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The noise of extended-range electric vehicles is an industry concern, with the range extender providing the main noise source. Suppressing torsional vibration is important for reducing the noise of the range extender. A new combined model of system dynamics, generator control, range extender control, and active vibration control is proposed. The working state and torsional vibration characteristics of the model were analyzed under typical working conditions for the start, idling, stable operation, and stop stages. Under steady-state working conditions, for example, idling and stable operation stages, the torsional vibration of the range extender is mainly related to the excitation torque fluctuation of the engine, and when switching the operation stage (e.g., from idling to stable operation), it is mainly related to sudden changes in the generator and engine torque. An active vibration control model—including active torque composition controllers for engine torque fluctuation and switching the operation mode—is established. The simulation showed that active control effectively improved the torsional vibration and provided a new concept for controlling torsional vibration. [ABSTRACT FROM AUTHOR]

Millireactors are widely used in flow chemistry due to their process intensification performance and sufficient product throughout. A novel structured tube‐in‐tube millireactor with industrial potential was designed, whose mixing and heat transfer were investigated by experiment and CFD. Partial least square regression (PLS) was introduced in the flow chemistry study and used to determine that the key parameters of the millireactor were jet Reynolds (Reh) and hole diameter Dh. The introduction of twist tape in the annular space generates swirling flow, which effectively increases the heat transfer capacity (up to 16.5 times). This millireactor achieves excellent mixing (>99% of mixing intensity after 1 cm) and the heat transfer enhancement ratio up to 6.5. The local flow field and concentration field from CFD model agreed with those obtained from PIV and PLIF visualization experiments. [ABSTRACT FROM AUTHOR]

A novel tube-in-tube millireactor with multiholes jet and twist tapes was designed and investigated for its excellent mixing, low price, and industrialization prospects. The inner tube fluid jets into the annular space through six circular microholes with a diameter of 0.2 mm that are evenly drilled around the inner tube. This arrangement achieves good inlet dispersion. The annular reaction channel is modified by three types of twist tape, which facilitates fluid splitting, recombination, and swirling. A detailed computational study has been carried out on the millireactor to characterize flow using a verified and validated CFD model. Villermaux-Dushman reaction and impulse method residence time distribution were used to study both the micromixing and macromixing performance. The multiholes jet has excellent micro-mixing performance with a micro-mixing time of less than 1 ms, at Re > 350. The twist tapes effectively improve the macromixing so that a Pe > 100 is achieved in most of the range of Re 32 ~ 634. Local flow field visualization of the annular spaces with tapes is obtained by PIV - RIM, and the results align with the CFD model. Highlights: A novel tube-in-tube millireactor with multiholes jet and twist tapes was designed with excellent micromixing and narrow RTD. Micromixing time is less than 1ms, at Re > 350. Pe > 100 in most of the studied range, V = 6 ~ 240 ml/min. The local flow field was obtained by PIV-RIM under structure shading. The micromixing and differential pressure between inner and outer tube are determined by R e h o l e . [ABSTRACT FROM AUTHOR]

Background: Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders.Methods: The American College of Surgeons National Surgical Quality Improvement Program Participant Use File was used to train four machine learning models to predict the likelihood of red cell transfusion using surgery-specific and patient-specific variables. A baseline model using only procedure-specific information was created for comparison. The models were trained on surgical encounters that occurred at 722 hospitals in 2016 through 2018. The models were internally validated on surgical cases that occurred at 719 hospitals in 2019. Generalizability of the best-performing model was assessed by external validation on surgical cases occurring at a single institution in 2020.Results: Transfusion prevalence was 2.4% (73,313 of 3,049,617), 2.2% (23,205 of 1,076,441), and 6.7% (1,104 of 16,053) across the training, internal validation, and external validation cohorts, respectively. The gradient boosting machine outperformed the baseline model and was the best- performing model. At a fixed 96% sensitivity, this model had a positive predictive value of 0.06 and 0.21 and recommended type and screens for 36% and 30% of the patients in internal and external validation, respectively. By comparison, the baseline model at the same sensitivity had a positive predictive value of 0.04 and 0.144 and recommended type and screens for 57% and 45% of the patients in internal and external validation, respectively. The most important predictor variables were overall procedure-specific transfusion rate and preoperative hematocrit.Conclusions: A personalized transfusion risk prediction model was created using both surgery- and patient-specific variables to guide preoperative type and screen orders and showed better performance compared to the traditional procedure-centric approach.Editor’s Perspective: [ABSTRACT FROM AUTHOR]

