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The odor of the foxtail millet is one of the most important indexes to evaluate the foxtail millet quality. Different sowing time results in different quality of the foxtail millet, and the volatiles can be used as fingerprint for distinguish them. The odor components of foxtail millets from spring sowing area and summer sowing area were investigated by means of gas chromatography-mass spectrometry (GC-MS) combined with simultaneous distillation extraction (SDE). A total of 53 volatile compounds were identified in all the samples containing 19 aldehydes, 4 alcohols, 7 ketones, 7 benzene derivatives, 12 hydrocarbons, 3 heterocycles and 1 acid. Aldehydes were the predominant odor components in foxtail millet. Compared with summer sowing area, there were abundant variety and high content of odor components in spring sowing area. Twenty-one volatiles were found to be odor-active compounds. The components with the highest OAVs in most sowing area were (E,E)-2,4-decadienal, (E)-2-nonenal, (E,E)-2,4-nonadienal and nonanal. Finally, 20 volatiles were screened out to distinguish the foxtail millet from spring-sowing area and summer-sowing area by multivariate statistical analysis. The results will be helpful to identify the foxtail millet from different sowing seasons and provide evidence that the spring-sowing foxtail millet shows more active odors.

The utilization of byproducts from foxtail millet polishing can reduce food loss and waste. Thus, it is necessary to know the chemical compounds from the millet and the segregation of the layers. The nutrients including minerals were compared among the husk, bran, and millet, and a LC-MS metabolomics analysis was also performed among them. The results showed that the protein, crude fat and 4 fatty acids, seven minerals, the nitrogen-containing compounds and phenolic acids were at much higher levels in the bran part than the husk and millet, whereas the husk only contained higher levels of dietary fibre, and some minerals. The millet section, as the edible part, contained the lowest level of chemical constituents. It illustrated that the bran part contained more functional and nutritional components than the millet and husk part. Therefore, the bran of the foxtail millet should be a food resources instead of wasting.

Abstract Electrochromic (EC) technology has been regarded as a promising energy‐saving technology in various applications, including smart windows, displays, thermal management, rear views, etc. Benefiting from the progress in electrochromic material synthesis, electrochromic electrode fabrication, and electrochromic device configuration design, the focus in electrochromic community has gradually shifted to multifunctional electrochromic devices (ECDs) in the era of Internet of Things. Multifunctional ECDs, such as electrochromic energy storage devices, multi‐color displays, deformable ECDs, smart windows, etc. have been showcased the ability to expand potential applications. In this review, the available device configurations, performance indexes and advanced characterization techniques for multifunctional ECDs are introduced and classified accordingly. The applications of multifunctional ECDs for energy storage, multicolor displays, deformable devices, self‐chargeable devices, smart windows, actuators, etc., are exemplified. The future development trends and perspectives of multifunctional ECDs are also overlooked. The aim of this review is to guide and inspire further efforts in the exploration of novel and advanced multifunctional ECDs.

A state-of-the-art review was delivered in the manuscript to analyze the current situations and the advantages/disadvantages of resilient deformation related issues to the unbound granular material (UGM), including testing methods, influencing factors and constitutive models, through which the bottlenecks in UGM resilient deformation characterizations were located and new theories and future research recommendations were proposed. Compared with other testing methods, the hollow cylinder test and true triaxial test are the only two to determine the anisotropic moduli of the UGM. Although significant advantages are observed in more advanced test apparatus such as true triaxial test, further improvements should be made, such as more advanced process identification control algorithm, to more conveniently control the nine independent hydraulic system and accurately measure UGM anisotropic properties. Confusions and conflicts in understanding the influencing factors to UGM resilient modulus were located, such as the stress history, the residual stress, the dry density, the gradation/fine content and the freeze-thaw cycle, based on which future research could be conducted. Through reviewing evolutions of the UGM constitutive models and comparing their representativeness and complexity comprehensively, major drawbacks were discovered and a new theory was proposed afterward to modify the UGM constitutive model, although further validations are still needed.

Reversibility of phase transformation, a basic requirement for functional properties of shape memory alloys, such as superelasticity, is severely affected by plastic deformation. Here, strain glass has been utilized to improve the reversibility of phase transformation. The incorporation of Al into Ti-Zr-Ni-Cu induces strain glass and endows the alloy with reversible phase transformation and thus ideal superelasticity. In situ x-ray diffraction and dynamic mechanical experiments were utilized to verify the mechanism for the conversion from transformation plasticity to superelasticity. This work provides a new way to improve the reversibility of phase transformation and expands the practical implications of strain glass.

To overcome incomplete point cloud data obtained from laser scanners scanning complex surfaces, multi-viewpoint cloud data needs to be aligned for use. A hybrid improved SAC-IA with a KD-ICP algorithm is proposed for local point cloud alignment optimization. The scanned point cloud data is preprocessed with statistical filtering, as well as uniform down-sampling. The sampling consistency initial alignment (SAC-IA) algorithm is improved by introducing a dissimilarity vector for point cloud initial alignment. In addition, the iterative closest point (ICP) algorithm is improved by incorporating bidirectional KD-tree to form the KD-ICP algorithm for fine point cloud alignment. Finally, the algorithms are compared in terms of runtime and alignment accuracy. The implementation of the algorithms is based on the Visual Studio 2013 software configurating point cloud library environment for testing experiments and practical experiments. The overall alignment method can be 40%~50% faster in terms of running speed. The improved SAC-IA algorithm provides better transformed poses, combined with the KD-ICP algorithm to select the corresponding nearest neighbor pairs, which improves the accuracy, as well as the applicability of the alignment.

