Search

Concept drift (CD) in data streams can significantly impact the performance and stability of data stream classification algorithms, diminishing the generalization capabilities of integrated learning models. This paper addresses CD issues in dichotomous data streams by introducing a novel modeling approach that leverages evolutionary computation techniques. The method entails grouping base learners based on their performance within a sliding window and then evolving the base learning periods using evolutionary techniques. Furthermore, the concept of “gene flow” is introduced to enhance diversity among base learners and improve CD prediction performance. Experimental results on real and artificial datasets demonstrate the superior comprehensive performance of the proposed method. Specifically, the BCDECA algorithm outperforms other similar methods, excelling in accuracy, diversity, convergence rate, and robustness on a range of datasets.Article Highlights: Addresses CD issues in dichotomous data streams by introducing a novel modeling approach that leverages evolutionary computation techniques. Grouping base learners based on their performance within a sliding window and then evolving the base learning periods using evolutionary techniques Introduce the concept of "gene flow" [ABSTRACT FROM AUTHOR]

Recent years have witnessed the transformative impact from the integration of artificial intelligence with organic and polymer synthesis. This synergy offers innovative and intelligent solutions to a range of classic problems in synthetic chemistry. These exciting advancements include the prediction of molecular property, multi-step retrosynthetic pathway planning, elucidation of the structure-performance relationship of single-step transformation, establishment of the quantitative linkage between polymer structures and their functions, design and optimization of polymerization process, prediction of the structure and sequence of biological macromolecules, as well as automated and intelligent synthesis platforms. Chemists can now explore synthetic chemistry with unprecedented precision and efficiency, creating novel reactions, catalysts, and polymer materials under the data-driven paradigm. Despite these thrilling developments, the field of artificial intelligence (AI) synthetic chemistry is still in its infancy, facing challenges and limitations in terms of data openness, model interpretability, as well as software and hardware support. This review aims to provide an overview of the current progress, key challenges, and future development suggestions in the interdisciplinary field between AI and synthetic chemistry. It is hoped that this overview will offer readers a comprehensive understanding of this emerging field, inspiring and promoting further scientific research and development. [ABSTRACT FROM AUTHOR]

A ventricular assist device (VAD) is a form of mechanical circulatory support that uses a mechanical pump to partially or fully take over the function of a failed heart. In recent decades, the VAD has become a crucial option in the treatment of end-stage heart failure in adult patients. However, due to the lack of suitable devices and more complicated patient profiles, this therapeutic approach is still not widely used for pediatric populations. This article reviews the clinically available devices, adverse events, and future directions of design and implementation in pediatric VADs. [ABSTRACT FROM AUTHOR]

The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has been dramatically improved, leading to human-like performances in understanding, language synthesizing, common-sense reasoning, etc. Such a major leap forward in general AI capacity will fundamentally change the pattern of how personalization is conducted. For one thing, it will reform the way of interaction between humans and personalization systems. Instead of being a passive medium of information filtering, like conventional recommender systems and search engines, large language models present the foundation for active user engagement. On top of such a new foundation, users’ requests can be proactively explored, and users’ required information can be delivered in a natural, interactable, and explainable way. For another thing, it will also considerably expand the scope of personalization, making it grow from the sole function of collecting personalized information to the compound function of providing personalized services. By leveraging large language models as a general-purpose interface, the personalization systems may compile user’s requests into plans, calls the functions of external tools (e.g., search engines, calculators, service APIs, etc.) to execute the plans, and integrate the tools’ outputs to complete the end-to-end personalization tasks. Today, large language models are still being rapidly developed, whereas the application in personalization is largely unexplored. Therefore, we consider it to be right the time to review the challenges in personalization and the opportunities to address them with large language models. In particular, we dedicate this perspective paper to the discussion of the following aspects: the development and challenges for the existing personalization system, the newly emerged capabilities of large language models, and the potential ways of making use of large language models for personalization. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of welding voltage, welding current, wire feeding speed, travelling speed and welding position on aspect ratio, dilution rate, microhardness and fluctuation using orthogonal experiment design. To achieve multi-objective optimization, S/N ratio conversion and grey relational analysis were employed, The model-predicted process parameters were then verified. The study findings indicate that the welding position has the greatest impact on aspect ratio, dilution rate and fluctuation, while the welding current has the greatest impact on the microhardness. Additionally, the force of the droplets at different positions affects the average aspect ratio and fluctuation, the arc shape affects the dilution rate, and the heat input affects the microhardness. Furthermore, the arc shape at different positions exhibits distinct characteristics. Finally, the optimal process parameters for different positions were obtained through an orthogonal test. The optimal combination of process parameters was found to be 70 A for welding current, 25 V for welding voltage, 500 cm.min−1 for wire feeding speed, 50 cm.min−1 for travelling speed, and vertical up welding position. The experimental results indicate that the optimized process parameters can consistently improve the welding quality, with a relative error of only 2.38% of only 2.38% between the grey relational degree and predicted value. This study's findings hold significant practical value for enhancing welding process stability and quality. [ABSTRACT FROM AUTHOR]

We developed and tested a theoretical model showing that authoritarian leadership has both positive and negative influences on employees' work performance. We posited that authoritarian leadership may shape both challenge stressors and hindrance stressors, which compel and undermine in-role and extra-role performance, respectively. We found consistent results across two studies. In Study 1, our results from two samples in different cultures showed that authoritarian leadership was positively related to objective performance (Sample 1: n = 402 Chinese chain restaurant managers) and extra-role performance (Sample 2: n = 369 U.K. police officers) via challenge stressors. Authoritarian leadership was negatively related to objective performance and extra-role performance via hindrance stressors. In Study 2 (n = 195 Chinese power industry employees), we replicated the findings of Study 1. Further, we found that authoritarian leadership behaviors among leaders who scored low on power distance orientation were not negatively related to in-role and extra-role performance via hindrance stressors. [ABSTRACT FROM AUTHOR]

Main conclusion: Mutation of OsSHR2 adversely impacted root and shoot growth and impaired plant response to N conditions, further reducing the yield per plant. Nitrogen (N) is a crucial factor that regulates the plant architecture. There is still a lack of research on it. In our study, it was observed that the knockout of the SHORTROOT2 (OsSHR2) which was induced by N deficiency, can significantly affect the regulation of plant architecture response to N in rice. Under N deficiency, the mutation of OsSHR2 significantly reduced root growth, and impaired the sensitivity of the root meristem length to N deficiency. The mutants were found to have approximately a 15% reduction in plant height compared to wild type. But mutants showed a significant increase in tillering at post-heading stage, approximately 26% more than the wild type, particularly in high N conditions. In addition, due to reduced seed setting rate and 1000-grain weight, mutant yield was significantly decreased by approximately 33% under low N fertilizer supply. The mutation also changed the distribution of N between the vegetative and reproductive organs. Our findings suggest that the transcription factor OsSHR2 plays a regulatory role in the response of plant architecture and yield per plant to N in rice. [ABSTRACT FROM AUTHOR]