• The overall transmission error (OTE) is directly applied to the planet fault differential diagnosis (DD) for the tooth flank spall and the gear fillet crack. • A novel reordered SVD approach guided by the sparse index L2/L1 norm is proposed for fault-dependent residual OTE extraction. • Fault components with different scales can be accurately extracted in residual OTE. • The proposed DD framework is robust in extremely low-speed or varying-speed conditions. Owing to recent studies on the relationship between the transmission error and the localized gear fault, TE-based differential diagnosis (DD) technology has emerged as a promising approach to identifying the tooth flank spall and the gear fillet crack which have similar signatures but quite different fault mechanisms and prognoses. However, due to complex structural factors, the fault modes can hardly be identified from the calculated overall transmission error (OTE). In light of this, a novel DD framework based on encoder-measured data is proposed in this work. Specifically, the OTE signal calculated from resampled data are filtered by flexible time synchronous average. A reordered singular values decomposition (RSVD) approach guided by the sparse index L2/L1 norm is then developed for fault-dependent residual OTE extraction. Experimental results validate that the faulty events can be accurately identified by the proposed framework without prior information under different speed conditions for planet gear DD. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) and its progressive form nonalcoholic steatohepatitis (NASH) comprise a spectrum of chronic liver diseases in the global population that can lead to end-stage liver disease and hepatocellular carcinoma (HCC). NAFLD is closely linked to the metabolic syndrome, and comorbidities such as type 2 diabetes, obesity and insulin resistance aggravate liver disease, while NAFLD promotes cardiovascular risk in affected patients. The pathomechanisms of NAFLD are multifaceted, combining hepatic factors including lipotoxicity, mechanisms of cell death and liver inflammation with extrahepatic factors including metabolic disturbance and dysbiosis. Nuclear receptors (NRs) are a family of ligand-controlled transcription factors that regulate glucose, fat and cholesterol homeostasis and modulate innate immune cell functions, including liver macrophages. In parallel with metabolic derangement in NAFLD, altered NR signaling is frequently observed and might be involved in the pathogenesis. Therapeutically, clinical data indicate that single drug targets thus far have been insufficient for reaching patient-relevant endpoints. Therefore, combinatorial treatment strategies with multiple drug targets or drugs with multiple mechanisms of actions could possibly bring advantages, by providing a more holistic therapeutic approach. In this context, peroxisome proliferator-activated receptors (PPARs) and other NRs are of great interest as they are involved in wide-ranging and multi-organ activities associated with NASH progression or regression. In this review, we summarize recent advances in understanding the pathogenesis of NAFLD, focusing on mechanisms of cell death, immunometabolism and the role of NRs. We outline novel therapeutic strategies and discuss remaining challenges. [ABSTRACT FROM AUTHOR]

With the advantages of discriminative correlation filter (DCF) in tracking accuracy and computational efficiency, the DCF-based methods have been widely used in the field of unmanned aerial vehicles (UAV) for target tracking. However, UAV tracking inevitably encounters various challenging scenarios, such as background clutter, similar target, partial/full occlusion, fast motion, etc. These challenges generally lead to multi-peak interferences in the response map that cause the target drift or even loss. To tackle this problem, a response-consistent and background-suppressed correlation filter is proposed for UAV tracking. First, a response-consistent module is developed, in which two response maps are generated by the filter and the features extracted from adjacent frames. Then, these two responses are kept to be consistent with the response from the previous frame. By utilizing the l2-norm constraint for consistency, this module not only can avoid sudden changes of the target response caused by background interferences but also enables the learned filter to preserve the discriminative ability of the previous filter. Second, a novel background-suppressed module is proposed, which makes the learned filter to be more aware of background information by using an attention mask matrix. With the introduction of this module into the DCF framework, the proposed method can further suppress the response interferences of distractors in the background. Finally, extensive comparative experiments have been conducted on three challenging UAV benchmarks, including UAV123@10fps, DTB70 and UAVDT. Experimental results have proved that our tracker has better tracking performance compared with 22 other state-of-the-art trackers. Moreover, our proposed tracker can run at ∼36 FPS on a single CPU for real-time UAV tracking. [ABSTRACT FROM AUTHOR]