Abstract Introduction Refractory cough, a chronic cough with an unclear diagnosis or poor treatment response. The symptoms are often stubborn and persistent, causing serious complications and lowering the patient's quality of life. Cough hypersensitivity syndrome (CHS) is proposed as a potential cause, and reducing sensory nerve hyperresponsiveness is suggested as an effective treatment. However, current drugs have low efficacy and benefit rates and numerous side effects. This trail proposes using duloxetine, a selective 5-HT and norepinephrine reuptake inhibitor, as a potential treatment for refractory cough, which has shown promise in treating pain and depression. Duloxetine may inhibit pain conduction and oxidative stress in peripheral nerves by inhibiting the activity of TRPV1 channels, which play an important role in the peripheral afferent pathway of refractory cough. Meanwhile, the antidepressant effects of duloxetine may also play a role in the treatment of refractory cough. Methods and analysis This is a single-center, prospective, randomized, double-blind, and controlled trial. A total of 98 individuals will be randomized in a 1:1 ratio to duloxetine group and placebo control group (starting with 20 mg QD, increasing 20 mg daily until 20 mg TID). After a screening period, the second stage runs from baseline to the 42nd (last) day of treatment, with follow-up visits on the 3rd, 7th, 14th, 21st, 28th, 35th, 42nd and 49th days. The main end-stage observation indicators include objective cough frequency, cough visual analog scale (VAS), cough symptom score, Leicester Cough Questionnaire (LCQ), and cough evaluation test (CET); the secondary end-stage observation indicators include capsaicin cough sensitivity, Patient Health Questionnaire-9 (PHQ-9), Major Depression Inventory (MDI), the Generalized Anxiety Disorder-7 scale (GAD-7), Life Events Scale (LES-32), induced sputum supernatant. The safety measures will be AEs/SAEs, vital signs, liver and kidney function, fecal occult blood test. Discussion This study is the first randomized, double-blind, and controlled clinical trial investigating the use of duloxetine in the treatment of refractory coughs. The study aims to provide a high-quality basis for evaluating the efficacy and safety of duloxetine for this condition. Trial registration Our study was registered in the Chinese Clinical Trials Register ( www.chictr.org.cn/ ) (ChiCTR2000037429) in 28/08/2020.

Satellite-derived bathymetry (SDB) is an effective means of obtaining global shallow water depths. However, the effect of inherent optical properties (IOPs) on the accuracy of SDB under different water quality conditions has not been clearly clarified. To enhance the accuracy of machine learning SDB models, this study aims to assess the performance improvement of integrating the quasi-analytical algorithm (QAA)-derived IOPs using the Sentinel-2 and ICESat-2 datasets. In different water quality experiments, the results indicate that four SDB models (the Gaussian process regression, neural networks, random forests, and support vector regression) incorporating QAA-IOP parameters equal to or outperform those solely based on the remote sensing reflectance (Rrs) datasets, especially in turbid waters. By analyzing information gains in SDB, the most effective inputs are identified and prioritized under different water qualities. The SDB method incorporating QAA-IOP can achieve an accuracy of 0.85 m, 0.48 m, and 0.74 m in three areas (Wenchang, Laizhou Bay, and the Qilian Islands) with different water quality. Also, we find that incorporating an excessive number of redundant bands into machine learning models not only increases the demand of computing resources but also leads to worse accuracy in SDB. In conclusion, the integration of QAA-IOPs offers promising improvements in obtaining bathymetry and the optimal feature selection should be carefully considered in diverse aquatic environments.

Due to the limitations of traditional geometric error measurement, the measurement accuracy of long-stroke geometric errors is generally not high and the operation is complicated. In response to the above situation, in this study, a geometric error measurement system is built with a laser beam as the reference line and 2D position sensitive detector as the photoelectric conversion device. The single measurement range is 40 m, and the measurement range is further expanded through the principle of segmented splicing. Using an ultra-long guide rail as the measurement object for straightness measurement, the experimental results are similar to those of a laser interferometer. The uncertainty analysis model was obtained through the analysis of quantity characteristics, and based on this, the variance synthesis theorem and probability distribution propagation principle were studied to form two uncertainty synthesis methods. The measurement evaluation results showed that the two methods were basically consistent. The work provided a reference method for the uncertainty evaluation of position-sensitive detector measurement systems in the future.

Pretreatment methods affect the properties of bean starch and thus the application of bean products. This article reviewed the effects of different pretreatment methods on the properties of bean starch such as particle morphology, molecular structure, solubility, pasting properties and resistant starch content, and analyzed the impact of those pretreatment methods on the application of beans in the development of low GI foods. Hydrothermal, ultra-high pressure, dry heated, irradiated, ultrasonic and enzymatic treatments under certain conditions increased the content of resistant starch or amylose starch in bean starch, which was beneficial for the application of bean in the development of low GI food. In addition, this paper summarized the current status of the application of different pretreated bean products in the development of low GI food, and provided an outlook on the future prospects.

Abstract The traditional Grey Wolf Optimization algorithm (GWO) has received widespread attention due to features of strong convergence performance, few parameters, and easy implementation. However, in actual optimization projects, there are problems of slow convergence speed and easy to fall into local optimal solution. The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO) in response to the above problems. The Cauchy-Gaussian mutation operator is introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm. This work retains outstanding grey wolf individuals through the greedy selection mechanism to ensure the convergence speed of the algorithm. An improved search strategy was proposed to expand the optimization space of the algorithm and improve the convergence accuracy. Experiments are performed with 16 benchmark functions covering unimodal functions, multimodal functions, and fixed-dimension multimodal functions to verify the effectiveness of the algorithm. Experimental results show that compared with four classic optimization algorithms, particle swarm optimization algorithm (PSO), whale optimization algorithm (WOA), sparrow optimization algorithm (SSA), and farmland fertility algorithm (FFA), the CG-GWO algorithm shows better convergence accuracy, convergence speed, and global search ability. The proposed algorithm shows the same better performance compared with a series of improved algorithms such as the improved grey wolf algorithm (IGWO), modified Grey Wolf Optimization algorithm (mGWO), and the Grey Wolf Optimization algorithm inspired by enhanced leadership (GLF-GWO).

A three-dimensional (3D) viscoelastic asphalt pavement, of which the surface was composed of asphalt mixture, was developed using ABAQUS and a moving 3D tire-pavement contact stress with different braking slip ratios was incorporated and programmed into ABAQUS by FORTRAN language. Five braking slip ratios were considered (Dual Tire Assembly (DTA): 0 (free rolling), 1.8%, 3.6%, 5.4%, and 7.0% (full braking); Wide Base Tire (WBT): 0 (free rolling), 1.5%, 3.0%, 4.5%, and 7.0% (full braking)). Results showed that the viscoelastic stress and strain of the asphalt mixture were significantly influenced by tire braking slip ratio and pavement temperature for both DTA and WBT loading conditions. As the slip ratio and the temperature increased, the stress and strain of the asphalt layer would increase and their positions shifted upwards to the surface of the asphalt layer. Comparatively, the peak shear strain was the most significantly influenced factor and its amplitude may increase by 10 times. However, the bottom strain of the asphalt layer was far less affected by tire breaking behaviours.

