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• Develop an original FE-analytical model of bolt tightening with elastic of bearing cap. • Propose a new assembly accuracy prediction method of planetary gear train. • Consider gear-shaft-bearing-bolt coupling effects on assembly accuracy. • Explore bolt tightening effects on shaft error motion and assembly accuracy. • Carry out the assembly accuracy and vibration tests to verify the proposed method. Planetary gear train (PGT) is a complex transmission system composed of gears, shafts, bearings and bolted joints, of which the assembly accuracy affects the transmission accuracy and dynamic behavior. In this paper, a new assembly accuracy prediction method is proposed for PGT considering gear-shaft-bearing-bolt coupling effects. The new method is formulated by establishing three models for bolt assembly, shaft error motion and assembly accuracy (transmission error) of PGT. The model for bolt assembly is an original FE-analytical model to analyze the bolt assembly effect on elastic deformation of bearing cap, which is developed by finite element (FE) method and analytical method for addressing the elastic effect of bearing cap and elastic interaction between bolts. The models for shaft error motion and assembly accuracy are performed by the three-dimensional contact analysis with errors and preload of bearing cap based on analytical method and kinematic geometry relationship. Combining the three models, the assembly accuracy can be predicted by simulations. The simulations indicate that the proper bolt assembly parameters can alleviate the influence of manufacturing errors on assembly accuracy. The assembly accuracy tests of PGT are conducted. The results well agree with the simulations', and the assembly accuracy is improved by the proper bolt assembly parameters. [ABSTRACT FROM AUTHOR]

Apiospora species are widely distributed fungi with diverse lifestyles, primarily functioning as plant pathogens, as well as exhibiting saprophytic and endophytic behaviors. This study reports the discovery of three new species of Apiospora, namely A. gongcheniae, A. paragongcheniae, and A. neogongcheniae, isolated from healthy Poaceae plants in China. These novel species were identified through a multi-gene phylogenetic analysis. The phylogenetic analysis of the combined ITS , LSU , tef1 , and tub2 sequence data revealed that the three new species formed a robustly supported clade with A. garethjonesii, A. neogarethjonesii, A. setostroma, A. subrosea, A. mytilomorpha, and A. neobambusae. Detailed descriptions of the newly discovered species are provided and compared with closely related species to enhance our understanding of the genus Apiospora. [ABSTRACT FROM AUTHOR]

Detecting potential depression and identifying the critical predictors of depression among older adults with chronic diseases are essential for timely intervention and management of depression. Therefore, risk prediction models (RPMs) of depression in elderly people should be further explored. A total of 3959 respondents aged 60 years or over from the wave four survey of the China Health and Retired Longitudinal Study (CHARLS) were included in this study. We used five machine learning (ML) algorithms and three data balancing techniques to construct RPMs of depression and calculated feature importance scores to determine which features are essential to depression. The prevalence of depression was 19.2 % among older Chinese adults with chronic diseases in the wave four survey. The random forest (RF) model was more accurate than the other models after balancing the data using the Synthetic Minority Oversampling Technique (SMOTE) algorithm, with an area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) of 0.957 and 0.920, respectively, a balanced accuracy of 0.891 and a sensitivity of 0.875. Furthermore, we further identified several important predictors between male and female patients via constructed sex-stratified models. Further research on the clinical impact studies of our models and external validation are needed. After several techniques were used to address class imbalance issues, most RPMs achieved satisfactory accuracy in predicting depression among elderly people with chronic diseases. RPMs may thus become valuable screening tools for both older individuals and healthcare practitioners to assess the risk of depression. • This study aimed to develop models to predict depression and explore the risk factors for depression among different research objects using machine learning methods. • The best-performing models in different populations generated approximately the same set of features (life satisfaction, self-reported health status, sleep duration, vision, etc.). • The prevalence of depression was 19.2 % among older adults with chronic diseases in China. Besides, the depression incidence rate in women was twice as high as that in men (68.4 % vs. 31.6 %). [ABSTRACT FROM AUTHOR]

The methane adsorption capacity is crucial for evaluating gas-in-place resources and the gas production potential in shale gas reservoirs. There are many reports concerning the interfacial interaction between methane fluid and rock using various thermodynamic models; however, little research has been performed to reveal how methane is adsorbed into nanopores with different scales. In this study, we did methane adsorption experiments on nine Longmaxi Formation shale kerogen. Then, molecular simulation and an improved Ono–Kondo model were used to analyze the methane adsorption behaviors. Results show that methane is preferentially adsorbed in sulfur-containing sites by surface adsorption and pore-filling adsorption, and methane adsorbed in the form of pore-filling contributes dominantly to the total methane adsorption amount. Surface adsorption capacity nS increases with increasing mesopore volume, while pore-filling adsorption capacity nF is affected by both micropore (<2 nm) development and the micropore accessibility of methane. On the one hand, nF increases logarithmically with increasing micropore volume. On the other hand, the mean interplanar distance of the aromatic layers d002 is the key parameter in determining nF because the micropores within the aromatic layer with d002 less than 0.38 nm will be inaccessible for methane. This study is essential for understanding the methane adsorption mechanism and lay the foundation for future investigation of fluids–rock interactions. [ABSTRACT FROM AUTHOR]

In this paper, a differential quartz-enhanced photoacoustic spectroscopy (D-QEPAS) sensor is reported. The differential photoacoustic cell (PAC) was used to generate the photoacoustic effect. Two quartz tuning forks (QTFs) with a quality factor (Q) up to 10 000 were used as the acoustic wave transducers. The signal of D-QEPAS sensor was doubly enhanced by the differential characteristic of differential PAC and resonant response of QTF. The background noise was suppressed based on the differential principle. With the help of the finite element method, the acoustic field characteristics were simulated and calculated. Wavelength modulation spectroscopy technique and second harmonic (2f) detection technique were applied to detect photoacoustic signal. Trace acetylene (C2H2) gas detection was performed to verify the D-QEPAS sensor performance. The 2f signal amplitude of differential mode was 116.03 μV, which had a 1.65 times improvement compared with the 2f signal amplitudes of QTF1. When the integration time was 334 s, the minimum detection limit of D-QEPAS sensor was about 496.7 ppb. The reported D-QEPAS provides a development and idea for the widely reported QEPAS technique. [ABSTRACT FROM AUTHOR]

To solve the problem of data association in multiple target tracking in a densely cluttered environment, a multiple target data association algorithm based on Takagi–Sugeno (T-S) intuitionistic fuzzy model is proposed. In the proposed algorithm, the new multiple target T-S intuitionistic fuzzy model (MTTS-IFM) is constructed by incorporating intuitionistic fuzzy sets and intuitionistic fuzzy numbers. To identify the premise parameters for MTTS-IFM, a new intuitionistic index is defined and a modified form of the intuitionistic fuzzy C-means clustering algorithm is proposed. Furthermore, to solve the multiple target data association for the MTTS-IFM approach, the input variables of MTTS-IFM are defined using the maximum intuitionistic fuzzy entropy, and a data association approach is proposed to deal with the uncertainty between targets and measurements. Finally, simulation results show that the performance of the proposed algorithm is better than that of algorithms designed for multiple target tracking in either a clutter-free or cluttered environment. Moreover, the real-time performance was clearly better than that of the joint probabilistic data association filter (JPDAF) and slightly better than that of maximum entropy fuzzy (MEF-JPDAF), Fitzgerald-JPDAF, and a T-S intuitionistic fuzzy model (TS-FM). [ABSTRACT FROM AUTHOR]

