearance and aesthetic value and finding wood elements that are similar to the ones in existing historic timber structures is not easy. Here we report a simple and effective method to archaize wood, Castanopsis sclerophylla, by ferric chloride (FeCl3) treatment without significantly affecting its mechanical properties and durability. The lightness and the color indexes of treated wood are similar to the ancient wood sample. The mechanical properties of FeCl3 treated wood are not statistically different from the control. Our durability testing results indicated that FeCl3 treated wood has good decay resistance against Irpex lacteus and Trametes versicolor with a mass loss of less than 10 %. This study provides a convenient method for the restoration and protection of ancient buildings

Resource-Adaptive Newton's Method for Distributed Learning

Distributed stochastic optimization methods based on Newton's method offer significant advantages over first-order methods by leveraging curvature information for improved performance. However, the practical applicability of Newton's method is hindered in large-scale and heterogeneous learning environments due to challenges such as high computation and communication costs associated with the Hessian matrix, sub-model diversity, staleness in training, and data heterogeneity. To address these challenges, this paper introduces a novel and efficient algorithm called RANL, which overcomes the limitations of Newton's method by employing a simple Hessian initialization and adaptive assignments of training regions. The algorithm demonstrates impressive convergence properties, which are rigorously analyzed under standard assumptions in stochastic optimization. The theoretical analysis establishes that RANL achieves a linear convergence rate while effectively adapting to available resources and maintaining high efficiency. Unlike traditional first-order methods, RANL exhibits remarkable independence from the condition number of the problem and eliminates the need for complex parameter tuning. These advantages make RANL a promising approach for distributed stochastic optimization in practical scenarios

Estimating perfluorocarbon emission factors for industrial rare earth metal electrolysis

Rare earth (RE) metals have been widely applied in new materials, leading to their drastic production increase in the last three decades. In the production process featured by the molten-fluoride electrolysis technology, perfluorocarbon (PFC) emissions are significant and therefore deserve full accounting in greenhouse gas (GHG) emission inventories. Yet, in the ‘2006 IPCC Guidelines for National Greenhouse Gas Inventories’, no method currently exists to account for PFC emissions from rare earth metal production. This research aims to determine emission factors for industrial rare earth metals production through on-site monitoring and lab analysis of PFC concentrations in the exhaust gases from rare earth metal electrolysis. Continuous FTIR measurements and time-integrated samples (analysed off-site by high-precision Medusa GC–MS) were conducted over 24–60 h periods from three rare earth companies in China, covering production of multiple rare earth metals/alloys including Pr-Nd, La and Dy-Fe. The study confirmed that PFC emissions are generated during electrolysis, typically in the form of CF4 (∼90% wt of detected PFCs), C2F6 (∼10%) and C3F8 (<1%); trace levels of c-C4F8 and C4F10 were also detected. In general, PFC emission factors vary with rare earth metal produced and from one facility to another, ranging from 26.66 to 109.43 g/t-RE for CF4 emissions, 0.26 to 10.95 g/t-RE for C2F6, and 0.03 to 0.27 g/t-RE for C3F8. Converted to 211.60 to 847.41 kg CO2-e/t-RE for total PFCs, this emissions intensity for rare earths electrolysis is of lower (for most RE production) or similar (Dy-Fe production) level of magnitude to industrial aluminium electrolysis

Research on Reactive Power Optimization Control Method for Distribution Network with DGs Based on Improved Second-Order Oscillating PSO Algorithm

With the increasing penetration of distributed generation (DG) in the distribution network, the original network structure of the distribution network has been changed. In addition, the randomness and intermittency of renewable power generation will also have an impact on the voltage and power flow of the distribution network. To solve this problem, this paper proposes a reactive power optimization control method for distribution network with DGs based on second-order oscillating particle swarm optimization (PSO) algorithm with a constriction factor. Considering the economic operation of the distribution network, the proposed control method realizes the coordinated operation of the DGs and battery group with the conventional static reactive power compensation device, so as to improve the voltage quality of the distribution network and reduce the system network loss. At the same time, an improved second-order oscillating PSO algorithm is proposed to improve the speed and convergence of the multiobjective algorithm. Finally, the effectiveness of the proposed control method is verified by using MATLAB/Simulink on IEEE 33 bus distribution network with DGs in both static and dynamic situations