Zinc-ion batteries (ZIBs) are increasingly recognized as highly promising candidates for grid-scale energy storage systems due to their cost-effectiveness, environmental friendliness, and high security. Despite recent advancements in the research of cathode materials, Zn anodes, and electrolytes, several challenges persist and must be addressed, including cathode dissolution, generation of by-products, and zinc dendrite formation, which hinder the future application of ZIBs. In this review, we systematically summarize the recent developments in electrospinning technology within ZIBs. First, the principle technical parameters and subsequent thermal treatment of electrospinning technology are discussed, and then the synthetic preparation, morphologies, and electrochemical performance of electrospun nanostructured electrodes in ZIBs are comprehensively reviewed. Finally, some perspectives on research directions and optimization strategies for electrospinning technology in energy applications are outlined.

The global energy crisis and growing environmental concerns provide a strong impetus for the development of fuel-efficient hydraulic excavators (HEs). The boom potential energy of a conventional HE is consumed by throttling in a lowering process, which is a major reason for energy inefficiency. To solve the issue, this paper presents a flywheel-based boom energy regeneration system for HEs using load-sensing systems. The otherwise wasted boom potential energy is regenerated by a pump/motor and stored in a flywheel. The recaptured energy is reused in the form of pressure energy released to the pump outlet. The energy efficiencies of a conventional load-sensing system and the proposed system were analyzed. A control strategy was proposed to optimize the energy-saving procedure. To obtain a more reliable simulation model, a coasting experiment of the flywheel was carried out to obtain the key parameters related to the friction of the flywheel. A 4-t HE in our laboratory was selected as a study case to investigate the energy-saving effect of the flywheel-based boom energy regeneration system. Numeric simulations showed that compared with a conventional load-sensing system, the energy-saving rate was about 32.7% in a typical digging and dumping cycle. These findings indicated that the flywheel-based boom energy regeneration system is promising for developing energy-efficient HEs.

Background: Sepsis is a fatal disease with a high rate of morbidity and mortality, during which acute lung injury is the earliest and most serious complication. Injury of pulmonary microvascular endothelial cells (PMVECs) induced by excessive inflammation plays an important role in sepsis acute lung injury. This study is meant to explore the protective effect and mechanism of ADSCs exosomes on excessive inflammation PMVECs injury. Results: We successfully isolated ADSCs exosomes, the characteristic of which were confirmed. ADSCs exosomes reduced excessive inflammatory response induced ROS accumulation and cell injury in PMVECs. Besides, ADSCs exosomes inhibited excessive inflammatory response induced ferroptosis while upregulated expression of GPX4 in PMVECs. And further GPX4 inhibition experiments revealed that ADSCs exosomes alleviated inflammatory response induced ferroptosis via upregulating GPX4. Meanwhile, ADSCs exosomes could increase the expression and nucleus translocation of Nrf2, while decrease the expression of Keap1. miRNA analysis and further inhibition experiments verified that specific delivery of miR-125b-5p by ADSCs exosomes inhibited Keap1 and alleviated ferroptosis. In CLP induced sepsis model, ADSCs exosomes could relieve the lung tissue injury and reduced the death rate. Besides, ADSCs exosomes alleviated oxidative stress injury and ferroptosis of lung tissue, while remarkably increase expression of Nrf2 and GPX4. Conclusion: Collectively, we illustrated a novel potentially therapeutic mechanism that miR-125b-5p in ADSCs exosomes could alleviate the inflammation induced PMVECs ferroptosis in sepsis induced acute lung injury via regulating Keap1/Nrf2/GPX4 expression, hence improve the acute lung injury in sepsis.

Chlorophyll-a concentration (Chla) is an important index for water eutrophication. In this study, retrieval models of Chla were established based on the measured water spectra, spectral response function, measured Chla and the corresponding Sentinel-2 imagery of the Pingzhai Reservoir, the first large-scale trans-regional, trans-basin, and long-distance source reservoir in Guizhou. The retrieved results from 11 Sentinel-2 from 2018 to 2021 were used to analyze the spatiotemporal variations in Chla and the influence of different environmental factors on their spatial differentiation, providing a powerful approach for monitoring Chla in the Pingzhai Reservoir. Our binomial function model based on B8*(B7-B5) of Sentinel-2 yielded acceptable to high fitting accuracies, accounting for 89% of the variation in Chla. Overall, the Chla was relatively low, with a mean value of 10.24 μg/L. Higher Chla were distributed in the catchment area, such as the Nayong River and the dam. Moreover, significant seasonal fluctuations and intra-year changes were observed . Spatio-temporal variations in Chla were influenced by human activities and environmental factors such as Dissolved Oxygen (DO), Total Nitrogen (TN), and Ammoniacal Nitrogen (NH4+-N). Our work provided compelling evidence that Sentinel-2 could be used for quantitative inversion of Chla in Pingzhai Reservoir.

In modern society, there is an increasing demand for energy storage solutions with superior energy density. Due to the low capacity of commercial graphite anode (372 mAh/g), high capacity and high stability anode materials for lithium-ion batteries (LIBs) have become a research hotspot. Among all kinds of anode materials, Ga-based materials have attracted much attention due to their high theoretical capacity and self-healing properties. These characteristics make it very suitable for the anode of high-performance LIBs and drive its commercialization. In this review, we analyzed the modification strategy, the feasibility of Ga-based lithium batteries, and how to utilize the electrochemical properties of Ga efficiently, to provide some insights into the design of high-performance batteries.