This paper presents a novel 3-D-printed circularly polarized (CP) open-ended waveguide (OEW) antenna array. It aims to provide a lightweight scheme for OEW array design and concurrently address the high fabrication complexity problem of conventional OEW arrays. To realize these, the proposed design adopts dielectric 3-D printing technology to build the main body of the array and employs electroplating to form the waveguide walls. To attain CP radiation, an in-built antipodal exponential groove polarizer is proposed and applied to a square waveguide. With the perturbation of the grooves, degenerate modes T E 10 and T E 01 can be excited simultaneously, and their differential phase can be 90 degrees, resulting in CP radiation. Moreover, a novel matching cube based on effective medium theory (EMT) is proposed for the antenna to mitigate the reflections at the waveguide aperture and therefore realize good impedance matching. To validate the design idea, prototypes for a single element and a 2 × 2 array were fabricated and measured. Experimental results show that their weights are 15 and 76 grams, and their axial ratio bandwidths (ARBWs) are 16% (5.45-6.4 GHz) and 11.1% (5.52-6.17 GHz), respectively. [ABSTRACT FROM AUTHOR]

Reverse water gas shift (RWGS) reaction is an attractive approach to convert CO2 with renewable H2 to produce CO. However, this reaction is always accompanied by undesirable methanation reaction lowering the CO selectivity, particularly, at low reaction temperature and on Ni catalysts. Herein, a strategy of deposition of inert Ag on the surface of Ni to block methanation reaction during RWGS for selective conversion of CO2 toward CO was reported. Characterizations showed that in contrast to formation of bulk Ni−Ag alloy, both Ag particle and highly dispersed Ag are direct contacting with Ni particle. Surface Ag modifies the property of Ni geometrically and electronically, resulting in reduced accessible surface Ni sites and Ni ensemble size, as well as electron transfer from Ni to Ag. Consequently, both the amount and strength of CO adsorption are reduced, which facilitates CO desorption and reduces CO methanation activity. The Ag modification tunes Ni/SiO2 from selective methanation to selective RWGS, with 100 % CO selectivity on Ni‐0.3 Ag/SiO2 (Ag/Ni=0.3) at 400 °C with gas hourly space velocity (GHSV) >300 L g−1 h−1 and CO selectivity is >80.7 % even at a low GHSV of 15 L g−1 h−1. Moreover, the Ni‐0.3 Ag/SiO2 appears to be very stable for RWGS reaction. [ABSTRACT FROM AUTHOR]

Introduction: Infantile epileptic spasms syndrome (IESS) is an age-specific and severe epileptic encephalopathy. Although adrenocorticotropic hormone (ACTH) is currently considered the preferred first-line treatment, it is not always effective and may cause side effects. Therefore, seeking a reliable biomarker to predict the treatment response could benefit clinicians in modifying treatment options. Methods: In this study, the complexities of electroencephalogram (EEG) recordings from 15 control subjects and 40 patients with IESS before and after ACTH therapy were retrospectively reviewed using multiscale entropy (MSE). These 40 patients were divided into responders and nonresponders according to their responses to ACTH. Results: The EEG complexities of the patients with IESS were significantly lower than those of the healthy controls. A favorable response to treatment showed increasing complexity in the γ band but exhibited a reduction in the β/α-frequency band, and again significantly elevated in the δ band, wherein the latter was prominent in the parieto-occipital regions in particular. Greater reduction in complexity was significantly linked with poorer prognosis in general. Occipital EEG complexities in the γ band revealed optimized performance in recognizing response to the treatment, corresponding to the area under the receiver operating characteristic curves as 0.8621, while complexities of the δ band served as a fair predictor of unfavorable outcomes globally. Conclusion: We suggest that optimizing frequency-specific complexities over critical brain regions may be a promising strategy to facilitate predicting treatment response in IESS. [ABSTRACT FROM AUTHOR]

In the microgravity environment, liquid is usually adsorbed in corners or slots. Liquid accumulated in these regions is difficult to be removed and used. A mathematical model describing static profiles of the liquid accumulated in the corner of truncated-cone-shaped containers under microgravity is obtained through theoretical derivation in this paper. The profiles have two cases according to liquid's wettability and the angle of containers' corners. Once coordinates of an endpoint of the profile are known, the profile and volume of the liquid can be obtained using the Shooting method according to the mathematical model. Besides, if abscissa values of the endpoints are unknown, the profile of the liquid can also be obtained using the dichotomy method and shooting method when liquid volume is given. It is easier to use these methods than others proposed before and they can also be used to predict liquid's profiles in other conditions. Numerical simulation is performed with the volume of fluid method and the results are in good agreement with theoretical ones. Based on the mathematical model, the profile and volume of the liquid accumulated in the corner can be predicted accurately. [ABSTRACT FROM AUTHOR]

Studies on the impacts of fertilization on plant production and species diversity are crucial for better maintaining the stability of grassland ecosystems and restoring degraded grasslands. Using a controlled fertilization experimental platform in a temperate Leymus chinensis meadow steppe ecosystem, the effects of different levels of nitrogen (N) and phosphorus (P) addition on plant community structure, biomass allocation, diversity, and the correlation relationship were explored. The major results were as follows: (1) The structural composition of the plant community changed after different levels of N and P addition; the dominance ratio and biomass of Poaceae plants increased gradually with increasing N and P addition levels. (2) The addition of N and P increased the height, density and coverage of the plant community, the biomass of the dominant L. chinensis and plant community and the total productivity of grassland, and reduced the root–shoot ratio of grassland biomass. For example, plant community biomass, gramineous plant biomass and grassland total productivity increased by 84.46–204.08%, 162.64–424.20%, and 38.12–46.44%, respectively, after N and P addition. (3) The community richness, diversity, and evenness indices decreased overall and showed binomial regression after N and P addition; the functional group of Poaceae plants was highly significantly negatively correlated with species diversity indices and was highly significantly positively correlated with the aboveground biomass of L. chinensis and community; Leguminosae plants and Ranunculaceae plants were highly significantly positively correlated with Margalef and Patrick richness indices; Ranunculaceae plants were highly significantly and negatively correlated with L. chinensis biomass, community biomass, and Poaceae plants. Moderate fertilization not only improved the plant community structure and productivity but was also beneficial for maintaining the grassland species diversity and stability. [ABSTRACT FROM AUTHOR]

Studies on the impacts of grazing on carbon, nitrogen, and phosphorus stoichiometry and storage are crucial for better understanding the nutrient cycles of grasslands ecosystems. Using a controlled grazing experimental platform in a meadow steppe ecosystem, the effects of different stocking rates (0.00, 0.23, 0.34, 0.46, 0.69, and 0.92 AU ha−1) on the carbon, nitrogen, and phosphorus contents of plant functional groups were explored. The major results were: (1) The carbon content of Gramineae Barnhart was significantly reduced by grazing intensity (p < 0.05), and the organic carbon content of Cyperaceae Rotundus was significantly higher than that of the other groups; the total nitrogen content of Cyperaceae and other groups and total phosphorus contents of Gramineae, Leguminosae Sp., Cyperaceae, and other groups all increased significantly with increasing grazing intensity (p < 0.05). (2) The carbon, nitrogen, and phosphorus storage amounts of Gramineae, Leguminosae, and Ranunculaceae L. decreased significantly with increasing grazing intensity. Heavy grazing reduced the carbon, nitrogen, and phosphorus storage amounts of Cyperaceae and other groups, while the carbon, nitrogen, and phosphorus storage amounts of Compositae were the largest under moderate grazing. (3) The nitrogen content of each functional group was highly significantly negatively correlated with the C/N ratio, and the phosphorus content was highly significantly negatively correlated with the C/P ratio. Grazing and foraging affected the growth of the different functional groups, which in turn affected their carbon, nitrogen, and phosphorus content, stoichiometry, and storage. Moderate grazing improved the nutrient utilization efficiency of grassland and is beneficial for promoting sustainable grassland development. [ABSTRACT FROM AUTHOR]