With the high morbidity and mortality, hepatocellular carcinoma (HCC) represents a major yet growing burden for our global community. The relapse-prone nature and drug resistance of HCC are regarded as the consequence of varying intracellular processes and extracellular interplay, which actively participate in tumor microenvironment remodeling. Amongst them, cellular senescence is regarded as a fail-safe program, leading to double-sword effects of both cell growth inhibition and tissue repair promotion. Particularly, cellular senescence serves a pivotal role in the progression of chronic inflammatory liver diseases, ultimately leading to carcinogenesis. Given the current challenges in improving the clinical management and outcome of HCC, senescence may exert striking potential in affecting anti-cancer strategies. In recent years, an increasing number of studies have emerged to investigate senescence-associated hepatocarcinogenesis and its derived therapies. In this review, we intend to provide an up-to-date understanding of liver cell senescence and its impacts on treatment modalities of HCC. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) poses a significant clinical challenge, necessitating the integration of immunotherapeutic approaches. Palbociclib, a selective CDK4/6 inhibitor, has demonstrated promising efficacy in preclinical HCC models and is being evaluated as a novel therapeutic option in clinical trials. Additionally, CDK4/6 inhibition induces cellular senescence, potentially influencing the tumor microenvironment and immunogenicity of cancer cells. In this study, we conducted comprehensive bioinformatic analyses using diverse HCC transcriptome datasets, including bulk and single-cell RNA-sequencing data from public databases. We also utilized human and mouse HCC cells to investigate functional aspects. Primary T cells isolated from mouse blood were employed to assess T cell immunity against HCC cells. Results revealed that CD8+ T-cell infiltration correlates with improved outcomes in HCC patients with suppressed CDK4/6 expression. Moreover, CDK4/6 expression was associated with alterations in the immune landscape and immune checkpoint expression within the liver tumor microenvironment. Furthermore, we found that treatment with Palbociclib and Doxorubicin induces cellular senescence and a senescence-associated secretory phenotype in HCC cells. Notably, pretreatment with Palbociclib augmented T cell-mediated cytotoxicity against HCC cells, despite upregulation of PD-L1, surpassing the effects of Doxorubicin pretreatment. In conclusion, our study elucidates a novel mechanism by which CDK4/6 inhibition enhances T-cell-associated cancer elimination and proposes a potential therapeutic strategy to enhance T-cell immunotherapy on HCC. [ABSTRACT FROM AUTHOR]

C/SiC composite has been widely used as a high‐temperature material for engineering components due to its excellent thermal properties. Facing the rapid development and threat of high‐energy laser, study on the ablation resistance under laser irradiation is strongly required. In this work, a continuous high‐energy laser was applied to explore the laser ablation behavior and mechanism of C/SiC composite. From the results, C/SiC composite shows different morphologies when irradiated at various laser power densities for 500 and 700 W/cm2. We divided the ablation area into three regions; the central, transition, and edge regions, where the formation of SiO2, SiO, and the breakage of carbon fiber were observed. The generated highly reflective SiO2 layer reduces the absorption of laser energy, which is beneficial to lower the back‐surface temperature and reduce the damage of composite. In addition, we put forward the ablation physical models and ablation mechanisms irradiated at different power densities. The work provides a basis for the laser ablation resistance of C/SiC composites under different conditions. [ABSTRACT FROM AUTHOR]