ObjectiveTo analyze the clinical characteristics and risk factors of viral shedding time in mildly symptomatic and asymptomatic patients with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (BA.2 and BA2.2) infection in Shanghai, and the effect of traditional Chinese medicine (TCM) treatment, so as to provide a reference basis for epidemic prevention, control and clinical treatment.MethodsA total of 6,134 asymptomatic or mildly symptomatic Omicron-infected patients admitted to Tianhua Road fangcang shelter hospital in Jinshan, Shanghai, between April 2022 and May 2022 were included. Demographic characteristics and clinical histories were collected and compared in subgroups according to the different durations of viral shedding. Spearman's correlation analysis was performed to explore the association between virus shedding time and clinical variables. Multiple linear regression was used to evaluate the risk factors for viral shedding time.ResultMost patients with asymptomatic and mildly symptomatic Omicron infection were male, and more than half of patients had a viral shedding time of 8–15 days. The patients were divided into three groups according to the time of viral shedding: short-duration (≤ 7 days), intermediate-duration (8–15 days) and long-duration group (≥16 days). The proportion of patients aged ≤ 29 years was the highest in the short-duration group (30.2%), whereas the proportion of patients aged 50–64 yeas was the highest in the long-duration group (37.9%). The proportion of patients with the chronic non-communicable diseases among the short-, intermediate- and long-duration groups was 6.2, 9.4, and 14.9%, respectively. Among them, hypertension was the most found (4.9, 7.8, and 11.7%, respectively). By multivariate analyses, we identified that viral shedding time of Omicron variants was independently negatively correlated with male patients, TCM treatment, and manual laborers, while it was independently positively associated with age and hypertension. Additionally, TCM treatment could significantly shorten the length of viral shedding time, especially for men, age ≥30 years, comorbid chronic non-communicable diseases, unemployed people and manual worker.ConclusionsOur results suggested that age and hypertension were independent risk factors for the duration of viral shedding in asymptomatic and mildly symptomatic omicron infected patients. TCM can effectively shorten viral shedding time.

This meta-analysis evaluated the diagnostic value of ultrasonic elastography in detecting placental stiffness in the diagnosis of preeclampsia (PE). A systematic search was conducted in the EMBASE, Web of Science, Cochrane Library, Scopus database, and PubMed databases to identify studies published before June 2023 using ultrasonic elastography to diagnose PE. The sensitivity, specificity, and diagnostic odds ratio of ultrasonic elastography for diagnosing PE were calculated, and a summary receiver operating characteristic curve model was constructed. The degree of heterogeneity was estimated using the I2 statistic, and a meta-regression analysis was performed to explore its sources. A protocol was determined previously (PROSPERO: CRD42023443646). We included 1188 participants from 11 studies, including 190 patients with PE and 998 patients without PE as controls. Overall sensitivity and specificity of ultrasonic elastography in detecting placental stiffness for the diagnosis of PE were 89% (95% CI: 85–93) and 74% (95% CI: 51–89), respectively. The I2 values for sensitivity and specificity were 59% (95% CI: 29–89) and 96% (95% CI: 95–98), respectively. The area under the receiver operating characteristic curve was 0.90 (95% CI: 0.87–0.92). The meta-regression analysis showed no significant heterogeneity. Ultrasonic elastography exhibits good diagnostic accuracy for detecting placental stiffness and can serve as a non-invasive tool for differentially diagnosing PE.

High-temperature shape memory alloys (HTSMAs) show enormous potential for applications as energy conversion devices, actuators and sensors in automotive, aerospace, and energy exploration industries, but they suffer from strength decrease and microstructural instability at elevated temperatures, rendering it difficult to achieve satisfactory high-temperature superelasticity. Here, a novel Ti20Hf15Zr15Cu25Ni25 high-entropy HTSMA exhibiting large superelasticity with a fully recoverable strain of 4.0% at temperatures up to 285°C is designed with the high-entropy alloy concept by employing the sluggish diffusion and severe lattice distortion effects to suppress thermal softening. This work illuminates the design of novel high-performance functional materials for high-temperature applications.

Abstract Exosomes have been associated with chemoresistance in various cancers, but such a role in ovarian cancer is not yet clear. Here, using in vitro cell-based and in vivo mouse model experiments, we show that downregulation of O-GlcNAcylation, a key post-translational protein modification, promotes exosome secretion. This increases exosome-mediated efflux of cisplatin from cancer cells resulting in chemoresistance. Mechanistically, our data indicate that downregulation of O-GlcNAclation transferase (OGT) reduces O-GlcNAclation of SNAP-23. Notably, O-GlcNAcylation of SNAP-23 is vital for regulating exosome release in ovarian cancer cells. Reduced O-GlcNAclation of SNAP-23 subsequently promotes the formation of soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex consisting of SNAP-23, VAMP8, and Stx4 proteins. This enhances exosome release causing chemoresistance by increasing the efflux of intracellular cisplatin.

Sustainable organic materials have gained considerable attention as electrodes for zinc-ion batteries (ZIB) due to their high theoretical capacity, structural versatility, and environmental friendliness. However, issues of inferior capacities and poor rate performance owing to limited inherent electronic conductivity and severe dissolution still persist. Herein, sandwich-structured perylene diimide-ethylene diamine/graphene (PDI-EDA/EG) composites are judiciously designed and synthesized. The two-dimensional graphene host can interact with the PDI-EDA polymer through π–π stacking, endowing accelerated ion/electron transfer, abundant active sites, excellent structural integrity, and mitigated solubility of the hybrid electrodes. When evaluated as an anode in ZIB, the hybrid electrode delivers a high capacity (110.2 mAh g−1 at 0.1 A g−1), superior rate capability (88.9 mAh g−1 at 5 A g−1), and exceptional durability (93.4% capacity retained after 1000 cycles). The structure evolution of the hybrid electrode during the insertion/extraction cycle was investigated by ex-situ Fourier transform infrared spectra (FTIR) and X-ray photoelectron spectroscopy (XPS), revealing the reversible Zn2+ storage at carbonyl sites. In addition, a prototype rocking-chair ZIB cell was constructed with a zinc pre-intercalated MnO2 cathode, displaying an ultrahigh energy density of 54.9 Wh kg−1 at a power density of 42.5 W kg−1 and excellent stability with negligible capacity decay after 1000 cycles.

As is known to all, the heterogeneous green scheduling objects have the intelligent feedback related to the efficiencies corresponding to the schemes, which has been largely ignored in most existing studies. That is why the existing optimization dynamics in green meta-heuristics scheduling algorithms, generally appear underpowered and vulnerable in the face of the rapid extension from homogeneity to heterogeneity of scheduling objects. Then, with respecting and ingeniously leveraging hardware (i.e., heterogeneous scheduling objects) intelligence, an efficient meta-heuristics algorithm with re-energized majorization dynamics for heterogeneous greener scheduling (i.e., CAr_FI(HS)), is proposed. The experimental results show that compared with the other meta-heuristics scheduling algorithms, CAr_FI(HS) has obvious advantages in the overall performance and the solution quality, for both data intensive and computing intensive instances.