In order to safeguard the surface structures from mining damage while optimizing the liberation of coal resources under the dense surface buildings of the Cedi River coal mine. Considering that the analysis of the structure and type of surface buildings and the geological mining conditions of the mine, a wide strip mining design with a retention width of 70 m and a mining width of 50 m was finally determined by using the pressure arch theory and Wilson's theory, combined with the actual layout of working faces 51,002, 51,004 and 51,006 at the site.The strip mining design is verified by probability integral method and FLAC3D numerical simulation calculation respectively, The findings indicate that the highest value of earth surface subsidence created by the mining of the wide strip is 210 mm, the surface horizontal deformation value is 1.0 to − 1.4 mm/m, the damage to surface buildings is less than Level I, which satisfying the prerequisites of the surface building protection level, and can realize the continuous advancement of mine 51,002, 51,004 and 51,006 working faces, The coal pillars of the retained strip have sufficient support strength and long-term consistency, and the movement and deformation of the overburden after mining will not cause undulating subsidence of the surface, which effectively solve the mine's technical difficulties in safely coal mining under surface buildings. [ABSTRACT FROM AUTHOR]

Medium-sized-ring compounds have been recognized as challenging synthetic targets in organic chemistry. Especially, the difficulty of synthesis will be augmented if an E-olefin moiety is embedded. Recently, photo-induced dearomative cycloaddition reactions that proceed via energy transfer mechanism have witnessed significant developments and provided powerful methods for the organic transformations that are not easily realized under thermal conditions. Herein, we report an intramolecular dearomative [5 + 4] cycloaddition of naphthalene-derived vinylcyclopropanes under visible-light irradiation and a proper triplet photosensitizer. The reaction affords dearomatized polycyclic molecules possessing a nine-membered-ring with an E-olefin moiety in good yields (up to 86%) and stereoselectivity (up to 8.8/1 E/Z). Detailed computational studies reveal the origin behind the favorable formation of the thermodynamically less stable isomers. Diverse derivations of the dearomatized products have also been demonstrated. Medium-sized-ring compounds are challenging synthetic targets in organic chemistry. Here the authors report an intramolecular dearomative [5 + 4] cycloaddition of naphthalene-derived vinylcyclopropanes to construct these molecules under visible-light irradiation and a proper triplet photosensitizer. [ABSTRACT FROM AUTHOR]

Given that defect detection in weld X-ray images is a critical aspect of pressure vessel manufacturing and inspection, accurate differentiation of the type, distribution, number, and area of defects in the images serves as the foundation for judging weld quality, and the segmentation method of defects in digital X-ray images is the core technology for differentiating defects. Based on the publicly available weld seam dataset GDX-ray, this paper proposes a complete technique for fault segmentation in X-ray pictures of pressure vessel welds. The key works are as follows: (1) To address the problem of a lack of defect samples and imbalanced distribution inside GDX-ray, a DA-DCGAN based on a two-channel attention mechanism is devised to increase sample data. (2) A convolutional block attention mechanism is incorporated into the coding layer to boost the accuracy of small-scale defect identification. The proposed MAU-Net defect semantic segmentation network uses multi-scale even convolution to enhance large-scale features. The proposed method can mask electrostatic interference and non-defect-class parts in the actual weld X-ray images, achieve an average segmentation accuracy of 84.75% for the GDX-ray dataset, segment and accurately rate the valid defects with a correct rating rate of 95%, and thus realize practical value in engineering. [ABSTRACT FROM AUTHOR]

One new seco-cyclic phorbol derivative and one new phorbol 12-monoester, named 13-oxo-14-isopropylphorbol (2), 12-angelylphorbol (5), together with four known compounds, named 4a-deoxyphorbol, 13-oxo-14-isopropenylphorbol, 12-isobutyrylphorbol, and phorbol-12-(2-methylbutyrate) were obtained by hydrolyzing croton oil. The structures of the compounds were elucidated by spectroscopic analysis. These compounds showed no cytotoxicity (CC50 > 200 μM). Phorbol-12-monoesters 4–6 showed the weak inhibitory activities on syncytia formation induced by HIV-1IIIB (4, EC50 = 56.05 μM; 5, EC50 = 72.97 μM; 6, EC50 = 52.58 μM). The seco-cyclic phorbol derivatives (2, 3) showed no anti-HIV-1 activities (EC50 > 200 μM), which indicated that chemical cleavage of cyclopropanols might affect anti-HIV-1 activity. [ABSTRACT FROM AUTHOR]

Background: Anthocyanins are a group of natural products widely found in plants. They have been found to alleviate the disorders of glucose metabolism in type 2 diabetes mellitus (T2DM), while the underlying mechanisms remain unclear. Methods: HepG2 and L02 cells were incubated with 0.2 mM PA and 30 mM glucose for 24 h to induce IR, and cells treated with 5 mM glucose were used as the control. C57BL/6 J male mice and db/db male mice were fed with a chow diet and gavaged with pure water or cyanidin-3-O-glucoside (C3G) solution (150 mg/kg/day) for 6 weeks. Results: In this study, the anthocyanin C3G, extracted from red bayberry, was found to alleviate disorders of glucose metabolism, which resulted in increased insulin sensitivity in hepatocytes, and achieved by enhancing the glucose consumption as well as glycogen synthesis in insulin resistance (IR) hepatpcytes. Subsequently, the expression of key proteins involved in IR was detected by western blotting analysis. Protein tyrosine phosphatase-1B (PTP1B), a negative regulator of insulin signaling, could reduce cellular sensitivity to insulin by inhibiting the phosphorylation of insulin receptor substrate-2 (IRS-2). Results of this study showed that C3G inhibited the increase in PTP1B after high glucose and palmitic acid treatment. And this inhibition was accompanied by increased phosphorylation of IRS proteins. Furthermore, the effect of C3G on improving IR in vivo was validated by using a diabetic db/db mouse model. Conclusion: These findings demonstrated that C3G could alleviate IR in vitro and in vivo to increase insulin sensitivity, which may offer a new insight for regulating glucose metabolism during T2DM by using the natural dietary bioactive components. [ABSTRACT FROM AUTHOR]