By using artificial lightning of standardized Waveform A, this study explored the lightning strike protection (LSP) effect of zirconium carbide (ZrC) and copper (Cu) modified short-carbon-fiber-reinforced boron phenolic resin composites under a current peak of 200 kA. Based on the results of visual observation, micro-morphologies characterization, three-dimensional shapes and X-ray detection, we observed a remarkable LSP effect in this composite, which can be attributed to the high bending strength of the laminated structure, and the well thermal resistance of ZrC. Meanwhile, the addition of Cu powders with concentrations of 10% was demonstrated to enable efficiently withstand the lightning strike and prevent crack propagation since the electrically conductive path. Our findings proposed a promising LSP skin material for the aircraft, which will maintain structural stability under the impact of a lightning strike. [ABSTRACT FROM AUTHOR]

Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations. [ABSTRACT FROM AUTHOR]

Bariatric surgery (BS) is a dependable method for managing obesity and metabolic diseases, however, the regulatory processes of lipid metabolism are still not well elucidated. Differentially expressed genes (DEGs) were analysed through three transcriptomic datasets of GSE29409, GSE59034 and GSE72158 from the GEO database regarding subcutaneous adipose tissue (SAT) after BS, and 37 DEGs were identified. The weighted gene co-expression network analysis (WGCNA), last absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms further screened four key genes involved in the regulation of STMN2, SFRP4, APOE and MXRA5. The GSE53376 dataset was used to further confirm the differential expression of SFRP4, APOE and MXRA5 in the postoperative period. GSEA analysis reveals activation of immune-related regulatory pathways after surgery. Finally, the silencing of MXRA5 was found by experimental methods to affect the expression of PPARγ and CEBPα during the differentiation of preadipocytes, as well as to affect the formation of lipid droplets. In conclusion, SAT immunoregulation was mobilized after BS, while MXRA5 was involved in the regulation of lipid metabolism. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced carbon matrix composites (C/C) will be easily oxidized in high temperatures, which will have a great negative effect on their performance. Preparing ultra-high-temperature ceramic (UHTC) coatings is a well-established method to improve the oxidation and ablation resistance of C/C. However, it is time-consuming and costly to obtain these coatings through the traditional experimental method. Motivated by the outstanding performance of machine learning (ML) algorithms in many fields, this study adopts ML algorithms based on historical experimental datasets to build a model. This model will predict the oxidation and ablation resistance, represented by mass ablation rate. For this purpose, variables that affect the mass ablation rate and are easily accessible were used as input features. That includes the chemical composition and essential physics/chemistry properties of coatings and experimental parameters. Seven different ML algorithms were used to establish the model; namely, ridge regression (Ridge), lasso regression (Lasso), kernel ridge regression (KRR), support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and bagging regression (Bagging). The results show that RFR has the optimal generalization performance with a mean absolute error (MAE) of 0.55, mean-squared error (MSE) of 0.71 and coefficient of determination (R2) of 0.87 on the testing set. SHapley Additive exPlanations (SHAP) analysis of the RFR model explained how these input features affect the mass ablation rate and further provided the critical features for performance prediction. The model established in this study can predict coating performance accurately and accelerate the development of UHTC-coated C/C composites from a data-driven perspective. [ABSTRACT FROM AUTHOR]