This paper focuses on the algorithms design of heterogeneous green scheduling for energy conservation and emission reduction in cloud computing. In essence, the real time, dynamic and complexity of heterogeneous scheduling require higher algorithm performance; however, the swarm intelligent algorithms although with some improvements, still exist big imbalances between local exploration and macro development or between route (solution) diversity and faster convergence. In this paper, a greener heterogeneous scheduling algorithm via blending pattern of particle swarm computing intelligence and geometric Brownian motion, is proposed, based on our earlier theoretical breakthroughs on G-Brownian motion and through a series of mathematical derivations or proofs; furthermore, in order for suitable for the hybrid processor architecture of the scheduling management server, the algorithm is designed in parallel with deep fusion of coarse-grained and master-slave models. A large number of experimental results are given. Compared with most newly published scheduling algorithms, there are significant advantages of the proposed algorithm on the dynamic optimization performance for consistent or semiconsistent and large inconsistent scheduling instances, although with lower improvement factors for small inconsistent instances.

Abstract Sodium-ion capacitors (SICs) have received increasing interest for grid stationary energy storage application due to their affordability, high power, and energy densities. The major challenge for SICs is to overcome the kinetics imbalance between faradaic anode and non-faradaic cathode. To boost the Na+ reaction kinetics, the present work demonstrated a high-rate MnS-based anode by embedding the MnS nanocrystals into the N, S-co-doped carbon matrix (MnS@NSC). Benefiting from the fast pseudocapacitive Na+ storage behavior, the resulting composite exhibits extraordinary rate capability (205.6 mAh g−1 at 10 A g−1) and outstanding cycling stability without notable degradation after 2000 cycles. A prototype SIC was demonstrated using MnS@NSC anode and N-doped porous carbon (NC) cathode; the obtained hybrid SIC device can display a high energy density of 139.8 Wh kg−1 and high power density of 11,500 W kg−1, as well as excellent cyclability with 84.5% capacitance retention after 3000 cycles. The superior electrochemical performance is contributed to downsizing of MnS and encapsulation of conductive N, S-co-doped carbon matrix, which not only promote the Na+ and electrons transport, but also buffer the volume variations and maintain the structure integrity during Na+ insertion/extraction, enabling its comparable fast reaction kinetics and cyclability with NC cathode.

Heterogeneous green scheduling in virtual cloud is an urgent need of human sustainable developments. However, on the one hand, there is still considerable space beyond reach of the hardware energy regulation mode; on the other hand, as the core of green software methods, meta-heuristics algorithms are still underperforming in heterogeneous scheduling, although with many achievements in homogeneous scheduling. In this paper, an efficient new meta-heuristics algorithm is presented (i.e., GHSA_di), including the co-evolutionary dynamics equation emphasizing on and taking advantage of the hardware energy-regulation principles. The experimental results show that compared with the other three meta-heuristic scheduling algorithms, GHSA_di algorithm has obvious advantages in overall performance, energy saving and scalability, for both data intensive and computing intensive instances.

Well-testing stage analysis is an important way for oilfield operation state decision-making and reservoir management. However, due to the variability and nonlinearity of the downhole data caused by the complex exploration activities and the differences of petroleum type and geological conditions, the classical methods are ineffective in feature extraction, learning network construction, and classifier optimization. In this work, we propose a new well-testing stage classification method based on a deep vector learning model (DVLM). The novelty of this study lies in the combination of multi-feature extraction, deep learning and feature vector mapping. The proposed method can overcome the problems of poor feature representation ability and poor classification model generalization ability in the existing machine learning methods, which mainly caused by the non-optimized training network structure and the unreasonable classifier design,. Firstly, the initial features are obtained by four classical methods. Then a five layers deep belief network embedded with the mutual information coefficient method is implemented for further feature extraction and purification. Finally, the optimized learning vector quantization classifier outputs the predicted tags. For model training and testing 572 field samples total of 4004 data streams are used. By considering the classification errors and accuracy metrics, the neurons number of deep learning network and the classifier are tuned, and an optimal and stable framework is obtained. Comparative experimental results with several classical integration models show that the proposed model achieves the highest classification accuracy of 98.065% as well as the least of features (nine). The results demonstrate that the proposed model has excellent performance in improving the classification accuracy and completing the feature compression. Moreover, the proposed model has very important practical significance for guiding the automatic analysis and processing oil and gas data.

Introduction: Rural residents have been shown to have limited access to reliable health information and therefore may be at higher risks for the adverse health effects of the COVID-19. The aim of this research is 2-fold: (1) to explore the impacts of demographic factors on the accessibility of health information; and (2) to assess the impacts of information channels on the reliability of health information accessed by rural residents in China during the COVID-19 outbreak.Methods: Mixed methods research was performed to provide a relatively complete picture about the accessibility and reliability of health information in rural China in the face of the COVID-19. A quantitative research was conducted through surveying 435 Chinese rural residents and a qualitative study was performed through collecting materials from one of the most popular social media application (WeChat) in China. The logistic regression techniques were used to examine the impacts of demographic factors on the accessibility of health information. The Content analysis was performed to describe and summarize qualitative materials to inform the impacts of information channels on the reliability of health information.Results: Age was found to positively associate with the accessibility of health information, while an opposite association was found between education and the accessibility of health information. Rural residents with monthly income between 3,001 CNY and 4,000 CNY were the least likely to access health information. Rural residents who worked/studied from home were more likely to access health information. Meanwhile, health information tended to be derived from non-official social media channels where rumors and unverified health information spread fast, and the elderly and less-educated rural residents were more likely to access health misinformation.Conclusions: Policy makers are suggested to adopt efficient measures to contain the spread of rumors and unverified health information on non-official social media platforms during the outbreak of a pandemic. More efforts should be devoted to assist the elderly and less-educated rural residents to access reliable health information in the face of a pandemic outbreak.

The high entropy alloy concept offers unique opportunities for the design of advanced materials with unprecedented properties. However, hitherto most of the research efforts are focused on structural materials. Here, by employing the core effect of severe lattice distortion in high entropy alloys, a novel functional NiCuTiHf0.6Zr0.4 high entropy alloy exhibiting intriguing wide-temperature-range perfect superelasticity in a broad temperature range from 263 to 463 K and giant elastocaloric effect with the maximum adiabatic temperature change of 9.3 K is developed. This work opens the door for designing high-performance superelastic and elastocaloric functional materials with the high entropy alloy concept.