We revisit the condensation scenario of charged pions in external magnetic field and rotation, which was first considered by Y. Liu and I. Zahed. Based on the Ginzburg-Landau analysis of the Nambu-Jona-Lasinio model, we find that the charged-pion condensation takes place only when both a strong coupling constant and negatively large baryon chemical potential are applied. Besides, our numerical calculation shows that the chiral restoration induced by the interplay between magnetic field and rotation (i.e., the rotational magnetic inhibition) interrupts the formation of the charged-pion condensate. This suggests that the analysis of such condensation requires a careful treatment of the inner structure of pions, which was not taken into account before. We also discuss the underlying physical mechanism of our finding and the indication of charged-rho condensation. [ABSTRACT FROM AUTHOR]

In this paper, we study the next-to-next-to-leading-order (NNLO) QCD corrections for the process e+e− → J/ψ + J/ψ at the B factories. By including the NNLO corrections, the cross section turns negative due to the poor convergence of perturbative expansion. Consequently, to obtain a reasonable estimation for the cross section, the square of the amplitude up to NNLO is used. In addition, the contributions from the bottom quark and the light-by-light part, which are usually neglected, are also included. The final cross section is obtained as 1.76 − 1.66 + 2.42 fb at a center-of-mass energy of s = 10.58 GeV. Our result for total cross section and differential cross section could be compared with precise experimental measurement in future at the B factories. [ABSTRACT FROM AUTHOR]

Background: This study aimed to compare the prognostic discrimination power of pretreatment pathologic N stage (prepN), lymph node tumor regression grade (LNTRG), and posttreatment pathologic N (ypN) category for esophageal squamous cell carcinoma (ESCC) patients who received neoadjuvant chemoradiotherapy (nCRT) plus surgery. Methods: The study reviewed 187 ESCC patients from two medical centers who underwent nCRT plus surgery. Pathologic LNTRG was defined by the proportion of viable tumor area within the tumor bed in lymph nodes (LNs). An average LNTRG then was calculated by averaging the tumor regression grade (TRG) score of all resected LNs. Lymph nodes containing regression changes or vital tumor cells were used for interpretation of the prepN stage, which reflects the estimated number of originally involved LNs. Results: The ypN, prepN, and LNTRG categories had significant prognostic stratification power (p < 0.001, log-rank test). Multivariable cox regression showed that all three categories were independent prognostic factors of disease-free survival (DFS) (p < 0.05). The LNTRG category showed a better prognostic value for DFS prediction than the ypN and prepN categories (Akaike information criterion [AIC]: LNTRG [933.69], ypN [937.56], prepN [937.45]). Additionally, the superior predictive capacity of the LNTRG category was demonstrated by decision curve analysis. Similar results were discovered for patients with remaining diseased LNs. Conclusions: The three staging categories had prognostic relevance for DFS, with the LNTRG category seeming to have better prognostic indication power. Comprehensive consideration of the ypN status, prepN status, and LN regression may allow for better prognostic stratification of patients. [ABSTRACT FROM AUTHOR]

(1−x)Ba0.8Sr0.2TiO3–xBi(Mg0.5Zr0.5)O3 [(1−x)BST–xBMZ] relaxor ferroelectric ceramics were prepared by solid-phase reaction. In this work, the phase structure, surface morphology, element content analysis, dielectric property, and energy storage performance of the ceramic were studied. 0.84BST-0.16BMZ and 0.80BST-0.20BMZ have good dielectric temperature stability and low dielectric loss (0–200 °C, tanδ < 0.01), meeting the X8R capacitor standard (− 55–150 °C, ΔC/C25 °C ≤ ± 15%). In addition, 0.84BST-0.16BMZ also has high recoverable energy storage density (Wrec) of 2.31 J/cm3 and energy storage efficiency of 83% (η) at 320 kV/cm, compared to pure Ba0.8Sr0.2TiO3 ceramic, the maximum breakdown strength (BDS) of 0.84BST-0.16BMZ increased from 78 to 320 kV/cm, which is four times that of pure Ba0.8Sr0.2TiO3 ceramic, and after 107 cycles at room temperature, the recoverable energy storage density has almost no change, showing excellent fatigue resistance. The study provides a viable approach for the development of new lead-free energy storage ceramic capacitor and Class II-type ceramic capacitor. [ABSTRACT FROM AUTHOR]

In this study, a novel polyoxometalates-based metal-organic frameworks (POMOFs) microsphere, that is [Ni(phen)H2O]·V2O6 (phen is o-phenanthroline), have been successfully fabricated by one-pot simple hydro(solvent)thermal reaction through regulating different solvent ratios (H2O/EtOH). The experimental results show that the solvent effect has a significant effect on the micro-morphology and the catalytic activity of the materials. The dual-function in electrocatalytic hydrogen evolution and catalytic reduction of Cr6+ of the as-prepared POMOFs microspheres have been also studied. The POMOF(V,Ni)_3 catalyst prepared in the medium of pure ethanol have the best catalytic activity, in which the overpotential of 159 mV for the electrode is comparable to that of MoS2-based electrode material, and the rate constant k reaches 0.14 in the process of catalytic reduction of Cr6+. In this study, a novel polyoxometalates-based metal-organic frameworks (POMOFs) microsphere [Ni(phen)H2O]·V2O6 have been successfully fabricated by one-pot simple hydro(solvent)thermal reaction through regulating different solvent ratios. The dual-function in electrocatalytic hydrogen evolution and catalytic reduction of Cr6+ of the as-prepared sample have been also studied. [ABSTRACT FROM AUTHOR]