[Display omitted] • IL11RA mRNA therapy demonstrated efficacy in controlling tumor growth. • CD8 + T cell expansion was observed in response to IL11RA mRNA treatment. • Transcriptome analysis revealed insights into IL11RA mRNA therapy mechanisms. • Potential for enhancing overall survival with IL11RA mRNA immunotherapy. Current methods for delivering genes to target tumors face significant challenges, including off-target effects and immune responses against delivery vectors. In this study, we developed a novel approach using messenger RNA (mRNA) to encode IL11RA for local immunotherapy, aiming to harness the immune system to combat tumors. Our research uncovered a compelling correlation between IL11RA expression and CD8 + T cell levels across multiple tumor types, with elevated IL11RA expression correlating with improved overall survival. Examination of the Pan-Cancer Atlas dataset showed a significant reduction in IL11RA expression in various cancer types compared to normal tissue, raising questions about its potential role in tumorigenesis. To achieve efficient in vivo expression of IL11RA, we synthesized two mRNA sequences mimicking the wild-type protein. These mRNA sequences were formulated and capped to ensure effective delivery, resulting in robust expression within tumor sites. Our investigation into IL11RA mRNA therapy demonstrated its effectiveness in controlling tumor growth when administered both intratumorally and intravenously in mouse models. Additionally, IL11RA mRNA treatment significantly stimulated the expansion of CD8 + T cells within tumors, draining lymph nodes, and the spleen. Transcriptome analysis revealed distinct transcriptional patterns associated with T cell functions. Using multiple deconvolution algorithms, we found substantial infiltration of CD8 + T cells following IL11RA mRNA treatment, highlighting its immunomodulatory effects within the tumor microenvironment. In conclusion, IL11RA mRNA therapy presents a promising strategy for tumor regression with potential immunomodulatory effects and clinical implications for improved survival outcomes. [ABSTRACT FROM AUTHOR]

• The GARCH family models from time series analysis is introduced for the first time into machine condition monitoring. • Health indicators constructed based on GARCH and EGARCH models demonstrate the capability to detect incipient faults in bearings, verified through two experimental cases. • In accelerated degradation tests, it is established that acoustic emission signals are closely associated with damage evolution. • Acoustic emission technology can be employed for quantitative assessment of bearing damage. Recent progress in digital twin (DT) has significantly contributed to the advancement of predictive maintenance. The interaction of data between physical and virtual models is facilitated through carefully designed health indicators (HIs). Conventional condition monitoring HIs are inadequate for early-stage fault detection and lack the capacity to quantitatively assess defects. In light of this, the paper proposes HIs based on the generalised autoregressive conditional heteroskedasticity (GARCH) family time series model to characterise the evolution of bearings dynamic response, specifically the cyclostationarity of the repetitive transients. The verification of the proposed indicators is assessed using a publicly available vibration dataset and acoustic emission signals acquired from accelerated bearing degradation tests. The result shows that the GARCH oriented indicators can determine the incipient failure earlier than traditional statistical HIs and have the ability to quantify defects. Significantly, the improved Exponential GARCH model-based indicator demonstrates heightened stability throughout the monitoring process. [ABSTRACT FROM AUTHOR]

[Display omitted] • A monolithic reactor with multi-jet-channel in the circumferential array is constructed. • Two jets unaligned-impinging induces unsteady flow with local eddies and interface deformation. • The degree of two-phase dispersion is higher in the helical channel with a smaller pitch. • The throughput increases by over tenfold with the condition of a higher K L a value. A monolithic reactor with multi-jet-channel in the circumferential array (MR-MJCCA) is designed to intensify mass transfer. Effects of operating conditions and geometric parameters on the mass transfer efficiency E and overall volumetric mass transfer coefficient K L a are illustrated through experimental and numerical methods. Results show that the increased jet impingement intensity due to a higher flow rate and smaller jet diameter D p effectively enhances mass transfer. E and K L a in the MR-MJCCA possessing eight channels increase with reducing the channel depth D e and helical pitch p , yet first rise and then decline with enlarging the jet unaligned-impinging distance w. A higher two-phase interfacial area and energy dissipation rate are conducive to improving mass transfer performance in configurations with D p = 0.3 mm, D e = 1 mm, p = 5 mm, and w = 1.5 mm. Increasing the axial length of reactor elevates E whereas K L a diminishes. A reliable artificial neural network model was used to correlate K L a with key parameters. MR-MJCCA exhibits a higher K L a compared to most microreactors and the throughput increases by over tenfold, demonstrating its scalability and broader industrial applications. [ABSTRACT FROM AUTHOR]