Meta-magnetic shape-memory alloys combine ferroelastic order with ferromagnetic order and exhibit attractive multifunctional properties, but they are extremely brittle, showing hardly any tensile deformability, which impedes their practical application. Here, for the first time, an Ni–Cu–Co–Mn–In microwire has been developed that simultaneously exhibits a magnetic field-induced first-order meta-magnetic phase transition and huge tensile superelasticity. A temperature-dependent in situ synchrotron high-energy X-ray diffraction investigation reveals that the martensite of this Ni43.7Cu1.5Co5.1Mn36.7In13 microwire shows a monoclinic six-layered modulated structure and the austenite shows a cubic structure. This microwire exhibits an oligocrystalline structure with bamboo grains, which remarkably reduces the strain incompatibility during deformation and martensitic transformation. As a result, huge tensile superelasticity with a recoverable strain of 13% is achieved in the microwire. This huge tensile superelasticity is in agreement with our theoretical calculations based on the crystal structure and lattice correspondence of austenite and martensite and the crystallographic orientation of the grains. Owing to the large magnetization difference between austenite and martensite, a pronounced magnetic field-induced magnetostructural transition is achieved in the microwire, which could give rise to a variety of magnetically driven functional properties. For example, a large magnetocaloric effect with an isothermal entropy change of 12.7 J kg−1 K−1 (under 5 T) is obtained. The realization of magnetic-field- and tensile-stress-induced structural transformations in the microwire may pave the way for exploiting the multifunctional properties under the coupling of magnetic field and stress for applications in miniature multifunctional devices.

Abstract The friction properties of wet clutches are highly dependent on the surface tribofilms formed by automatic transmission fluids (ATFs). Here, four commercial ATFs were evaluated with a disc-on-disc tribometer to study tribofilm formation on steel surfaces and the effects of tribofilms on the friction properties. The chemical composition, stoichiometry, structure, and thickness of the tribofilms were investigated by scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDX), secondary ion mass spectrometry (SIMS), and X-ray photoelectron spectroscopy (XPS). Calcium phosphate (CaP) tribofilms form on the friction surface with all ATFs, which contributes to their antishudder characteristics. The thickness and surface coverage of CaP tribofilms are positively correlated with their antishudder properties.

In this paper, we present an efficient method for single image super-resolution based on the dense network. First, considering that the dense architectures reuse all the previous features in each layer and cause a huge memory overhead, we simplify the dense architecture and only reuse the hierarchical features in the reconstruct layer; therefore, the redundancy in dense architecture is reduced. Subsequently, since the information of the degraded low-resolution image is much less than the potential high-resolution image, we propose a non-degenerate layer to address the degeneracy problem caused by the loss of input information. In the non-degenerate layer, we introduce a skip connection to the linear transformation to eliminate the singularity and utilize an invertible nonlinear activation function to avoid the dead zone. In addition, the direct reconstruction scheme is adopted for efficient models, and the structural similarity is utilized for better human perception. The experiments show promising results in terms of quantitative and qualitative results, which indicates that our method is effective and superior.

(1) Background: In order to study the effects of different electric cookers on the nutritional components of millet porridge, five different electric cookers (No. 1–5) were selected to cook millet porridge, then sensory and nutritional components in millet porridge, millet soup, and millet grains were analyzed; (2) Methods: Using principal component and cluster analysis, a variety of nutritional components were comprehensively compared; (3) Results: The results showed that among the different cooked samples, the content of amylose and reducing sugars was the highest in the samples cooked by electric cooker No. 3. The electric cooker No. 4 samples had the highest sensory evaluation score, crude fat, and protein content. The contents of ash, fatty acids, bound amino acids, and minerals were the highest in the electric cooker No. 5 samples. The sensory evaluation score and content of crude fat, ash, reducing sugars, direct starch, and Cu were higher in millet grains than in millet soup or porridge. The content of fatty acids, protein, amino acid, Zn, Fe, Mg, Mn, and Ca was highest in millet soup. Different electric cookers produced millet porridge with varying nutritional levels; (4) Conclusions: This study provides a reference for the further development of new electric cookers.

Next‐generation soft electronics are expected to be intrinsically stretchable, skin conformable, and fully integrated with diverse receptive modules to facilitate bidirectional human–machine interactions. Receptive touch sensors, in particular, should provide stable touch sensing outputs without being affected by external force‐induced strains. Herein, the design and fabrication of an iontronic touch sensing matrix, based on the fringe‐field capacitive mechanism, are introduced for robust touch mapping under large deformation. Enabled by our well‐formulated ink, ionic gel electrodes are directly inkjet printed onto elastomeric substrate to impart superior transparency and elasticity, and hybridized with a customized electronic circuitry through electrical double layers (EDLs) for the multiplexing and transduction of capacitive signals. Notably, the coplanar “interlocking‐diamond” electrode layout in a stretchable modality is adopted for the first time, which helps to boost touch sensitivity and suppress strain‐induced artifacts under static/dynamic deformations. For practical applications, the iontronic matrix demonstrates the capabilities of proximity sensing, multitouch detection, and gesture communication in real time, leading to a robust touch sensing interface that captures high‐fidelity signals during complex human–machine interactions.

Abstract Reversible electrochemical mirror (REM) electrochromic devices with electrochemical tunability in multiple optical states are exciting alternatives to conventional electrochromic smart windows. Electrochromic devices are studied extensively, yet widespread adoptions have not been achieved due to problems associated with durability, switching speed, limited options on optical states, and cost. In this study, a REM electrochromic device based on CuSn alloy is developed, which offers highly reversible switching between transparent, greyish‐blue, and mirror states via reversible electrodeposition and dissolution. The alloying element, Sn acts as an electrochemical mediator, which facilitates the electrodeposition and dissolution of Cu. The CuSn‐based REM device shows superior cycling stability for 2400 cycles (transmittance mode) and 1000 cycles (reflectance mode). The electrodeposited CuSn alloy film is resistant to surface oxidation in ambient air, with a 2.9% difference in reflectance at 2000 nm after 3 days. In addition, the alloy film exhibits excellent NIR reflectance property with thermal modulation of 18.5 °C at a high temperature of 180 °C. The REM device with zero power consumption maintains its mirror state for at least 100 min, making it a promising candidate for energy‐efficient applications.