Spatiotemporal prediction in two-dimensional (2D) space has been widely studied in computer science and geosciences, such as video prediction and weather forecasting. However, many spatiotemporal evolutions take place in three-dimensional (3D) space, e.g., atmospheric temperature changes of multiple isobaric surfaces. For such a 3D multi-plane prediction task, existing methods usually fail to model the information interactions between different 2D planes. In this paper, we propose a novel neural network-based method to tackle 3D multi-plane spatiotemporal prediction tasks. Specifically, compared with prediction in 2D space, information interactions in 3D space are more complicated because of the emergence of a new dimension. To clarify these interactions, we first propose to separate them into three types, i.e., inter-level interaction, intra-level interaction, and unknown residual interaction. This separation is novel and comprehensive, which covers vertical, horizontal, and potentially unknown factors. Based on the separation, we design a new spatiotemporal module for prediction. It contains three units to capture the three separated interactions, respectively: (1) an inter-level interaction unit, which is the first one dedicated to modeling the information interaction between different levels. The inter-level interaction, as a new feature in 3D space, has not been effectively studied. Hence, in this unit, we propose to model it by leveraging the spatial associations between adjacent levels and developing a new dynamics simulation. (2) An intra-level interaction unit is utilized to model the information interaction within the same level. With the benefit of interaction separation, we can leverage the neural partial differential equations to formulate spatiotemporal dynamics and exploit prior physical knowledge in data. (3) Furthermore, a residual unit is employed to capture the remaining unknown and uncertain factors, which can help further improve the expressiveness of our model. As for a prediction, we employ the three units to capture various dynamics, and combine them to obtain a comprehensive one. The final dynamics will be decoded for generating prediction via a convolutional neural network. We conduct extensive experiments on a dataset of atmospheric temperature changes. The experimental results show that our method obtains a significant improvement and achieves a new state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Effectively detecting anomalies for multivariate time series is of great importance for the modern industrial system. Recently, reconstruction-based deep learning methods have been widely used in time series anomaly detection. However, the rich local and global characteristics of time series may not be well captured by methods that compress and reconstruct time series through a single-scale neural network. In addition, under the influence of a complex environment, small fluctuations occur during the normal operation of the system, which will also bring challenges for those methods to reconstruct exact values. In this paper, we propose an unsupervised multivariate time series anomaly detection method based on a probabilistic autoencoder with multi-scale feature extraction (PAMFE). The multiple parallel dilated convolutions with different dilation factors and feature fusion module enable PAMFE to capture overall and detailed information of time series to identify various types of anomalies better. Furthermore, considering the normal fluctuation of data, we reconstruct the expected distribution of input and calculate the anomaly score based on the probability that the input belongs to the distribution. Extensive experiments on four publicly real-world datasets demonstrate that PAMFE outperforms state-of-the-art methods in F1-Score. Moreover, we investigate the contributions of the major components of PAMFE, and the experimental results show that they all contribute to performance improvement. [ABSTRACT FROM AUTHOR]

In geophysical exploration, the expression of the electromagnetic field in homogeneous layered media involves Hankel integrals, and the kernel function can be simplified into a combination of zero- and first-order Bessel functions. Owing to the complex nature of the electromagnetic field, only a few analytical expressions for the Hankel integrals of the electromagnetic field in layered media exist. Moreover, as the argument of the Bessel functions increases, the Hankel integrals exhibit rapid oscillations and slow decay, making it difficult for the computation of the electromagnetic field to achieve high accuracy, especially for high-frequency and long-range transmission and reception scenarios. To address this issue, we propose a fully analytical integration method applicable to both high and low frequencies and long and short transmission and reception distances. The study used quadratic function interpolation for the coefficient part of the Hankel integrals in the expression of the electromagnetic field in layered media. The interpolated results within each element, multiplied by the Bessel functions, could derive analytical expressions. Then, by summing up all the discrete elements, the numerical solution of the Hankel integrals could be obtained, resulting in a high-precision numerical simulation of the layered media. The fully analytical integration algorithm was tested using the expressions of the electromagnetic field in the entire space with different forms of Bessel function integrals. The computational results demonstrated the correctness and high accuracy of the algorithm. The proposed algorithm exhibited high computational accuracy in different frequency ranges and transmission and reception distances, especially in high-frequency and long-distance scenarios, where its accuracy exceeds that of the four selected digital filtering algorithms by over two orders of magnitude, indicating its strong universality.. [ABSTRACT FROM AUTHOR]

Semi-supervised anomaly detection has received wide interest because of not requiring counterexamples during training. Existing competence measures for semi-supervised dynamic ensemble anomaly detection models do not consider the imbalance characteristic of training samples, which will result in serious overfitting on normal samples. This paper proposes two outlier-sensitive measures to estimate the competence of base classifiers for dynamic ensemble models. When a normal sample is correctly classified, both measures give a higher positive score to base classifiers with confidence closer to 0.5, which is different from the conventional idea that base classifiers with higher confidence should obtain higher scores. When a sample is misclassified, the Output-based Outlier-Sensitive measure calculates a negative score based on the confidence outputted by the base classifier, while the Cost-Sensitive-based Outlier-Sensitive measure gives a negative score based on the category of this sample. Multiple experiments are carried out on 30 datasets from public repositories under the unified framework proposed in this paper, and results show that dynamic ensemble models with our competence measures can outperform a number of typical ensemble models in terms of G-mean and F1, regardless of the pseudo outlier labeling methods and base classifier selection methods used in the model. [ABSTRACT FROM AUTHOR]

Creativity is important for a group's success, and thus, groups often demand that their members contribute creativity by setting up group goals. Group goal external regulation arises when individual members feel that their behavior is externally initiated and enforced. While research from the self-determination perspective suggests that such external regulation undermines creativity, emerging research also suggests the opposite: external regulation has motivational functions boosting creativity. To integrate these seemingly contradictory perspectives, we developed a contingency model (based on social dilemma theory) to explain the impacts of group goal external regulation. Specifically, conceiving creativity in the individual-group context of multiple goals, we suggest that group goal external regulation can prompt or hinder individual member creativity, depending on their individual goal progress ("concern for me") and group identification ("concern for us"). We found support from two studies that when group identification is high, individuals demonstrate higher levels of creativity, irrespective of their individual goal progress and group goal external regulation experienced. When group identification is low, individuals demonstrate more contingent responses—that is, external regulation is positively (negatively) related to individual member creativity when their individual goal progress is poor (good). [ABSTRACT FROM AUTHOR]

The Huashandong W deposit (7.7 Mt @ 0.26% WO3) is a newly discovered Neoproterozoic scheelite-dominated deposit in the world-class Jiangnan tungsten belt, South China. It occurs largely in metasedimentary rocks near the western margin of the Neoproterozoic Jiuling batholith and mainly consists of W-bearing exogreisen, hydrothermal breccias, and quartz veins. Two episodes of Neoproterozoic granite magmatism are identified with zircon U-Pb ages of ca. 830–823 and 807–804 Ma, respectively. According to cross-cutting relationships and mineral assemblages, two periods of hydrothermal alteration and W mineralization, corresponding to the two episodes of granite magmatism, are recognized in the Huashandong deposit. The early period is dominated by greisenization and associated scheelite mineralization whereas the late period is characterized by sericitization, hydrothermal brecciation, quartz veining, and associated scheelite and minor wolframite mineralization. Molybdenite Re-Os dating indicates that the early- and late-period W mineralization took place at ca. 828 and 800 Ma, respectively. Zircon trace element characteristics suggest that the Jiuling granite crystallized from reduced granitic magmas and has the potential for W mineralization. The petrologic and geochemical features of the Jiuling granite, coupled with the zircon U-Pb ages and Hf isotopic signatures and molybdenite Re-Os ages, imply that the Huashandong W deposit is genetically related to two phases of evolved granodiorites in the Jiuling batholith. The magmatic-hydrothermal nature of both metallogenic events is confirmed by H-O isotopes on hydrothermal quartz. [ABSTRACT FROM AUTHOR]