Summary: Morphological novelties, including formation of trait combinations, may result from de novo gene origination and/or co‐option of existing genes into other developmental contexts. A variety of shape–color combinations of capitular florets occur in Chrysanthemum and its allies. We hypothesized that co‐option of a carotenoid cleavage dioxygenase gene into the floral symmetry gene network would generate a white zygomorphic ray floret.We tested this hypothesis in an evolutionary context using species in Chrysanthemum sensu lato, a monophyletic group with diverse floral shape–color combinations, based on morphological investigation, interspecific crossing, molecular interaction and transgenic experiments.Our results showed that white color was significantly associated with floret zygomorphy. Specific expression of the carotenoid cleavage dioxygenase gene CCD4a in marginal florets resulted in white color. Crossing experiments between Chrysanthemum lavandulifolium and Ajania pacifica indicated that expression of CCD4a is trans‐regulated. The floral symmetry regulator CYC2g can activate expression of CCD4a with a dependence on TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING (TCP) binding element 8 on the CCD4a promoter.Based on all experimental findings, we propose that gene co‐option of carotenoid degradation into floral symmetry regulation, and the subsequent dysfunction or loss of either CYC2g or CCD4a, may have led to evolution of capitular shape–color patterning in Chrysanthemum sensu lato. See also the Commentary on this article by Zhang, 236: 800–802. [ABSTRACT FROM AUTHOR]

A urea-utilizing bacterium, designated Q2-2 T, was isolated from landfill. Cells of strain Q2-2 T were Gram stain-negative, aerobic, short-rod bacteria. Strain Q2-2 T was observed to grow at a temperature range of 15–37℃ (optimum 30 ℃), a pH range of 5.5–9.5 (optimum pH 8.0) and 0–4% (w/v) NaCl (optimum 1%). The major respiratory quinone was Q-8, and the major polar lipids were diphosphatidyl glycerol, phosphatidylethanolamine, phosphatidylmethylethanolamine, and phosphatidyl glycerol. Based on the 16S rRNA gene sequence, strain Q2-2 T had the highest similarity with Paracandidimonas caeni 24 T (98.0%), followed by Pusillimonas soli MJ07T (97.5%), Parapusillimonas granuli Ch07T (97.2%), Pusillimonas ginsengisoli DCY25T (97.1%) and Paracandidimonas soli IMT-305 T (96.4%). The ANI values between strain Q2-2 T and the above related type strains were 71.02%, 73.52%, 74.32%, 74.59% and 72.29%, respectively. The DNA G + C content of strain Q2-2 T was 61.1%. Therefore, strain Q2-2 T represents a novel species of the genus Paracandidimonas, for which the name Paracandidimonas lactea sp. nov. (type strain Q2-2 T = CGMCC 1.19179 T = JCM 34906 T) is proposed. [ABSTRACT FROM AUTHOR]

Wind energy, as clean energy, has attracted more and more attention. Wind power generation is easily threatened by the irregular fluctuation of wind speed, which interferes with the safety and stability of power system. In this study, a wind speed interval prediction method based on variational mode decomposition (VMD), phase space reconstruction (PSR), whale optimization algorithm (WOA), quantile regression (QR) and gated recurrent unit (GRU) is proposed. Firstly, the wind speed time series is decomposed into a variety of intrinsic mode functions (IMFs) through VMD to reduce the stochasticity. Secondly, all IMFs are then reconstructed using PSR to get the optimal input variables of the model. Then, the QRGRU model is optimized by the improved WOA to get the optimal QRGRU model parameters. Then, the wind speed interval prediction model of PSR-IWOA-QRGRU is established for each intrinsic mode function. Finally, the prediction results of each component are superimposed to realize the wind speed interval prediction. By checking on three different data sets, the effectiveness of the proposed method in wind speed interval prediction is proved. [Display omitted] [ABSTRACT FROM AUTHOR]

• Revealing the potential regulatory mechanisms of structural variation in psychiatric disease risk loci. • The risk gene Borcs7 is associated with schizophrenia-like behaviors in mice model. • The structrual variations explain association signals of multiple GWAS loci. Multiple types of variations have been postulated to confer risk of schizophrenia and bipolar disorder, but majority of present GWAS solely focused on SNPs or small indels, and the impacts of structural variations (SVs) remain less understood. Nevertheless, accumulating evidence suggest that SVs may explain the association signals in certain GWAS hits. Here, we conducted pairwise linkage disequilibrium (LD) analyses of SNPs and SVs in populations from 1000 Genomes Project. Among the 299 psychiatric GWAS loci, 1213 SVs showed an LD of r 2 > 0.1 with GWAS risk SNPs, and 66 of them were in moderate to strong LD (r 2 > 0.6) with at least one GWAS risk SNP. Nine SVs were subject to further explorative analyses, including eQTL analysis in DLPFC, luciferase reporter gene assays, CRISPR/Cas9-mediated genome deletion and RT-qPCR. These assays highlighted several functional SVs showing regulatory effects on transcriptional activities, and some risk genes (e.g., BORCS7, GNL3) affected by the SVs were also annotated. Finally, mice overexpressing Borcs7 in the mPFC exhibited schizophrenia-like behaviors, such as abnormal prepulse inhibition and social dysfunction. These data suggest that SNPs association signals at GWAS loci might be driven by SVs, highlighting the necessities of considering such variants in future. [ABSTRACT FROM AUTHOR]

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. • An outlier-robust edRVRFL prediction model is developed. • Use VMD to decompose the data and enhance the model prediction capability. • Fuzzy entropy is used as a criterion for data aggregation. • AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD. [ABSTRACT FROM AUTHOR]

• Finish foot-terrain interaction experiments under large sinkage and dynamic sliding. • Establish mechanical models considering large sinkage and dynamic sliding effect. • Design a coronal foot with better anti-sinkage and tangential traction performance. Legged robots exhibit superior adaptability to complex extraterrestrial environments compared to wheeled mobile robots. However, legged robots employed in planetary exploration face challenges in dealing with soft terrains. This paper focuses on investigating the issues of large foot sinkage and slip encountered by legged robots on soft terrain. Extensive experiments on quasi-static loading, loading with impact and tangential force have been carried out for both spherical and cylindrical feet. The variations in normal force, tangential force, and sinkage are meticulously recorded and analyzed. Foot-terrain interaction mechanics models are established to address scenarios involving substantial sinkage and sliding sinkage, leveraging the stress distribution characteristics of deformable soil. Accurate models are obtained through parameter identification utilizing experimental data, which can aid in the foot design of legged robots intended for planetary exploration. Based on the developed models and experimental data, a design optimization scheme for the coronal foot is proposed, leading to performance enhancements that are validated through experimental verification. [ABSTRACT FROM AUTHOR]

The global increase in energy demand and environmental concerns have made the development of wind energy increasingly important. Accurate wind speed prediction is crucial for maximizing the benefits of wind energy. In order to further improve the accuracy of multi-site wind speed prediction, this study adopts an evolutionary algorithm-based deep learning model that fully considers the spatiotemporal relationships among multiple station's wind speed data. Firstly, the mutual information (MI) method is used to select variables with stronger correlations to wind speed as auxiliary input factors. Then, an improved version of Attribute-Augmented Spatiotemporal Graph Convolutional Network (IASTGCN) is employed to process data from multiple stations, taking into account both temporal and spatial factors. Additionally, an MI-based wind speed data relationship matrix between multiple stations is calculated to replace the original distance relationship matrix in the model, enabling the model to better capture and utilize the relationships between stations. Next, the Tree-structured Parzen Estimator (TPE) is used to optimize the hyperparameters of the model. This ultimately achieves multi-site multi-step wind speed prediction. Experimental results demonstrate that the proposed model outperforms baseline models and models that only consider either temporal or spatial factors in various scenarios, exhibiting better predictive performance. • A new hybrid model has been proposed for multi-site wind speed prediction. • The IASTGCN model is used to achieve multi-site multi-step wind speed prediction. • Partial improvements have been made to the ASTGCN model. • The TPE algorithm is used to optimize the hyperparameters of the model. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the clinical characteristics and outcomes on the success of bronchial arterial embolization (BAE) in patients with and without systemic artery-to-pulmonary vessel fistula (SA-PF) and to evaluate the feasibility of CTA in the assessment of SA-PF. Methods: We retrospectively enrolled 420 consecutive patients that underwent BAE for hemoptysis control in our hospital from September 2011 to May 2019. The clinical characteristics, preprocedural CTA findings, BAE procedural findings, and follow-up outcomes were collected. Patients were divided into two groups according to DSA findings: patients with SA-PF and those without. Results: A total of 184 (43.7%) patients presented with SA-PF. Pneumonia was less likely to be the concomitant condition in patients with SA-PF (p < 0.001). The mean number of culprit arteries per patient was significantly higher in patients with SA-PF compared to that in patients without SA-PF (p = 0.017). The SA-PF patients saw a greater probability of recurrence (HR: 2.782, 95% CI: 1.617–4.784, p < 0.001). SA-pulmonary venous fistula (SA-PVF) favored lower hemoptysis recurrence rate (HR: 0.199, 95%CI: 0.052–0.765, p = 0.019). SA-pulmonary artery fistula (SA-PAF) can be detected by optimized CTA protocol with a detection rate of 65.3% (49/75). Conclusions: The presence of SA-PF is an independent risk factor predicting early recurrence of hemoptysis after BAE. SA-PVF seems to be a protective factor for longer hemoptysis control compared to SA-PAF. Optimized preprocedural CTA is a reliable examination to identify SA-PAF. Key Points: • The appearance of SA-PF is associated with a greater probability of early recurrent hemoptysis after bronchial artery embolization. • The presence of SA-PVF seems to be a protective factor for longer hemoptysis control after BAE compared to SA-PAF. • Optimized CTA protocol seems to be a promising auxiliary examination to detect SA-PAF. [ABSTRACT FROM AUTHOR]