An interferometric semiconductor x-ray detection system is proposed in this paper. The system is based on the RadOptic effect, and it utilizes Fabry–Perot interferometry to measure radiation-induced changes in the optical refractive index of a semiconductor (GaAs). In this work, the intrinsic time resolution and the sensitivity of a Fabry–Perot interferometric sensor were systemically studied. Based on the transient free carrier absorption model, the prototype system was established to quantitatively measure the time-dependent x-ray flux with the deconvolution algorithm for the first time. The time resolution of the detection system was approximately 21 ps, and the output signal induced by an x-ray pulse showed a high signal-to-noise ratio and immunity to electromagnetic interference. This interferometer will enable x-ray bang-time and fusion burn-history measurements in inertial confinement fusion with higher time resolution.

Increasing attention has been paid to the next generation of ‘green’ electronic devices based on renewable nanocellulose, owing to its low roughness, good thermal stability and excellent optical properties. Various proof-of-concept transparent nanopaper-based electronic devices have been fabricated; these devices exhibit excellent flexibility, bendability and even foldability. In this review, we summarize the recent progress of transparent nanopaper that uses different types of nanocellulose, including pure nanocellulose paper and composite nanocellulose paper. The latest development of transparent and flexible nanopaper electronic devices are illustrated, such as electrochromic devices, touch sensors, solar cells and transistors. Finally, we discuss the advantages of transparent nanopaper compared to conventional flexible plastic substrate and the existing challenges to be tackled in order to realize this promising potential.

A hydraulic excavator (HE) is a typical piece of construction equipment and is widely used in various construction fields. However, the poor energy efficiency of HEs results in serious energy waste and has aroused the attention of researchers. Furthermore, rising fuel prices and increasing stringent waste gas emission legislation sparked demand for ways to improve energy efficiency. Recovering the otherwise wasted boom potential energy of a conventional HE by proper methods offers the potential to improve the fuel efficiency of HEs. In this paper, a mechanical energy recovery system consisting of a pump/motor and a flywheel is presented for HEs using a load sensing system. When the boom moves down, the boom potential energy is converted into mechanical energy by the boom cylinder and the pump/motor to accelerate the flywheel. When needed, the captured energy stored in the flywheel is converted back into a form of pressure energy to directly drive the boom cylinder up without throttling the main valve. In the lifting process, a compound circuit that consists of a throttling control circuit and a displacement control circuit is presented. A control strategy is proposed to optimize the energy recovery and reuse procedure. A 4-t HE is used as a study case to investigate the energy-saving potential of the proposed system. Numeric simulations show that the proposed system, when compared with a conventional load sensing system, can reduce as much as 48.9% energy consumption in a non-loaded cycle of boom lifting and lowering process. As to a fully loaded case, the energy-saving rate is 16.9%. This research indicates the flywheel-based scheme is promising for developing an energy-efficient fluid power system for HEs and reducing energy consumptions.

Achieving nanostructures with high surface area is one of the most challenging tasks as this metric usually plays a key role in technological applications, such as energy storage, gas sensing or photocatalysis, fields in which NiO is gaining increasing attention recently. Furthermore, the advent of modern NiO-based devices can take advantage of a deeper knowledge of the doping process in NiO, and the fabrication of p-n heterojunctions. By controlling experimental conditions such as dopant concentration, reaction time, temperature or pH, NiO morphology and doping mechanisms can be modulated. In this work, undoped and Sn doped nanoparticles and NiO/SnO2 nanostructures with high surface areas were obtained as a result of Sn incorporation. We demonstrate that Sn incorporation leads to the formation of nanosticks morphology, not previously observed for undoped NiO, promoting p-n heterostructures. Consequently, a surface area value around 340 m2/g was obtained for NiO nanoparticles with 4.7 at.% of Sn, which is nearly nine times higher than that of undoped NiO. The presence of Sn with different oxidation states and variable Ni3+/Ni2+ ratio as a function of the Sn content were also verified by XPS, suggesting a combination of two charge compensation mechanisms (electronic and ionic) for the substitution of Ni2+ by Sn4+. These results make Sn doped NiO nanostructures a potential candidate for a high number of technological applications, in which implementations can be achieved in the form of NiO–SnO2 p-n heterostructures.

Metamagnetic shape memory alloys exhibit a series of intriguing multifunctional properties and have great potential for applications in magnetic actuation, sensing and magnetic refrigeration. However, the poor mechanical properties of these alloys with hardly any tensile deformability seriously limit their practical application. In the present work, we developed a Ni-Fe-Mn-In microwire that exhibits both a giant, tensile superelasticity and a magnetic-field-induced first-order phase transformation. The recoverable strain of superelasticity is more than 20% in the temperature range of 233–283 K, which is the highest recoverable strain reported heretofore in Ni-Mn-based shape memory alloys (SMAs). Moreover, the present microwire exhibits a large shape memory effect with a recoverable strain of up to 13.9% under the constant tensile stress of 225 MPa. As a result of the magnetic-field-induced first-order phase transformation, a large reversible magnetocaloric effect with an isothermal entropy change ΔSm of 15.1 J kg−1 K−1 for a field change from 0.2 T to 5 T was achieved in this microwire. The realization of both magnetic-field and tensile-stress-induced transformations confers on this microwire great potential for application in miniature multi-functional devices and provides an opportunity for multi-functional property optimization under coupled multiple fields.

The effect of different particle sizes and processing techniques on the nutrients and physicochemical properties of millet flour was investigated. The processed samples, polished millet flour (POF) and germinated brown millet flour (GEF), and brown millet flour (BRF) were sieved to obtain 6 particle sizes, then determined the content of reducing sugar, starch, amino acid, protein and the parameters (color, viscosity, gelatinization temperature, crystallinity and microstructure) of millet flour. The results showed that smaller particle size could have more reducing sugar and amino acid, lower viscosity, less redness and yellowness, more lightness and higher gelatinization temperature. Germination increased reducing sugar content but consumed starch. Besides that, germination also improved amino acids and protein content, and changed proportion of amino acids. Gelatinization temperature and thermal stability increased but viscosity decreased in different particle size after germination. Grinding processing improved the viscosity and enthalpy, increased the proportion of starch in the whole grain, enhance the brightness, yellowness and redness but the thermal stability decreased in different particle size. The change of nutrients content affected the physicochemical parameters. Correlation analysis showed that the viscosity, flour color and starch gel temperature of BRF and GEF were mainly affected by starch, while that of POF were mainly affected by amino acids. Research on processing technology and particle size of foxtail millet was guidance for selection of raw materials food processing and food formula.

The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.