Background: Although histopathological evaluation after endoscopic submucosal dissection (ESD) is critical to assess the accuracy of endoscopic diagnosis, it is still challenging to perform precise endoscopic to pathological evaluation. We evaluated the importance of tissue marking dye (TMD)-targeted marking for post-ESD specimen guided by magnificent endoscope on histopathological accuracy and endoscopic-to-histopathological reconstruction. Study design: A total of 81 specimens resected by ESD [43 without TMD marking (N-TMD group), and 38 specimens with TMD-targeted cancerous areas marking guided by post-procedural magnifying endoscopy on resected specimens (TMD group)] between January 31, 2019, and January 31, 2022 at the Renmin Hospital of Wuhan University were included in the study. The baseline characteristics of patients, discrepancies between endoscopic and histopathological diagnosis, and the impact of TMD on histopathological diagnosis and reconstruction were analyzed. Results: Discrepancies between endoscopic (pre-ESD) and histopathological (post-ESD) diagnosis increased significantly in TMD group (68.4% (26/38) for tumor areas, 26.3% (10/38) for tumor margins, and 26.3% (10/38) for tumor differentiations) when compared with N-TMD group (p < 0.0001). Deeper sections were achieved in all TMD-marked resected lesions and 27.9% (12/43) lesions in the N-TMD group (p < 0.001). More pathological evaluations in TMD group were changed from curative resection to non-curative resection [6/38(15.8%) vs 1/43(2.3%)] compared with N-TMD group (p < 0.0001). TMD-targeted marking also improved the efficiency of histopathological reconstruction on pre-procedural endoscopic images and benefit endoscopists training. Conclusion: TMD-targeted labeling on resected specimens could improve precise endoscopic-to-pathological diagnosis, reconstruction by point-to-point marking and benefit endoscopists training. [ABSTRACT FROM AUTHOR]

In this paper, we calculate the next-to-next-to-leading-order (NNLO) QCD corrections to e+e− → J/ψ + ηc at the B factories. After including the NNLO corrections, the cross section of e+e−→ J/ψ +ηc is enhanced by about 17%, and the perturbative series of the prediction shows the convergent behavior. It is also found that the contributions from bottom quark starts at the α s 3 -order, which is about 2.4% of the total prediction. The renormalization scale μR dependence of the cross section is reduced at the NNLO level, but the prediction is sensitive to the charm quark mass mc. By considering the uncertainties caused by renormalization scale μR, charm quark mass mc and the NRQCD factorization scale μΛ, our prediction shows agreement with the BABAR and BELLE measurements within errors. [ABSTRACT FROM AUTHOR]

Fusarium wilt of banana is caused by the soil-borne pathogen Fusarium oxysporum f.sp. cubense (Foc). To increase the suppression activity, an improved mixed microbial community (a mixture of antagonists H-1, H-5, H-6, and H-7) was prepared for biofertilizer production, and underlying disease suppression mechanisms were determined. The results from pot experiments indicated that the biocontrol efficacies of organic acid soil ameliorant in combination with biofertilizer (OBIOs) and inorganic acid soil ameliorant in combination with biofertilizer (ABIOs) were 70.0% and 73.3%, respectively. OBIOs and ABIOs treatments also significantly increased the soil pH from 4.50 to 4.92 and 5.57. Real-time PCR analysis revealed that the densities of bacteria and actinomycetes increased, while those of fungi decreased, in the rhizosphere soil and/or fresh plant stems. The fermentation of antagonists H-1 and H-7 showed significant antagonistic effects on pathogens by increasing the malonaldehyde (MDA) concentration while reducing the ergosterol and protein concentrations and pectinase activities. The suppression efficacy of different treatments was significantly (P < 0.05) positively correlated with the ∆pH, the bacteria/fungi ratio, NH4-N, organic matter, and dissolved organic carbon, whereas it was negatively correlated with the pathogen density and NO3-N. In conclusion, the combination of acid soil ameliorant with biofertilizer showed a greater potential to control Fusarium wilt of banana, and the effect might result from improving the soil acid-alkali conditions and the toxicological effect on the pathogen. [ABSTRACT FROM AUTHOR]

Our aim is to solve the problems of cracking and porosity of laser cladding Ni60A/WC high-hardness composite coatings. Ta powder was added to synthesize TaC in situ with C in a molten pool, to reduce the carbon content of the metal matrix and inhibit the generation of carbon oxide gases to obtain a new Ni60A/WC composite coating with no defects, reasonable hard phase distribution, and excellent properties. The phase composition, microstructure, elemental distribution, and mechanical properties of the composite coating were studied by x-ray diffraction, scanning electron microscopy, and tribometry. The results show that Ta more easily combines with C. Adding an appropriate amount of Ta powder inhibits the formation of other carbides and carbon oxide gases, and significantly reduces the cracking sensitivity of the cladding layer. The fine TaC particles synthesized in situ are used as nucleation sites to significantly refine grains and improve microstructure uniformity. In addition, TaC particles with high hardness and good wear resistance also improve the microhardness and wear resistance of the cladding layer, which can provide a reference for the preparation of nickel-based composite coatings with excellent properties. [ABSTRACT FROM AUTHOR]

With the application of artificial intelligence technology, face detection is now not only concerned with accuracy but detection speed as well. However, most previous works have relied on heavy backbone networks and required prohibitive run-time resources, which seriously restricts their scope for deployment and has resulted in poor scalability. In this study, we used YOLOv5s, which has a good detection rate and accuracy, as the baseline network. First, we added a none-parameter channel attention self-enhancement module to allow the backbone of the network to capture the characteristic features of the face more effectively. Second, a low-level feature fusion module was added to enhance the features of shallow neural layers and then fuse them with the features of deeper layers. Third, a receptive field matching module allows the network's perceptual field to better match the scale of actual faces. Finally, contextual information based on face key points allows the face detector to exclude more cases of error and missed detections. On the most popular and challenging face detection dataset, WIDER FACE, our model performed better than the original network, with improvements of 3.8, 4.4, and 11.6% on the easy, medium, and hard subsets, respectively, and achieved a rate higher than 72 FPS, which meets the real-time requirements. [ABSTRACT FROM AUTHOR]