The human-centric philosophy is considered a crucial development direction for smart Product-Service Systems (smart PSS). Smart PSS has effectively advanced the circular economy by enhancing user experience, incorporating servitization and digital servitization, and extending product lifecycle, among other key aspects of sustainable development. However, there is currently a lack of a reasonable, accurate, and actionable framework and methodology to express the human element, namely user profiling, within this philosophy. To address this gap, this paper utilizes statistical methods such as factor analysis, cluster analysis, and regression analysis, building upon the concept of traditional user profiling. The aim is to integrate the three prominent approaches of goal-oriented, scenario-based, and data-driven user profiling, with the goal of complementing each other and designing a top-level user profiling framework for smart PSS. Furthermore, Industry 4.0 technologies and text mining techniques are employed to collect data on users' product and service usage. As these data contain real-time information about user needs, behaviors and goals at different time periods, they can be used to construct dynamic features of user profiling, and ultimately achieve the construction of dynamic user profiling for smart PSS. To validate the proposed user profiling framework and dynamic user features of smart PSS, this paper presents a case study focusing on the user group of maternal women. This case study promotes the in-depth exploration of smart PSS research in expressing the human element. [ABSTRACT FROM AUTHOR]

• Propose a new FE-analytical slice model for load distribution analysis of PGT. • Reveal the load distribution behavior with coupling effects of elasticity and errors. • Optimize the load distribution by tooth modification and in-phase eccentricity arrangement. • Conduct the load distribution simulation and test of PGT to verify the proposed model. Load distribution behavior of planetary gear train (PGT) is crucial for the contact and bending fatigue failure of wind turbine transmission system. With the increasing power of wind turbines, the load and failure rate of wind turbine become larger, which leads to the demand of accurate and efficient modeling, analysis and optimization for load distribution of PGT. In this paper, a new efficient and accurate FE-analytical slice model for load distribution analysis and optimization of PGT in wind turbine is proposed, which is explicitly formulated by the geometry slice model of external/internal gear contact, original FE-analytical slice model for local contact deformation, and efficient finite element (FE) model for global structural deformation. In the FE-analytical slice model, geometry errors are effectively descripted by discretizing gears into a series of slices, while gear tooth contact elasticity and structural elasticity are addressed by the FE-analytical method accurately and efficiently. Upon the proposed model, the load distribution behaviors of PGT with coupling effects of elasticity and errors are revealed and optimized by tooth modification and in-phase eccentricity arrangement strategy. The load distribution tests of PGT in wind turbine are conducted, verifying the proposed FE-analytical slice model which is significant to improve the load-bearing capacity and avoid the fatigue failure of wind turbine transmission system. [ABSTRACT FROM AUTHOR]

• A novel wind power prediction method based on influencing factors is proposed. • A multivariate model based on TCN-ReOSELM is proposed for the first time. • PC technology is utilized to process the input data of the model. • An improved AHA algorithm is introduced to optimize the TCN-ReOSELM model. • The superiority of the proposed model is verified by ten benchmark models. Highly accurate wind power prediction is important for the operation and management of wind farms, energy utilization and power supply. In recent years, in order to further improve prediction accuracy, some researchers have considered utilizing factors such as wind speed and wind direction to establish prediction models. However, this often results in input redundancy due to a lack of technical means. In this paper, a multivariate prediction model for wind power based on variational mode decomposition (VMD), fuzzy entropy (FE), partial auto-correlation function and cross-correlation function (PACF&CCF, abbreviated as PC), improved artificial hummingbird algorithm (IAHA), temporal convolutional network (TCN), and regularized online sequential extreme learning machine (ReOSELM) is proposed. To address the nonlinearity and non-stationarity of the original wind power time series and decrease computational costs, the VMD combined with FE data preprocessing technique is employed. Considering the information redundancy in the multivariate data, the PC technique is utilized to conduct correlation analysis on the input data and select highly correlated features as inputs. When the input is multivariate and high-dimensional, the ReOSELM model struggles to achieve satisfactory prediction performance. Therefore, a new model based on TCN-ReOSELM is constructed. Additionally, to overcome the drawbacks of manual parameter adjustment, an IAHA algorithm is proposed, which integrates Latin hypercube sampling and chaotic local search strategies, to optimize the crucial parameters of TCN-ReOSELM. The experimental results demonstrate that the multivariate method proposed in this paper reduces the RMSE by 31% compared to the univariate method. Specifically, the proposed VMD-FE-PC-TCN-ReOSELM model reduces the RMSE, MAE, and SMAPE by 60–64%, 55–58%, and 10–36%, respectively, compared to the PC-TCN-ReOSELM model. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is crucial for their safe and stable operation. This study proposes a hybrid deep learning model based on Gaussian data augmentation (GDA), the TimesNet model, error correction (EC), and an improved Bayesian algorithm called Sequential Model-based Algorithm Configuration (SMAC) for SOC and SOE estimation in lithium-ion batteries. Firstly, we compared the performance of the TimesNet model with other benchmark models. Then, GDA data with different signal-to-noise ratios were used for testing, and the model's performance was improved using GDA data with appropriate signal-to-noise ratios. Finally, an error correction method was employed to further enhance the estimation accuracy. During the experiment, SMAC was used to optimize its hyperparameters. In NN and UDDS drive cycles at temperatures of 0 °C, 10 °C, and 25 °C, the highest RMSE values for SOC and SOE estimation of the proposed model were 0.105%, 0.098%, 0.227%, and 0.213%, respectively. Experimental results demonstrate that the TimesNet model achieves good prediction performance for SOC and SOE estimation. GDA and EC effectively enhance the accuracy of the model. • Using visual networks to analyze the operational data of lithium batteries. • Gaussian data augmentation is applied to optimize the raw data. • Error correction is used to improve the estimation accuracy of SOC and SOE. [ABSTRACT FROM AUTHOR]