The interpretation of well-testing data is a key means of decision-making support for oil and gas field development. However, conventional processing methods have many problems, such as the stochastic nature of the data, feature redundancies, the randomness of the initial weights or thresholds, and fluctuations in the generalization ability with slight changes in the network parameters. These result in a poor ability to characterize data features and a low generalization ability of the interpretation models. We propose a new integrated well-testing interpretation model based on a multi-feature extraction method and deep mutual information classifiers (MFE-DMIC). This model can avoid the low model classification accuracy caused by the simple training structures, lacking of redundancy elimination, and the non-optimal classifier configuration parameters. First, we obtained the initial features according to four classical feature extraction methods. Then, we eliminated feature redundancies using a deep belief network and united the maximum information coefficient method to achieve feature purification. Finally, we calculated the interpretation results using a hybrid particle swarm optimization–support vector machine classification system. We used 572 well-testing field samples, including five working stages, for model training and testing. The results show that the MFE-DMIC model had the highest total stage classification accuracy of 98.18% as well as the least of features (nine) compared with the classical feature extraction and classification methods and their combinations. The proposed model can reduce the efforts of oil analysts and allow accurate labeling of samples to be predicted.

Functional and nutritional compounds are increased during foxtail millet germination while bad smell is produced due to the fatty acid oxidation. To eliminate the unpleasant aroma, the origins of the volatiles must be known. A comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry showed forty-nine volatiles containing 8 ketones, 10 aldehydes, 20 alkanes, 4 alcohols, 5 alkenes, and 2 furans were tentatively identified, and they increased during the germination of the foxtail millet. To identify the origin of some volatiles, model experiments by adding 6 fatty acids to the crude enzymes of the foxtail millet was designed, and 17 volatiles could be detected. The saturated fatty acids (palmitic acid and stearic acid) had no contributions to the formation of the volatiles, whereas the unsaturated fatty acid played important roles in the formation of volatiles. Among the unsaturated fatty acids, palmitoleic acid and linoleic acid produced most aldehydes, alcohols, and ketones, while linolenic acid produced the most alkanes and alkenes. This study will be helpful for controlling the smell of germinated seeds from the raw material selection.

During petroleum exploration and exploitation, the oil well-testing data collected by pressure gauges are used for monitoring the well condition and recording the reservoir performance. However, due to the large number of the collected data, the classification of this large volume of data requires a previous processing for the removal of noise and outliers. It is impractical to partition and process these data manually. Vibration-based features reflect geological properties and offer a promising option to fulfil such requirements. Based on the 75 on-site measured samples, the time-frequency-domain features are extracted and the classification performance of three classical classifiers are investigated. Then the downhole data processing and classification method is present by analysing the cross interaction of different types of data features and different classification mechanism. Several feature combinations are tested to establish a processing flow that can efficiently remove the noise and preserve the shape of curves, high signal to noise ratio rates, with minimum absolute errors. The results show that optimal multi-feature combination can achieve the highest working stage identification rate of 72%, the parameters optimized support vector machine can achieve the better classification performance than other listed classifiers. This paper provides a theoretical study for the data denoising and processing to enhance the working stage classification accuracy.

Quantum cryptography communication，which provided unconditional security by means of quantum key distribution and one-time pad encryption，has become one focus of communication academia and industry. The quantum key distribution protocol，devices，and network architecture were reviewed. The security loopholes and quantum hacking schemes of practical system were analyzed. Corresponding security countermeasures were summarized. Recent progress of measurement-device-independent quantum key distribution was also introduced. In the end，the security challenge，development direction and prospects of quantum cryptography communication were also discussed.

Cloud base height (CBH) is an important cloud macro parameter that plays a key role in global radiation balance and aviation flight. Building on a previous algorithm, CBH is estimated by combining measurements from CloudSat/CALIPSO and MODIS based on the International Satellite Cloud Climatology Project (ISCCP) cloud-type classification and a weighted distance algorithm. Additional constraints on cloud water path (CWP) and cloud top height (CTH) are introduced. The combined algorithm takes advantage of active and passive remote sensing to effectively estimate CBH in a wide-swath imagery where the cloud vertical structure details are known only along the curtain slice of the nonscanning active sensors. Comparisons between the estimated and observed CBHs show high correlation. The coefficient of association (R2) is 0.8602 with separation distance between donor and recipient points in the range of 0 to 100 km and falls off to 0.5856 when the separation distance increases to the range of 401 to 600 km. Also, differences are mainly within 1 km when separation distance ranges from 0 km to 600 km. The CBH estimation method was applied to the 3D cloud structure of Tropical Cyclone Bill, and the method is further assessed by comparing CTH estimated by the algorithm with the MODIS CTH product.

To ensure successful hosting of the 2022 Olympic Winter Games, a comprehensive understanding of the wind field characteristics in the Chongli Mountain region is essential. The purpose of this research was to accurately simulate the microscale wind in the Chongli Mountain region. Coupling the Weather Research and Forecasting (WRF) model with a computational fluid dynamics (CFD) model is a method for simulating the microscale wind field over complex terrain. The performance of the WRF-CFD model in the Chongli Mountain region was enhanced from two aspects. First, as WRF offers multiple physical schemes, a sensitivity analysis was performed to evaluate which scheme provided the best boundary condition for CFD. Second, to solve the problem of terrain differences between the WRF and CFD models, an improved method capable of coupling these two models is proposed. The results show that these improvements can enhance the performance of the WRF-CFD model and produce a more accurate microscale simulation of the wind field in the Chongli Mountain region.

Understanding the details of micro-scale wind fields is important in the development of wind energy. Research has proven that coupling Numerical Weather Prediction (NWP) and Computational Fluid Dynamics (CFD) models is a better approach for micro-scale wind field simulation. The main purpose of this work is to improve the NWP/CFD model performance in two parts: (i) developing a new coupling method that is more suitable for complex terrain between the NWP and CFD models, and (ii) applying a data assimilation system in the CFD model. Regarding part (i), in order to solve the problem of great topographical difference at the domain boundaries between the two models, Cressman interpolation is utilized to impose the NWP model wind on the CFD model boundaries. In part (ii), an assimilation method, nudging, to apply assimilation of observations into the CFD model is explored. Based on the Cressman interpolation coupling method, a preliminary implementation of data assimilation is performed. The results show that the NWP/CFD model with the improved coupling method may capture the details of micro-scale wind fields more accurately. Using data assimilation, the NWP/CFD model performance may be further improved by cooperating observation data.