In this study, an analytical model based on Classic Laminate Theory was developed for the prediction of effective thermal expansion coefficients (ETECs) of general angle-ply laminates (symmetrical and asymmetrical). Examples are given of the applications of the analytical solution to the prediction of the ETEC of sandwich structures with fiber reinforced composite (FRC) core for various ply angles. The proposed analytical solutions are applied to predict the ETEC of laminate structures with fiber reinforced composite core for various ply angles. Laminate structures with negative thermal expansion (NTE) are obtained and considered as special cases of interest. For an extreme case, a laying angle of [0Ti/(± 30)2S/0Ti] and [0Ti/(± 60)2S/0Ti] along with an NTE of -1.335 × 10–5/K has been achieved. Meanwhile, Finite Strip Method is extended to the framework of first order shear theory, and the corresponding governing equations are established. With the help of the Bolotin method, the dynamic instability region of the sandwich structures with NTE is obtained. Numerical results show that the thermal vibration, buckling and dynamic instability behaviors may be adjusted and controlled by changing the TEC and stacking configurations. The present analytical results are not only theoretical fundament for understanding the interrelationship between the mechanical and temperature dependent material properties and the shrinkage of the structures but also serve as guiding NTE design of structures. [ABSTRACT FROM AUTHOR]

Cross-domain Sentiment Classification (CDSC) aims to exploit useful knowledge from the source domain to obtain a high-performance classifier on the target domain. Most of the existing methods for CDSC mainly concentrate on extracting domain-shared features, while ignoring the importance of domain-specific features. Besides, these approaches focus on reducing the discrepancy of the source domain and target domain on the word-level. As a result, they cannot fully capture the whole meaning of a sentence, which makes these methods unable to learn enough transferable features. To address these issues, we present a Sentence-level Attention Transfer Network (SentATN) for CDSC, with two distinctive characteristics. Firstly, we design an efficient encoder unit to extract domain-specific features of a sentence. Secondly, SentATN provides a sentence-level adversarial training method, which can better transfer sentiment across domains by capturing complete semantic information of a sentence. Comprehensive experiments have been conducted on extended Amazon review datasets, and the results show that the proposed SentATN performs significantly better than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As an important part of distributed graph computing, graph partitioning has been widely studied. However, the majority of the existing approaches to distributed graph partitioning barely take into consideration the relationship between the partition strategy and the graph algorithm's characteristics. The current graph partitioning strategies are on the basis of the graph structure, and the performance is diverse as running different graph algorithms. In this paper, considering the characteristics of a graph algorithm, we propose a distributed graph partitioning framework that can decide which partitioning strategy is better based on the program analysis. We also design a triangle-based partitioning strategy which benefits several graph algorithms such as the triangle counting. In addition, to reduce the number of shuffling operations in PageRank algorithm, we redesign the PageRank algorithm which considers the corresponding hash partitioning. The experimental results show that our adaptive graph partitioning framework implemented on Graphx surpasses the original one, especially for the triangle counting and PageRank, no matter on real-world graphs or synthetic graphs. [ABSTRACT FROM AUTHOR]

Owing to the safety and low toxicity, photodynamic therapy (PDT) for cancer treatment has received extensive attention. However, the excess H2S in cancer cells reduces the PDT efficiency, because H2S indirectly depletes the reactive oxygen species (ROS). To improve anticancer efficiency, a mitochondria-targeted iridium(III) complex Ir-MMB has been developed as H2S consumer and photo-oxidation anticancer agent. On the one hand, complex Ir-MMB can consume H2S with sensitive phosphorescence turn-on, which has been successfully applied to exogenous and endogenous H2S response imaging in living cells. On the other hand, Ir-MMB can enhance its anticancer activity and cause photo-oxidation damage via catalyzing the oxidation of reduced form of nicotinamide-adenine dinucleotide (NADH) to NAD+ and producing H2O2 under light, and ultimately results in cell apoptosis through mitochondrial depolarization and ROS production. [ABSTRACT FROM AUTHOR]

Anomaly detection problem has been extensively studied in a variety of application domains, where the data tags are difficult to obtain. Most unsupervised algorithms rely on some notions such as distance and density to detect anomalies. However, the performance of such algorithms is easier to decrease as the dimension of the datasets increases. Some studies which use features as pseudo-labels for prediction detect anomalies according to the deviation value of the prediction model. Even so, the improvement of model performance is still restricted to ignoring the correlation between feature attributes. In this paper, we propose a correlation-based feature partition regression prediction method called CFPR, which can alleviate the adverse effects of dataset dimensions and irrelevant attributes on model performance to a certain extent. According to the correlation between the features, the high-dimensional datasets will be divided into multiple feature subspaces. In each subspace, the feature with the highest correlation coefficient will be conducted as a pseudo-label. After that, we use the remaining features as the prediction attributes to train a supervised regression prediction model. We can calculate the anomaly score of each sample in the subspace according to the difference between the regression prediction value and the true value of the pseudo-label. Furthermore, we define a weighting strategy based on the level of correlation in the subspace integration stage to obtain the final anomaly score ranking table. Extensive experiments on twenty-eight UCI public datasets show that the CFPR performs better than several state-of-art anomaly algorithms at the AUC metric. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is an aggressive carcinoma with genome instability. Long non-coding RNAs (LncRNAs) have been functionally associated with genomic instability in cancers. However, the identification and prognostic value of lncRNAs related to genome instability have not been explored in hepatocellular carcinoma. In this study, we aim to identify a genomic instability-related lncRNA signature for predicting prognosis and the efficacy of immunotherapy in HCC patients. Methods: According to the somatic mutation and transcript data of 364 patients with HCC, we determined differentially expressed genome instability-related lncRNAs (GInLncRNAs). Gene ontology (GO) enrichment analyses and Kyoto Encyclopedia of genes and genomes enrichment analyses revealed the potential functions of genes co-expressed with those lncRNAs involved in cancer development and immune function. We further determined a genome instability-related lncRNA signature (GInLncSig) through Cox regression analysis and LASSO regression analysis. Thereafter, we performed correlation analyses with mutations, clinical stratification analyses, and survival analyses to evaluate GInLncSig predictive function. Subsequently, we construct a nomogram model for prognostic assessments of patients with HCC. Finally, we performed Immunocytes infiltration analysis, gene set enrichment analysis (ssGSEA) of immunity circle-associated pathways, and T cell-inflamed score to explore GInLncSig's potential value in guiding immunotherapy. Results: We identified 11 independent prognosis-associated GInLncRNAs (AC002511.2, LINC00501, LINC02055, LINC02714, LINC01508, LOC105371967, RP11_96A15.1, RP11_305F18.1, RP11_342M1.3, RP11_432J24.3, U95743.1) to construct a GInLncSig. According to the risk score calculated by GInLncSig, the high-risk group was characterized by a higher somatic mutation count, significantly poorer clinical prognosis, higher T cell-inflamed score, and specific tumor immune infiltration status compared to the low-risk group. Furthermore, we constructed a nomogram model to improve the reliability and clinical utility of predicting the prognosis of patients with HCC. Conclusion: Our study established a reliable prognostic prediction signature that could be a tool for prognosis prediction and a promising predictive biomarker of immunotherapy in hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]