High hydrostatic pressure (HHP) treatment (< 200 MPa) has been known to increase the emulsification properties of myosin. However, the relationship between interfacial elasticity and emulsion stability is complicated. Therefore, this study was focused on exploring the effects of high hydrostatic pressure on the physiochemical and structural properties of interfacial proteins within a stabilized oil-in-water (O/W) rabbit myosin emulsion. We analyzed the adsorption rate and structure of interfacial proteins pressurized under 0.1 [control], 100, 150, and 200 MPa prior to emulsion preparation. As the pressure was increased from 0.1 to 150 MPa, the adsorption rate of interfacial protein increased. HHP treatment caused myosin to unfold, which enhanced the exposure of reactive sulfhydryl and hydrophobic groups. Furthermore, the frequency of hydrogen bonding and hydrophobic interactions increased. As hydrogen bonding increased, the secondary structure of myosin became more stable. As the pressure increased to 200 MPa, the interfacial myosin aggregated, displaying a decreased surface hydrophobicity and reactive sulfhydryl groups (P < 0.05) and thus the emulsion stability decreased. In conclusion, HHP treatment of myosin at 150 MPa significantly improved the stability of the myosin emulsion. Myosin is an important protein in meat and its emulsion stability can be enhanced by HHP. This study reveals the conformational mechanism by which HHP improves the stability of myosin emulsions, providing potential theoretical basis for the application of HHP treatment to improve the quality of emulsified meat products as a non-thermal processing technology in the practical production. [Display omitted] • Moderate HHP promoted the unfolding of myosin and its interfacial adsorption. • 150 MPa optimally increased surface hydrophobicity and reactive SH groups content. • 150 MPa optimally enhanced the hydrogen bonding and hydrophobic interaction. • Excessive HHP led to myosin aggregation and impeded myosin adsorption. [ABSTRACT FROM AUTHOR]

• An improved model DWTimesNet based on dilated convolution and WN is proposed. • RF was used to reduce the effect of missing data on the proposed model. • To eliminate the effect of some variables, MIC is used for feature selection. • The PBT algorithm is used to efficiently parameterize the proposed model. • Two cases with different data quality are used to verify the proposed model. The share of wind power in global electricity generation is increasing year by year, and the prediction of wind power is a practical and necessary scientific research. In this paper, the TimesNet model is used for multistep prediction of wind power. It is observed that the prediction error of the TimesNet model could be improved by replacing the original convolutional structure with the dilated convolution and weight normalization (WN). And in addition, the population-based training (PBT) algorithm is introduced to guide the model tuning. Missing values in the raw data are imputed to enhance data integrity. And the maximal information coefficient (MIC) method is chosen to select features for the different influences on wind power, which reduces the computational effort of the model. In order to demonstrate the effectiveness of the proposed model MIC-PBT-DWTimesNet, nine models are arranged in this paper as a control group and compared with their predicted values with each other. It can be observed that the RMSE of the proposed model MIC-PBT-DWTimesNet is decreased by 17–33% compared to the base TimesNet method. [ABSTRACT FROM AUTHOR]

Making residential parking spots available to the public has become popularized in recent years. The sharing of residential parking spots can promote the further use of parking space and enhance the utility of parking resources in urban metropolitan areas. However, little is known about the relationship between spots' physical or temporal factors and rental effects from practical experience. This study aims to evaluate the effects of residential parking spot sharing from both individual and social benefit perspectives. One-year real behavioral records concerning parking spots' owners and borrowers were obtained, and the field survey of various parking spots' physical characteristics was conducted. Two Partial Least Squares Structural Equation Modeling (PLS-SEM) models emphasizing the individual and societal points of view were adopted. Results revealed that the spots' physical factors, including spot type, visibility, ease of parking, and distances to major surrounding buildings, along with owners' sharing willingness and preferences, tend to pose significant influences on the rental effects from both individual and social benefit perspectives. Some differences were also discovered between the two models. For the individual model, owners' sharing willingness was the dominant factor affecting the parking spots' sharing effects, while for the social model, parking spots' physical characteristics appear to be more important in determining the sharing effects. Based on these findings, suggestions were discussed to promote residential parking spot sharing and increase the benefits of sharing to individuals and society. [ABSTRACT FROM AUTHOR]

Background: DDP-based chemotherapy is one of the first-line treatment in GC. However, the therapeutic efficacy of DDP is limited due to side effects. Therefore, it is of great significance to develop novel adjuvants to synergize with DDP. We had demonstrated previously that rMV-Hu191 had antitumor activity in GC. Here we examined the synergism of rMV-Hu191 with DDP in vitro and in vivo. Methods: Cellular proliferation, the synergistic effect and cell apoptosis were evaluated by CCK-8 assay, ZIP analysis and flow cytometry, respectively. The protein levels and location of ASMase were monitored by western blot and immunofluorescence assay. shRNA and imipramine were used to regulate the expression and activity of ASMase. MβCD was administrated to disrupt lipid rafts. Mice bearing GC xenografts were used to confirm the synergism in vivo. Results: From our data, combinational therapy demonstrated synergistic cytotoxicity both in resistant GC cell lines from a Chinese patient and drug-nonresistant GC cell lines, and increased cell apoptosis, instead of viral replication. Integrity of lipid rafts and ASMase were required for rMV-Hu191- and combination-induced apoptosis. The ASMase was delivered to the lipid raft microdomains at the initial stage of rMV-Hu191 treatment. In vivo GC mice xenografts confirmed the synergism of combinational treatment, together with increased apoptosis and trivial side-effects. Conclusions: This is the first study to demonstrate that rMV-Hu191 combined with DDP could be used as a potential therapeutic strategy in GC treatment and the ASMase and the integrity of lipid rafts are required for the synergistic effects. [ABSTRACT FROM AUTHOR]

To expound the roles of mTOR and NF-kB signaling pathway in intermittent hypoxia (IH)-induced damage of hippocampal neurons. For rat experiments, mTOR inhibitor (Rapamycin, Rapa) and NF-κB signaling inhibitor (ammonium pyrrolidine dithiocarbamate, PDTC) were applied to inhibit mTOR and NF-κB signaling, respectively. For neuron experiments, hippocampal neurons from rat were successfully cultured. Different concentrations of Rapa and PDTC were added to the cultured hippocampal neurons. Rat or primary hippocampal neurons were exposed to normoxic or IH conditions after administration of Rapa and PDTC. The effects of Rapa and PDTC administration on learning and memory ability of rats and hippocampal injury after IH exposure were assayed by Morris water maze and H&E staining. Electron microscope was utilized to examine primary hippocampal neuron ultrastructure changes after IH exposure and Rapa or PDTC administration. The expressions of NF-κB-p65, IκBα, IKKβ, BDNF, TNF-α, IL-1β, PSD-95 and SYN in hippocampal neurons were examined. Compared with normal control rats or neurons, IH-treated group had elevated expression levels of NF-kB, TNF-α and IL-1β and suppressed expression level of BDNF, PSD-95 and SYN. These results were reversed upon pre-treatment with Rapa and PDTC. Furthermore, IκBα and IKKβ expressions were down-regulated in IH group. No notable difference was manifested in PDTC pre-treatment group, while a prominent increase was shown after Rapa pre-administration. The administration of PDTC and Rapa could prevent IH-induced hippocampal neuron impairment, indicating that inhibition of the mTOR and NF-κB pathway may likely act as a therapeutic target for obstructive sleep apnea. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of 5G technology, computer technology, communication technology, and the financial industry, people have paid more and more attention to financial information security issues. This article aims to use PCA technology and optimized BP neural network, and merge the two together to build a financial information security model and conduct in-depth research. This paper proposes the PCA technology, the principal component analysis technology, which uses the concept of dimensionality reduction to convert multiple indexes into partially integrated indexes. It is a data set simplification technology and the reverse propagation algorithm training according to a certain error. The BP neural network with many levels and multiple feedbacks carried out a financial information security model simulation experiment, and finally obtained experimental data to support the model. The experimental results of this article show that in 2020, my country has become the country with the highest network coverage in the world, reaching 64.3%, and the number of denizens has reached 632 million. In this experiment, there are a total of 13,896 pieces of useful data, including 4 types. Therefore, current research attaches great importance to the field of financial information security. [ABSTRACT FROM AUTHOR]