The acoustic emission (AE) technique is known for its sensitivity to early damage and is well-suited for online condition monitoring of rolling element bearings (REBs) in various industrial application scenarios. Nonetheless, identifying weak faults under varying speed and strong background noise conditions still remains challenging. In addition, the comprehensive modelling of AE signal from faulty REB in electromechanical systems is still a pending issue. In light of this, a well-considered model is firstly developed for the AE signal of faulty REBs in this work. After that, a novel bearing fault diagnosis framework based on semantic segmentation networks is devised. Precisely, the proposed framework consists of three main components: a preprocessing step depending on the signal segmentation algorithm, a diagnosis step using fault fingerprint mapping, and a postprocessing evaluation step supplemented by a density peak clustering (DPC) approach. We evaluate the presented procedures through simulation analysis and an experimental case under varying speed conditions. Meanwhile, the comparison with the original threshold-based fault fingerprint recognition algorithm is also conducted. The comprehensive results demonstrate the efficacy of identifying fault-associated fingerprint feature (FPF), indicating that the proposed framework holds promise for condition monitoring. [ABSTRACT FROM AUTHOR]

[Display omitted] • Evaluate the effect of ultrasound combined with low temperature pretreatment on wine. • Pretreatment could increase the contents of anthocyanins and phenols. • Pretreatment affected the color of wine. • Pretreatment significantly improve its antioxidant capacity. • Pretreatment could affect the volatile components and improve the aftertaste. Ultrasound combined with low temperature treatment is a new food processing technology. In this study, low temperature, three ultrasound power levels, and their combinations were adopted in the must before fermentation to study their effects on Merlot red wine. The results showed that ultrasound combined with low temperature pretreatment increased the total and monomer contents of anthocyanins and phenols, affected the color of the wine, and significantly increased its antioxidant capacity. In particular, 240 W of ultrasound combined with low temperature pretreatment reduced the bad odors (caprylic acid, benzaldehyde, and 1-ethanol content) and improved the flower and fruit aroma (1-octanol and phenethyl acetate), as well as the aftertaste, thus improving the quality of the wine. Ultrasound combined with low temperature pretreatment positively affected the quality of Merlot red wine. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ablation mechanism of metamaterials under high-energy continuous-wave laser irradiation was for the first time revealed. • The microwave absorption performance of metamaterials under extreme laser damage conditions was firstly deeply investigated. Metamaterials, typically consisting of sub-wavelength metal-atom arrays and a dielectric layer matrix such as glass fiber reinforced epoxy resin (FR-4), have been widely used in the field of stealth equipment due to their good electromagnetic parameter designability and strong absorption for electromagnetic waves at specific frequencies. In offensive and defensive confrontations, laser weapons pose a serious threat to the survival of stealth equipment. However, the interaction mechanism between the metamaterials and laser and the effect of laser ablation on microwave absorption performance are still unclear. In this work, we firstly irradiated the double arc-shaped Cu-atom array/FR-4 matrix metamaterials by using a high-energy continuous-wave laser and studied the interaction and response mechanism between them. The high temperature caused by laser irradiation leads to the melting of the metal atoms and the pyrolysis of the resin matrix. By simulating and testing the reflection loss of the metamaterials before and after irradiation, we found that the resin pyrolysis products strongly affect the impedance matching characteristics and reduce the microwave absorption properties significantly in extreme cases. Our findings help deeply understand the interaction between the metamaterials and the laser and will show guidance in the applications involving laser-metamaterial interactions. [ABSTRACT FROM AUTHOR]