The utilization of powders is the main factor determining the cost of laser-cladding industrial applications. The work established a 3D numerical model of gas-powder two-phase flow in the coaxial powder-feeding process to understand the gas-powder flow stream characteristics and improve the utilization of powders and the forming efficiency. The verified model was used to analyze the influences of the distance between the nozzle and substrate, carrier-gas flow rate, and powder feeding voltage on powders' concentration on the substrate, cladding morphology, and powders' utilization efficiency. The results showed that 9.5 mm was the optimal working distance of the nozzle, with the highest powder concentration on the substrate and the highest powder utilization rate. Appropriately increasing the gas flow rate was conducive to gathering powder particles; however, the increased gas flow rate aggravated particles to spray outwards and reduced the powder concentration on the substrate. After powder-feeding gas flow rate GF = 8 NL/min, the focused powder diameter no longer decreased. At this time, the cladding area was the maximum with the highest utilization rate of powders. As the powder feeding voltage increased, the cladding area and the utilization rate of powders increased. This model can accurately predict the transportation and concentration of powders on the substrate, which provides theoretical guidance for efficient forming by laser cladding. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-platform simulation method for the seismic performance assessment of masonry infilled reinforced concrete (RC) frames, especially for those that, due to inadequate reinforcing details, tend to undergo shear failure during an earthquake. The multi-platform method is based on a micro–macro modeling approach in which a detailed finite element model of the RC frame is incorporated with a strut model of the infill wall. It exploits the strut model to increase its computational efficiency and the finite element model based on the modified compression field theory to capture the nonlinear behaviour of the RC frame. The proposed method is validated against previously tested frames subjected to lateral loads, and its advantages over the conventional strut models are demonstrated through both quasi-static and dynamic analyses. [ABSTRACT FROM AUTHOR]

This study evaluated the effects of foliar spraying melatonin (MT) on the growth of salt-stressed rice. Seedlings were treated with 50 and 100 mM of NaCl and different concentrations of MT (25, 50, 100, 200, 300, and 400 μM) for 14 days. Different concentrations of MT could promote plant growth significantly under salt stress, particularly at concentrations of 200, 300, and 400 μM. A concentration of 200 μM MT was considered as optimal and used in a subsequent experiment on biomass, water content, antioxidation, mineral nutrition, salt absorption, and distribution of salt-stressed rice seedlings. Results showed that MT's promoting effect on plant growth under salt stress was evident with time, particularly under high salt stress. MT improved the activities of antioxidant enzymes, reduced membrane lipid peroxidation, alleviated cell injury in plant leaves, and increased N content and Si accumulation in the leaves and roots under salt stress, particularly under high salinity. This compound also inhibited Na uptake and upward transport, but it promoted or maintained the uptake and upward transport of K and Ca in salt-stressed rice. Thus, MT improved the ion homeostasis of K/Na and Ca/Na in plants, particularly in the leaves. Foliar spraying of MT alleviated salt stress on rice by promoting nutrient accumulation or translocation, improving ion homeostasis, which is evident in the leaves, and consequently enhancing its salt resistance. The antioxidative improvement caused by MT might also be related to the improved ion homeostasis. [ABSTRACT FROM AUTHOR]

The use of zinc oxide (ZnO) and titanium oxide (TiO2) nanoparticles in ultraviolet (UV) filters can cause serious health issues due to their genotoxicity and cytotoxicity effect. Recent research on cerium oxide (CeO2) nanoparticles has demonstrated their biocompatible nature, excellent antioxidant and ultraviolet–visible (UV–Vis) absorption properties. CeO2-based nanocomposite materials or nano-architectures can offer uniform absorptive properties, therapeutic skin effect and easy skin application. In this study, cerium oxide-polyethylene glycol (CeO2-PEG) nano-architecture was prepared by hydrothermal processing with the aim to improve stability, antioxidant properties, and enhance UV absorption for UV filtering applications. The morphology study of the developed nanocomposite revealed the formation of polymer bands with well-dispersed CeO2 nanoparticles. The nanocomposite structure has exhibited excellent UV absorption in the UV range 200–400 nm due to decrease in optical band gap in comparison to the naked CeO2 NPs. The evaluation of the photocatalytic activity (PCA) of the nanocomposite structure against P25 photocatalyst and ZnO nanoparticles showed no dye (crystal violet) degradation on UV irradiation. Hence, PCA assay verified the absence of ROS generation and increased stability of the nanostructure under UV irradiation. The investigation of antioxidant properties of the Ce-PEG nanocomposite by a dichlorofluorescein (DCF)-based assay revealed sufficient intracellular ROS scavenging in comparison to N-acetylcysteine (NAC) and H2O2 only assays. In vitro toxicity evaluation of Ce-PEG nanocomposite towards non-malignant human keratinocyte cell line (HaCaT) revealed a non-significant cell mortality ~ 10% of control at a dosage of 50 mg L−1. The nanocomposite structure also exhibited excellent UV protection for HaCaT cells under UV irradiation conditions compared to P25. Our findings suggest that the developed nanocomposite combined the antioxidant and UV absorption properties of CeO2 and skin emollient role of (PEG), and hence offered a novel biocompatible and multifunctional structure for sunscreens and therapeutic skin product. [ABSTRACT FROM AUTHOR]

We derive a Cooper-Frye type spin polarization formula for Dirac fermions at local thermal equilibrium described by a grand canonical ensemble specified by temperature, fluid velocity, chemical potential, and spin potential. We discuss the physical meaning of different contributions to spin polarization and compare them with previous work. The present formula provides machinery to convert the spin potential computed in, e.g., relativistic spin hydrodynamics to the spin polarization observable in, e.g., heavy-ion collisions. [ABSTRACT FROM AUTHOR]

Autophagy is a catabolic process to eliminate defective cellular molecules via lysosome-mediated degradation. Dysfunctional autophagy is associated with accelerated aging, whereas stimulation of autophagy could have potent anti-aging effects. We report that cannabidiol (CBD), a natural compound from Cannabis sativa, extends lifespan and rescues age-associated physiological declines in C. elegans. CBD promoted autophagic flux in nerve-ring neurons visualized by a tandem-tagged LGG-1 reporter during aging in C. elegans. Similarly, CBD activated autophagic flux in hippocampal and SH-SY5Y neurons. Furthermore, CBD-mediated lifespan extension was dependent on autophagy genes (bec-1, vps-34, and sqst-1) confirmed by RNAi knockdown experiments. C. elegans neurons have previously been shown to accumulate aberrant morphologies, such as beading and blebbing, with increasing age. Interestingly, CBD treatment slowed the development of these features in anterior and posterior touch receptor neurons (TRN) during aging. RNAi knockdown experiments indicated that CBD-mediated age-associated morphological changes in TRNs require bec-1 and sqst-1, not vps-34. Further investigation demonstrated that CBD-induced lifespan extension and increased neuronal health require sir-2.1/SIRT1. These findings collectively indicate the anti-aging benefits of CBD treatment, in both in vitro and in vivo models, and its potential to improve neuronal health and longevity. [ABSTRACT FROM AUTHOR]