Background: The optimal sequence of pulmonary vessel interruption during lung cancer resection remains controversial. This review aimed to elucidate the association of vein-first versus artery-first ligation and survival of the patients.Methods: We searched PubMed, Web of Science, Scopus, Embase, Cochrane Library and Google Scholar from their inception to September 2021 for published articles that compared vein-first (the pulmonary vein was interrupted first) and artery-first procedure (the pulmonary artery was ligated first) during lung cancer surgery.Results: Finally, a total of 13 full articles were obtained. First, 7 studies with survival information were included for meta-analyses. As compared with the artery-first ligation, vein-first approach did not decrease the risk of local recurrence (risk ratio [RR] 0.92 in favour of vein-first; 95% confidence interval [CI] 0.61-1.39, p = 0.68) or distant metastasis (RR 0.92; 95% CI 0.30-2.85, p = 0.89); but it was associated with better disease-free survival (RR 0.52; 95% CI 0.37-0.73, p < 0.01) as well as 5-year overall survival (RR 0.60; 95% CI 0.41-0.86, p < 0.01). In addition, the operative time, intraoperative blood loss, total complications, and length of postoperative stay were mainly comparable between the two groups. Second, 7 studies provided the data of tumor cells indicated by different biomarkers and detection methods; and 3 of these reports showed that vein-first ligation decreased the extent of intraoperative tumor dissemination. However, a quantitative meta-analysis was not possible due to the significant heterogeneity.Conclusion: Vein-first ligation in lung cancer surgery may be associated with improved survival of the patients, which might be ascribed to potentially lower risk of tumor cell dissemination. Well-designed, large-scale trials are warranted to clarify these occasional findings. [ABSTRACT FROM AUTHOR]

Background: The dual inhibitors of receptor interacting protein kinase-1 and -3 (RIP1 and RIP3) play an important role in cell death processes and inflammatory responses. White matter injury (WMI), a leading cause of neurodevelopmental disabilities in preterm infants, which is characterized by extensive myelination disturbances and demyelination. Neuroinflammation, leads to the loss and differentiation-inhibition of oligodendrocyte precursor cells (OPCs), represents a major barrier to myelin repair. Whether the novel RIP1/RIP3 dual inhibitor ZJU-37 can promote transplanted OPCs derived from human neural stem cells (hOPCs) survival, differentiation and myelination remains unclear. In this study, we investigated the effect of ZJU-37 on myelination and neurobehavioral function in a neonatal rat WMI model induced by hypoxia and ischemia. Methods: In vivo, P3 rat pups were subjected to right common carotid artery ligation and hypoxia, and then treated with ZJU-37 or/and hOPCs, then OPCs apoptosis, myelination, glial cell and NLRP3 inflammasome activation together with cognitive outcome were evaluated at 12 weeks after transplantation. In vitro, the effect of ZJU-37 on NLRP3 inflammasome activation in astrocytes induced by oxygen–glucose deprivation (OGD) were examined by western blot and immunofluorescence. The effect of ZJU-37 on OPCs apoptosis induced by the conditioned medium from OGD-injured astrocytes (OGD-astrocyte-CM) was analyzed by flow cytometry and immunofluorescence. Results: ZJU-37 combined with hOPCs more effectively decreased OPC apoptosis, promoted myelination in the corpus callosum and improved behavioral function compared to ZJU-37 or hOPCs treatment. In addition, the activation of glial cells and NLRP3 inflammasome was reduced by ZJU-37 or/and hOPCs treatment in the neonatal rat WMI model. In vitro, it was also confirmed that ZJU-37 can suppress NLRP3 inflammasome activation in astrocytes induced by OGD. Not only that, the OGD-astrocyte-CM treated with ZJU-37 obviously attenuated OPC apoptosis and dysdifferentiation caused by the OGD-astrocyte-CM. Conclusions: The novel RIP1/RIP3 dual inhibitor ZJU-37 may promote OPC survival, differentiation and myelination by inhibiting NLRP3 inflammasome activation in a neonatal rat model of WMI with hOPC graft. [ABSTRACT FROM AUTHOR]

Drag-free control is one of the key technologies for the verification of Taiji-1 satellite. In the direction of sensitive axis, the drag-free controller receives the measurement signal from the high-precision gravitational reference sensor on the satellite, and instructs the micro-thruster system to counteract the disturbance force acting on the sensitive axis, so that the microgravity level in the sensing axis direction of the satellite can reach the order of 10 − 9 m/s2 in the measurement band. In order to fully verify the drag-free control system, a ground one-dimensional drag-free semi-physical simulation system is built to simulate the performance of various payloads in the drag-free control loop, and to verify the performance and technical targets that the drag-free control system can achieve in the ground control loop. Through the small angle approximation, the equivalent relationship between the rotation of the experimental model and the translational motion of the experimental satellite in the direction of drag-free is demonstrated. In the condition of neglecting the stiffness and damping of the suspended pendulum, the parameters of the suspended pendulum are designed according to the principle of equal acceleration, and their effectiveness is verified by numerical simulation. According to the operation mode of on orbit drag-free control, the ground drag-free experimental scheme and drag-free controller are designed, and the experimental research and verification are carried out. The results show that the controller can effectively control the displacement and acceleration of the experimental model, and also can effectively suppress the disturbance of certain amplitude and frequency. [ABSTRACT FROM AUTHOR]

A novel Gram-stain-negative, aerobic strain, designated Y22T, was isolated from peanut field soil in Laoshan Mountain in China. Cells of strain Y22T were rod-shaped and motile by a single flagellum. The strain was found to be oxidase- and catalase-positive. 16S rRNA gene sequence based on phylogenetic analysis indicated that strain Y22T belonged to the genus Pseudomonas, and showed the highest 16S rRNA gene sequence similarity of 99.0% to Pseudomonas pelagia JCM 15562T, followed by Pseudomonas salina JCM 19469T (98.4%), Pseudomonas sabulinigri JCM 14963T (97.9%), Pseudomonas bauzanensis CGMCC 1.9095T (97.6%) and Pseudomonas litoralis KCTC23093T (97.5%). The phylogenetic analysis based on multilocus sequence analyses with concatenated 16S rRNA, gyrB, rpoD and rpoB genes indicated that strain Y22T belonged to Pseudomonas pertucinogena lineage. The average nucleotide identity scores between strain Y22T and closely related species were 74.6–82.8%, and the Genome-to-Genome Distance Calculator scores were 16.4–44.9%. The predominant cellular fatty acids of strain Y22T were C18:1ω7c (29.6%), C17:0 cyclo (17.5%) and summed feature 3 (C16:1ω7c and/or C16:1ω6c) (17.4%). The genomic DNA G+C content was 57.9 mol%. On the basis of phenotypic characteristics, phylogenetic analyses and in silico DNA–DNA relatedness, a novel species, Pseudomonas laoshanensis sp. nov. is proposed. The type strain is Y22T (= JCM 32580T = KCTC 62385T = CGMCC 1.16552T). [ABSTRACT FROM AUTHOR]

It is of great significance to obtain the parameters of photovoltaic (PV) models quickly and accurately for the efficient operation and maintenance of PV power plants. A multi-strategy adaptive guidance differential evolution (AGDE) algorithm using fitness-distance balance (FDB) and opposition-based learning (OBL) is proposed for constrained global optimization of PV cells and modules. FDB and OBL are added on the basis of AGDE to improve the local search ability of the algorithm, and thus identify the parameters of the PV models faster and more accurately. Among the two improved strategies, FDB is a recently developed powerful method that can efficiently model selection processes in nature. In this study, the mutation mating pool of the AGDE algorithm is redesigned using the FDB method. OBL is adopted to increases the initial population diversity of AGDE. In order to verify the performance of the proposed FDB-AGDE in the parameter estimation for PV models, the experimental verification is carried out on two PV cells and three PV modules. The experimental results show that FDB-OADE has better accuracy and robustness in photovoltaic identification compared with other algorithms. [Display omitted] • The improved AGDE algorithm is used for PV parameter identification. • FDB is used to optimize the mutation operations of AGDE. • OBL enriches initial population diversity. • The comparison experiment of nine algorithms proves the superiority of the proposed algorithm. • The identification performance of the proposed FDB-OAGDE was tested on two photovoltaic cells and three photovoltaic modules. [ABSTRACT FROM AUTHOR]