This study investigated the wintertime vertical distributions and source areas of aerosols, NO 2 , and HCHO in a coastal city of Dongying from December 2020 to March 2021, using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) and a potential source contribution function (PSCF) model, respectively. Moreover, the chemical production sensitivity of O 3 at different height layers was analyzed using HCHO/NO 2 ratios. The results revealed that the wintertime averaged highest concentrations of aerosol (1.25 km−1), NO 2 (14.81 ppb), and HCHO (2.32 ppb) were mainly distributed at the surface layer, 100–200 m layer, and 200–300 m layer, respectively. Regarding the diurnal cycles, high concentrations of aerosol (>1.4 km−1) and NO 2 (>16.0 ppb) usually appeared in the early morning and late afternoon, while high concentrations of HCHO (>2.5 ppb) usually occurred during 12:00–15:00. The PSCF model revealed that the wintertime aerosol mainly originated from Shandong, northern Jiangsu, Korea, and the northwestern Mongolian Plateau. Below 200 m, NO 2 was mainly from western Shandong, whereas above 600 m, it was mainly from northern Shandong and the Beijing-Tianjin-Hebei (BTH) region. The corresponding sources for HCHO were central and southern Shandong (below 200 m) and northern Shandong, northern Jiangsu, and southeastern BTH (above 600 m). In addition, the chemical production sensitivity of O 3 below 100 m was observed only in the VOC-limited regime. The percentages of O 3 production under the NO x -limited, NO x -VOC-limited, and VOC-limited regimes were 10.75% (31.18%), 4.30% (19.35%), and 84.95% (49.47%) at the 500–600 m (900–1000 m) layer. This study has guiding significance for the coordinated control of PM 2.5 and O 3 , and can assist in the implementation of regional joint prevention and control strategies for air pollutants. • The averaged wintertime NO 2 and HCHO profiles all exhibited a Gaussian shape. • The vertical potential sources of aerosol, NO 2 and HCHO were analyzed using PSCF. • O 3 chemical production sensitivity under 100 m was only VOC-limited. • The percentages of VOC-limited O 3 production at 500–600 were 84.95%. [ABSTRACT FROM AUTHOR]

Since the COVID-19 outbreak in 2020, China's air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China's urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production. [ABSTRACT FROM AUTHOR]

• A review of blind deconvolution methods (BDMs) in machinery fault diagnosis. • History, principle, classic method and applications of BDMs are reviewed. • Modified BDMs are classified to review according to their basic principle and merits and limitations are verified. • Future prospects of BDMs in machinery fault detection is discussed. Fault diagnosis is of significance for ensuring the safe and reliable operation of machinery equipment. Due to the heavy noise and interference, it is difficult to detect the fault directly from the measured signal. Hence, signal processing techniques that can achieve feature extraction, signal denoising, and fault identification are the most common tools in the field. Blind deconvolution methods (BDMs), as one of the most classic methods, have been studied extensively and applied fully for machinery fault diagnosis. Up to now, plenty of publications about the studies and applications of BDMs for machinery fault diagnosis have been presented to academic journals, technical reports, and conference proceedings. This paper intends to survey and summarize the current progress of BDMs applied in machinery fault diagnosis, as well as provides a comprehensive review of BDMs from history to state-of-the-art methods and finally to research prospects. Firstly, the theoretical background and brief history of BDMs are introduced. Secondly, the modified BDMs are classified to review their basic principles. After that their merits and limitations as well as the performance analysis are summarized. Thirdly, the research and application on machinery fault detection using BDMs are overviewed. Finally, the prospects of BDMs in machinery fault diagnosis are discussed. [ABSTRACT FROM AUTHOR]

Air pollutant transport plays an important role in local air quality, but field observations of transport fluxes, especially their vertical distributions, are very limited. We characterized the vertical structures of transport fluxes in central Luoyang, Fen-Wei Plain, China, in winter based on observations of vertical air pollutant and wind profiles using multi-axis differential optical absorption spectroscopy (MAX-DOAS) and Doppler wind lidar, respectively. The northwest and the northeast are the two privileged wind directions. The wind direction and total transport scenarios were dominantly the northwest during clear days, turning to the northeast during the polluted days. Increased transport flux intensities of aerosol were found at altitudes below 400 m on heavily polluted days from the northeast to the southwest over the city. Considering pollution dependence on wind directions and speeds, surface-dominated northeast transport may contribute to local haze events. Northwest winds transporting clean air masses were dominant during clean periods and flux profiles characterized by high altitudes between 200 and 600 m in Luoyang. During the COVID-19 lockdown period in late January and February, clear reductions in transport flux were found for NO2 from the northeast and for HCHO from the northwest, while the corresponding main transport altitude remained unchanged. Our findings provide better understandings of regional transport characteristics, especially at different altitudes. [ABSTRACT FROM AUTHOR]