Few studies have described the key features and prognostic roles of lung microbiota in patients with severe community-acquired pneumonia (SCAP). We prospectively enrolled consecutive SCAP patients admitted to ICU. Bronchoscopy was performed at bedside within 48 h of ICU admission, and 16S rRNA gene sequencing was applied to the collected bronchoalveolar lavage fluid. The primary outcome was clinical improvements defined as a decrease of 2 categories and above on a 7-category ordinal scale within 14 days following bronchoscopy. Sixty-seven patients were included. Multivariable permutational multivariate analysis of variance found that positive bacteria lab test results had the strongest independent association with lung microbiota (R2 = 0.033; P = 0.018), followed by acute kidney injury (AKI; R2 = 0.032; P = 0.011) and plasma MIP-1β level (R2 = 0.027; P = 0.044). Random forest identified that the families Prevotellaceae, Moraxellaceae, and Staphylococcaceae were the biomarkers related to the positive bacteria lab test results. Multivariable Cox regression showed that the increase in α-diversity and the abundance of the families Prevotellaceae and Actinomycetaceae were associated with clinical improvements. The positive bacteria lab test results, AKI, and plasma MIP-1β level were associated with patients' lung microbiota composition on ICU admission. The families Prevotellaceae and Actinomycetaceae on admission predicted clinical improvements. [ABSTRACT FROM AUTHOR]

Coastal wetlands are among the most carbon-rich ecosystems in the world. Litter decomposition is a major process controlling soil carbon input. Non-additive effect of litter mixture on decomposition has been observed in many terrestrial plants but rarely tested in coastal species. We selected three common mangrove species and one saltmarsh species co-occurring in subtropical coasts to test whether the non-additive effect occurs when their litters mixed together, and how the nutrients release into water are impacted consequently. A litter-bag experiment was carried out in a glasshouse with single and mixed leaf litters. Non-additive effects were observed in the litter mixtures of mangrove species Aegiceras corniculatum vs. Kandelia obovata (antagonistic) and A. corniculatum vs. Avicennia marina (synergistic), but not in the litter mixtures of A. corniculatum (mangrove species) vs. Spartina alterniflora (saltmarsh species). The strength of the non-additive effect was unrelated to the initial trait dissimilarity of litters. Instead, the decomposition rate and mass remaining of litter mixtures were strongly related to the carbon concentrations in litter mixtures. Nutrient content in waters was dependent on the decomposition rate of litter mixtures but not on the initial nutrient concentrations in litters. Despite the behind mechanisms were not yet revealed by the current study, our findings have improved the understanding of the litter decomposition of coastal species and the consequent nutrient release. [ABSTRACT FROM AUTHOR]

Zr substituted Bi0.5Y1.5−xCa1+xZrxV0.5Fe4.5−xO12 (x = 0.00, 0.25, 0.50, 0.75, 1.00) samples were prepared via solid-state reaction method. The structure, morphology, electromagnetic properties of Zr-doped Bi0.5Y1.5−xCa1+xZrxV0.5Fe4.5−xO12 were investigated by SEM, XRD, FT-IR, Raman, VSM, impedance analyzer, and vector network analyzer. The results showed that the Zr4+ and Ca2+ ions successfully entered the octahedral a-sites and dodecahedral c-sites, respectively. The saturation magnetization increased with the increase of doping concentration in low concentration range. However, the excessive dopant of diamagnetic Zr4+ ions resulted in a decrease in the saturation magnetization, indicating that the crystal lattice appeared short-range antiferromagnetic order at the d-sites, and the samples changed from ferromagnetism to paramagnetism. Among them, Bi0.5Y1.25Ca1.25Zr0.25V0.5Fe4.25O12 has the optimum comprehensive properties, Ms = 17.254 emu/g, Hc = 4.043 Oe, Mr = 0.268 emu/g, ΔH = 81 Oe, εr = 18.68 @ 1 MHz, and tanδε = 0.002. The as-prepared material has potential application in communication devices such as circulators and isolators. [ABSTRACT FROM AUTHOR]

Accurate and reliable monthly runoff forecasting plays an important role in making full use of water resources. In recent years, long short-term memory neural networks (LSTM), as a deep learning technology, has been successfully applied in forecasting monthly runoff. However, the hyperparameters of LSTM are predetermined, which has a significant influence on model performance. In this study, given that the decomposition of monthly runoff series may provide a more accurate prediction, as revealed by many previous studies, a hybrid model, namely, VMD-GWO-LSTM, is proposed for monthly runoff forecasting. The proposed hybrid model comprises two main components, namely, variational mode decomposition (VMD) coupled with the gray wolf optimizer (GWO)-based LSTM. First, VMD is utilized to decompose raw monthly runoff series into several subsequences. Second, GWO is implemented to optimize the hyperparameters of the LSTM for each subsequence on the condition that the inputs are determined. Finally, the total output of all subsequences is aggregated as the final forecast result. Four quantitative indices are employed to evaluate the model performance. The proposed model is demonstrated using 73 and 62 years of monthly runoff series data derived from the Xinfengjiang and Guangzhao Reservoirs in China's Pearl River system, respectively. To identify the feasibility and superiority of the proposed model, backpropagation neural networks (BPNN), support vector machine (SVM), LSTM, EMD-LSTM, VMD-LSTM and GWO-LSTM are also utilized for comparison. The results indicate that the proposed hybrid model can yield best forecast accuracy among these models, making it a promising new method for monthly runoff forecasting. [ABSTRACT FROM AUTHOR]

Selective laser melting (SLM) is a manufacturing technology that involves melting metal powder beds. Due to the temperature gradient during the course of forming, anisotropy is further caused by oriented grain growth. However, the influence of process parameters on anisotropy has not been studied. Two types of parameter scanning strategies and rotation angle increments were selected to alter the growth tendency of grains, thus affecting anisotropy. Comparing the STRIPES scanning strategy and the CHESS strategy, the latter can restrain anisotropy, while in the former, the anisotropy of tensile stress is influenced by the layer angle increment. The maximum tensile strength of SLM-formed parts in the formed direction deviates from an angle of 60° (1235 MPa and elongation of 5.3%), and the maximum microhardness property appears at the formed direction surface, namely, the top side surface (487 HV). [ABSTRACT FROM AUTHOR]