When a manufacturer possesses demand information, there is an opportunity for the retailer to infer the manufacturer's demand information based on the wholesale price. This paper explores the impact of such inference on the retailer's unilateral information sharing and bilateral information exchanging strategies in a supply chain with one retailer and two competing manufacturers. Two scenarios were studied under information sharing and exchanging schemes: an intelligent retailer who infers the manufacturers' demand information from the wholesale prices and an unintelligent retailer who does not infer the information. Our analysis reveals that, regardless of whether the retailer infers or not, the retailer does not want to unilaterally share his information upward with the manufacturers. However, an unintelligent retailer is willing to exchange information with the manufacturers, and the inference does not fully undermine the retailer's incentives to exchange information. Furthermore, we find that, although the retailer's inference can help him cope with demand uncertainty, it can actually turn into a hazard (i.e., the retailer's intelligence hazard) under both information sharing scheme and information exchanging scheme; that is, being ignorant to the manufacturers' demand information may be a bliss to everyone. We finally observe that side payment transfer may help eliminate the intelligence hazard under bilateral information exchanging but cannot resolve the intelligence hazard under unilateral information sharing. • Explores impact of inference on the retailer's information sharing and exchanging. • Retailer does not want to unilaterally share information upward with manufacturers. • An unintelligent retailer is willing to exchange information with the manufacturers. • Being ignorant to the manufacturers' demand information may be a bliss to everyone. • Payment transfer may eliminate intelligence hazard under information exchanging. [ABSTRACT FROM AUTHOR]

The continuous development of advanced manufacturing has triggered a strong demand for technological service platforms. To better support the development of the technological service platform, it is necessary to conduct a performance evaluation for the technological service platform. However, the existing evaluation methods fail to fully consider the ambiguity and uncertainty of decision-makers' judgments. Meanwhile, conventional methods require quite a few pairwise comparisons when determining the criteria weights. This paper proposes a novel approach that integrates rough Z-number, the best worst method (BWM), and TODIM (an acronym in Portuguese of Interactive and Multi-Criteria Decision Making) to address these issues. The proposed method can significantly reduce the number of pairwise comparisons. It also shows a significant advantage in dealing with vague information and handling the reliability of the evaluation. Moreover, the TODIM method helps capture the decision-makers' risk attitude towards uncertainty. Finally, a case study of seven technological service platforms is conducted to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Water content of fresh oilseed rape leaves were determined by hyperspectral imaging. • Large number of leaves were used to establish regression models. • CNN-AT-LSTM-R model performed best for water content prediction. • Wavelengths contributing more for water content prediction models were explored. Water content is crucial for plant growth. Determination of water content can help monitor plant growth status. In this study, spectral data in the range of 900–1700 nm acquired by near-infrared hyperspectral imaging and corrected by black-white calibration were used to detect the water content of fresh oilseed rape leaves. The oilseed leaves were analyzed without particular treatments. Conventional machine learning (support vector regression, partial least squares regression and least absolute shrinkage and selection operator) and deep learning regression models (Convolutional Neural Network and Long Short-Term Memory) were developed to predict oilseed rape leaf water content. The performance of CNN-LSTM-R was highly accurate. The coefficient of determination and root mean square error of the testing set (RMSEP) were 0.814 and 0.005, respectively. The characteristic wavelengths with strong correlation with water content prediction of the regression models were analyzed. The results showed that the deep learning-based regression models showed great potential for water content determination of oilseed rape leaves. Therefore, this study provides an important theoretical basis and practical application for the detection of fresh plant water content. [ABSTRACT FROM AUTHOR]

Investigation of biomarkers may facilitate understanding the mechanisms of primary open-angle glaucoma (POAG) and developing therapeutic targets. This study aimed to identify potential genes based on competing endogenous RNA (ceRNA) network for POAG. Based on long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs) from the Gene Expression Omnibus (GEO) database, we identified differential expressed lncRNAs (DELs), differential expressed miRNAs (DEMis) and differential expressed mRNAs (DEMs) and then constructed a ceRNA network. Through weighted gene co-expression network analysis (WGCNA), we identified gender-specific genes for gender-associated ceRNA network construction, followed by the protein-protein interaction (PPI) network and functional enrichment analysis to screen hub genes and reveal their functions. The expression levels of hub genes were measured in steroid-induced ocular hypertension (SIOH) mice. A total of 175 DELs, 727 DEMs and 45 DEMis were screened between control and POAG samples. Seven modules were identified through WGCNA and one module was associated with gender of POAG patients. We discovered 41 gender-specific genes for gender-associated ceRNA construction and then identified 8 genes (NAV3, C1QB, RXRB, P2RY4, ADAM15, VAV3, ZNF207 and TOP1), which were enriched in cell cycle-related pathways and immune-related pathways. C1QB, RXRB, Top1 and ZNF207 were highly interacted with other proteins. The expression levels of NAV3 and C1QB were downregulated in SIOH, while the levels of RXRB, P2RY4, ADAM15, VAV3, ZNF207 and TOP1 were upregulated in SIOH. This study identifies hub genes associated with the pathogenesis of gender-specific POAG and provides potential biomarkers for POAG. • We established gender-related lncRNA-miRNA-mRNA network to identify 8 potential genes involved in POAG. • These potential genes were enriched in cell cycle procession and immune-related pathways. • This study might reveal the underlying pathogenesis of POAG and provid biomarkers for POAG treatment. [ABSTRACT FROM AUTHOR]

Accurate and reliable wind speed forecasting is vital in power system scheduling and management. Ensemble techniques are widely employed to enhance wind speed forecasting accuracy. This paper proposes a negative correlation learning-based regularized extreme learning machine ensemble model (NCL-RELM) integrated with optimal variational mode decomposition (OVMD) and sample entropy (SampEn) for multi-step ahead wind speed forecasting. For this purpose, the original wind speed time series is firstly decomposed into a few variational modes and a residue using OVMD, and then the decomposed subseries with approximate SampEn values are aggregated into a new subseries to reduce the computational burden. Secondly, a NCL-RELM ensemble model is employed to model each aggregated subseries. The NCL technique is employed to enhance the diversity among multiple sub-RELM models such that the predictability of a single RELM model can be enhanced. Finally, the prediction results of all subseries are added up to obtain an aggregated result for the original wind speed. The simulation results indicate that: (1) the NCL-RELM model performs better than other ensemble approaches including BAGTREE, BOOST and random forest; (2) the proposed OS-NCL-RELM model obtains the best statistical metrics from 1- to 3-step ahead forecasting compared with the other nine benchmark models. Image 1 • Negative correlation learning technique can enhance the performance of RELM. • The OVMD-SE technique is employed to decompose the original wind speed signal. • The RELM model performed better than the ANN and SVM models. • The proposed OS-NCL-RELM model performed better than the benchmark models. [ABSTRACT FROM AUTHOR]

A closing synthesis device with special structure was designed to effectively collect the ZnO synthesized via self-propagating high temperature synthesis (SHS) method. With this device, the influence of CaF2 on products was investigated. Products were characterized using scanning electron microscope (SEM) and X-ray diffraction (XRD). Results showed that with the increase of CaF2, there is a trend of first increase and following decrease in the yield of products and there appears increasing amount of one-dimensional ZnO nanostructures and there are no obvious changes on the structure of products. Possible mechanisms of the influence of CaF2 on products were discussed. [ABSTRACT FROM AUTHOR]