An Output Power Interval Control Strategy Based on Pseudo-Tip-Speed Ratio and Adaptive Genetic Algorithm for Variable-Pitch Tidal Stream Turbine

International audiencePower extraction has become a critical consideration in tidal stream turbine (TST) systems. In practice, the lumped disturbances under varying tidal current conditions may deteriorate the maximum power point tracking (MPPT) performance and cumulate fatigue damage overrated power. Besides, the conventional pitch controllers are sensitive to parameter uncertainties of the nonlinear TST system. In this paper, a novel output power internal control strategy based on pseudo-tip-speed ratio and adaptive genetic algorithm (PTSR-AGA) is proposed to improve the anti-interference ability and reliability. The proposed control scheme consists of two parts. The first part proposes the PTSR method for MPPT to predict the TST's operating point which contributes reducing the logical errors assigned to swell disturbances. The second part designed an AGA for the optimization of the pitch controller to conduct its angle delay. A reduced pitch control strategy is applied to the preprocessing of the pitch controller to reduce the mechanical wear over the rated power. The comparative simulation results validate the TST system can obtain a higher power efficiency of energy capture and a smoother power output with the proposed control strategies at full range of tidal current speed

Comparative Metabolomics Analysis of Stigmas and Petals in Chinese Saffron (Crocus sativus) by Widely Targeted Metabolomics

The dried stigmas of Crocus sativus, commonly known as saffron, are consumed largely worldwide because it is highly valuable in foods and has biological activities beneficial for health. Saffron has important economic and medicinal value, and thus, its planting area and global production are increasing. Petals, which are a by-product of the stigmas, have not been fully utilized at present. We compared the metabolites between the stigmas and petals of C. sativus using a non-targeted metabolomics method. In total, over 800 metabolites were detected and categorized into 35 classes, including alkaloids, flavonoids, amino acids and derivatives, phenols and phenol esters, phenylpropanoids, fatty acyls, steroids and steroid derivatives, vitamins, and other metabolites. The metabolite composition in the petals and stigmas was basically similar. The results of the study showed that the petals contained flavonoids, alkaloids, coumarins, and other medicinal components, as well as amino acids, carbohydrates, vitamins, and other nutritional components. A principal components analysis (PCA) and an orthogonal partial least-squares discriminant analysis (OPLS-DA) were performed to screen the different metabolic components. A total of 339 differential metabolites were identified, with 55 metabolites up-regulated and 284 down-regulated. The up-regulated metabolites, including rutin, delphinidin-3-O-glucoside, isoquercitrin, syringaresinol-di-O-glucoside, dihydrorobinetin, quercetin, and gallocatechin, were detected in the petals. The down-regulated metabolites were mainly glucofrangulin B, acetovanillone, daidzein, guaiazulene, hypaphorine, indolin-2-one, and pseudouridine. KEGG annotation and enrichment analyses of the differential metabolites revealed that flavonoid biosynthesis, amino acids biosynthesis, and arginine and proline metabolism were the main differentially regulated pathways. In conclusion, the petals of C. sativus are valuable for medicine and foods and have potential utility in multiple areas such as the natural spice, cosmetic, health drink, and natural health product industries

MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture

The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method

Changes in fruit anthocyanins, their biosynthesis-related enzymes and related genes during fruit development of purple and yellow passion fruits

Together with other polyphenols and flavonoids, anthocyanins have the capacity to serve as free radical scavengers against detrimental oxidants including reactive oxygen and nitrogen species. Moreover, the role of anthocyanin pigments as natural fruit colorings is quite common. In this study, the anthocyanin profile of purple and yellow passion fruit was determined at five developmental phases i.e., fruitlet, green, veraison, maturity, and ripening stage. Total flavonoids were abundantly found among other metabolites including anthocyanins and proanthocyanins. Purple passion fruits contained more than 2-times higher flavonoid content than yellow passion fruits at ripening stage. The findings showed that fruit maturation increased the amount of total flavonoids, anthocyanins, and procyanidins in the pulp of both varieties of passion fruit. Correlation analysis revealed that the passion fruit anthocyanin metabolism may be regulated by the enzymes C4H, 4CL, CHS, UFGT, and GST. The metabolism of anthocyanins in passion fruit may be significantly influenced by the genes PePAL4, PeCHS1, and PeGST7. New information from this work will help future research into the fundamental processes controlling the production of anthocyanins in passion fruit