Large storage dam projects may modify geo-environmental conditions in many ways. The reservoir impoundment of the 285.5 m high Xiluodu arch dam located on the Jinsha River (China) caused large-scale canyon deformations, including significant canyon contraction and uplift movements from reservoir to downstream valley. The dam experienced subsequent tilting towards upstream and raised a safety concern of the project. A Thermo-Hydro-Mechanical (THM) mechanism is proposed for this extraordinary behavior. Due to reservoir impounding and seepage, significant temperature drops and fluid pressure increase within the underlying geothermal limestone aquifer in a synclinal basin are primary root causes. Finite element THM simulations successfully reproduce these unique deformations. Recent observations of large quantities of thermalized discharge water downstream and high pore pressure in the limestone layer provide further support for the proposed mechanism. Furthermore, refined numerical modeling is adopted to evaluate the safety of Xiluodu dam subjected to potential larger canyon contractions. We conclude that these unprecedented phenomena are dominantly the consequence of THM response to regional hydrogeological evolution following the build-up of a large reservoir. The accumulated canyon contractions at the current stage would not pose a direct threat to the dam safety, but a tripled situation may cause severe safety issues. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is a highly prevalent gynecologic malignancy and its mortality is extremely high. Therefore, the development of novel therapeutic approaches for OC is of great significance. In this study, LINC01342 was upregulated in OC tissue in the GSE38666 microarray and in tumor tissue samples collected in our center. The silencing of LINC01342 suppressed the proliferative and metastatic capacities of A2780 and HO8910 cells. Subcellular distribution assays showed that LINC01342 was mainly enriched in the cytoplasm. Subsequently, the downregulation of microRNA‐30c‐2‐3p was proven to be the target of LINC01342. The silencing of microRNA‐30c‐2‐3p enhanced the clonality and migratory capacity of OC cells. Moreover, the silencing of microRNA‐30c‐2‐3p could reverse the inhibited migration and clonality in OC cells caused by LINC01342 knockdown. In addition, hypoxia‐inducible factor 3 subunit α (HIF3A) was proven to be the target gene of microRNA‐30c‐2‐3p, which was upregulated. HIF3A was negatively regulated by microRNA‐30c‐2‐3p but positively regulated by LINC01342 in OC cells. An RNA binding protein immunoprecipitation assay showed that microRNA‐30c‐2‐3p, LINC01342, and HIF3A could bind to argonaute RISC catalytic component 2. The overexpression of HIF3A reversed the inhibited migration and clonality in OC cells with LINC01342 knockdown. By analyzing the follow‐up data from the enrolled OC patients, the LINC01342 and HIF3A levels were negatively correlated with prognosis, while the microRNA‐30c‐2‐3p level was positively correlated with the same. In short, the upregulated LINC01342 in OC absorbs microRNA‐30c‐2‐3p to release HIF3A. Thus, upregulated HIF3A expression accelerates the progression of OC. [ABSTRACT FROM AUTHOR]

Background: To investigate potential effects of herbicide phytotoxic on crops, a major challenge is a lack of non-destructive and rapid methods to detect plant growth that could allow characterization of herbicide-resistant plants. In such a case, hyperspectral imaging can quickly obtain the spectrum for each pixel in the image and monitor status of plants harmlessly. Method: Hyperspectral imaging covering the spectral range of 380–1030 nm was investigated to determine the herbicide toxicity in rice cultivars. Two rice cultivars, Xiushui 134 and Zhejing 88, were respectively treated with quinclorac alone and plus salicylic acid (SA) pre-treatment. After ten days of treatments, we collected hyperspectral images and physiological parameters to analyze the differences. The score images obtained were used to explore the differences among samples under diverse treatments by conducting principal component analysis on hyperspectral images. To get useful information from original data, feature extraction was also conducted by principal component analysis. In order to classify samples under diverse treatments, full-spectra-based support vector classification (SVC) models and extracted-feature-based SVC models were established. The prediction maps of samples under different treatments were constructed by applying the SVC models using extracted features on hyperspectral images, which provided direct visual information of rice growth status under herbicide stress. The physiological analysis with the changes of stress-responsive enzymes confirmed the differences of samples under different treatments. Results: The physiological analysis showed that SA alleviated the quinclorac toxicity by stimulating enzymatic activity and reducing the levels of reactive oxygen species. The score images indicated there were spectral differences among the samples under different treatments. Full-spectra-based SVC models and extracted-feature-based SVC models obtained good results for the aboveground parts, with classification accuracy over 80% in training, validation and prediction set. The SVC models for Zhejing 88 presented better results than those for Xiushui 134, revealing the different herbicide tolerance between rice cultivars. Conclusion: We develop a reliable and effective model using hyperspectral imaging technique which enables the evaluation and visualization of herbicide toxicity for rice. The reflectance spectra variations of rice could reveal the stress status of herbicide toxicity in rice along with the physiological parameters. The visualization of the herbicide toxicity in rice would help to provide the intuitive vision of herbicide toxicity in rice. A monitoring system for detecting herbicide toxicity and its alleviation by SA will benefit from the remarkable success of SVC models and distribution maps. [ABSTRACT FROM AUTHOR]

By introducing the artificial anisotropy (AA) parameters, a 3-D one-step leapfrog hybrid implicit–explicit finite-difference time domain (HIE-FDTD) method is proposed, which can reduce the numerical dispersion error without increasing the computational cost. The formulation of the AA one-step leapfrog HIE-FDTD method is obtained by introducing the relative permittivity and permeability tensors in the original one-step leapfrog HIE-FDTD method. The Courant–Friedrichs–Lewy (CFL) stability condition of the AA one-step leapfrog HIE-FDTD method is close to that of the original one-step leapfrog HIE-FDTD method. In addition, the proposed HIE-FDTD method has lower numerical dispersion error and higher calculation accuracy than that of the one-step leapfrog HIE-FDTD method. Finally, to testify the characteristics of the proposed HIE-FDTD method, numerical simulation experiments are given. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have attracted a lot of attention from researchers as the most promising photovoltaic devices due to their outstanding power conversion efficiency (PCE) and low-cost. Although the PSC have reached a PCE over 25%, the interface between the perovskite and hole transport layer (HTL) still is a crucial factor limiting higher PCE and long-term stability. In this work, we choose an organic molecule Bis(trifluoromethyl)benzo amide (BTFZA) to passivate defects at the perovskite/HTL interface. Owing to a strong interaction with uncoordinated Pb2+, the BTFZA effectively passivated the surface defects, remarkably promoted carrier transportation. As a result, the perovskite device based on BTFZA surface modification showed a PCE increase of 16.4% over the control device. Especially, the fill factor (FF) and the open-circuit voltage (V OC) of the modified device increased significantly. In addition, the unencapsulated BTFZA-modified device maintains better PCE compared to the control device after aging for over 600 h in ambient air with extreme humidity and temperature conditions. Results suggested that the introduction of BTFZA is an effective way to regulate the surface of perovskite film aiming at highly efficient and stable perovskite solar cells. The successful application of surface passivation strategy can effectively improve the efficiency of perovskite solar cells. [Display omitted] [ABSTRACT FROM AUTHOR]

Solar energy is highly economical and widespread in new energy applications, and analyzing solar radiation information is an important part of solar photovoltaic power applications. However, because of its data complexity and difficulty to measure, solar radiation data needs to be predicted. Temporal Convolutional Network (TCN) model is used to extract features and Artificial Ecosystem-based Optimization (AEO) algorithm is used to optimize the parameters of TCN. Out of consideration for the phenomenon of strong fluctuations and complex features of solar radiation data, the optimal variational mode decomposition (OVMD) method is incorporated into the model. First, the signal decomposition is performed on the original data to obtain several subsequences, and then aggregated by fuzzy entropy to reduce the number of sequences, after which the data are fed into the TCN model and the model parameters are optimized using the improved AEO algorithm, and finally the results of the model prediction are the output. Four months of solar radiation data are selected for testing, it is finally concluded that the OVMD-IAEO-TCN model can be used for solar radiation prediction with higher accuracy and reliability than the other nine comparison models. • A novel hybrid approach is proposed for solar radiation prediction. • Optimal VMD method and fuzzy entropy are used to decomposing time series. • The improved AEO algorithm is introduced to optimize the TCN model. • The hyperparameters of TCN are discussed in detail. • Eight benchmark models are used to verify the performance of the proposed model. [ABSTRACT FROM AUTHOR]