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The Kukaazi Pb-Zn-Cu-W polymetallic deposit, located in the Western Kunlun orogenic belt, is a newly discovered skarn-type deposit. Ore bodies mainly occur in the forms of lenses and veins along beddings of the Mesoproterozoic metamorphic rocks. Three ore blocks, KI, KII, and KIII, have been outlined in different parts of the Kukaazi deposit in terms of mineral assemblages. The KI ore block is mainly composed of chalcopyrite, scheelite, pyrrhotite, sphalerite, galena and minor pyrite, arsenopyrite, and molybdenite, whereas the other two ore blocks are made up of galena, sphalerite, magnetite and minor arsenopyrite and pyrite. In this study, we obtained a molybdenite isochron Re-Os age of 450.5 ± 6.4 Ma (2σ, MSWD = 0.057) and a scheelite Sm-Nd isochron age of 426 ± 59 Ma (2σ, MSWD = 0.49) for the KI ore block. They are broadly comparable to the ages of granitoid in the region. Scheelite grains from the KI ore block contain high abundances of rare earth elements (REE, 42.0-95.7 ppm) and are enriched in light REE compared to heavy REE, with negative Eu anomalies (δEu = 0.13-0.55). They display similar REE patterns and Sm/Nd ratios to those of the coeval granitoids in the region. Moreover, they also have similar Sr and Nd isotopes [Sr/Sr = 0.7107-0.7118; ε(t) = −4.1 to −4.0] to those of such granitoids, implying that the tungsten-bearing fluids in the Kukaazi deposit probably originate from the granitic magmas. Our results first defined that the Early Paleozoic granitoids could lead to economic Mo-W-(Cu) mineralization at some favorable districts in the Western Kunlun orogenic belt and could be prospecting exploration targets. [ABSTRACT FROM AUTHOR]

ABSTRACT Hyperlipidemia is one kind of common and frequently occurring syndromes and closely related to cardiovascular and cerebrovascular disease occurrences in the world. It is significant to explore the marine foods and potential drug resources for the application in treating the hyperlipidemia. We conducted this study to investigate the reducing effect and mechanism of kelp ( Saccharina japonica) on serum lipid of rat models with hyperlipidemia induced by a fat-rich diet. The levels of serum lipid including the triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) were detected by biochemical assay. Enzyme-linked immunosorbent assay was applied to determine the level of oxidized LDL (ox-LDL). The activity of lipoprotein lipase (LPL) and hepatic lipase (HL) was determined by chemical colorimetry. The concentrations of malondialdehyde (MDA) and nitric oxide (NO) were respectively measured by thiobarbituric acid assay and nitrate reductase assay. The activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX) were respectively determined by xanthinoxidase assay and chemical colorimetry. The results showed that the serum levels of TG, TC and LDL decreased, while HDL increased significantly in treated group compared with those of model groups ( P < 0.05). The value of ox-LDL of treated groups was much lower than that of model groups ( P < 0.05). The activities of LPL and HL in serum and hepatic tissue in treated groups were significantly higher than those of model groups ( P < 0.05). The levels of MDA and NO in serum and hepatic tissue were lower than those of model groups ( P < 0.05), while the activities of SOD and GSH-PX were significantly higher than those of model groups ( P < 0.05). Our study indicated that kelp may correlate with the metabolism of TG, TC, LDL and HDL by enhancing the activities of LPL and HL, and regulate the levels of serum lipids by increasing the activities of SOD and GSH-PX to reduce the levels of MDA and NO. PRACTICAL APPLICATIONS The objective of this work is to explore the potential application of kelp for treating hyperlipidemia. It is showed that the kelp could regulate to the normal level of triglyceride, total cholesterol, low-density lipoprotein and high-density lipoprotein in the models. It also could enhance the activity of the lipoprotein lipase and hepatic lipase to change the oxidative stress. [ABSTRACT FROM AUTHOR]

This paper is concerned with an initial value problem of the compressible full Hall-MHD equations in three-dimensional whole space. Both the global existence and the optimal decay rates of solutions are obtained, when the smooth initial data are sufficiently close to the non-vacuum equilibrium in H 1 . As a by-product of the uniform estimates, the vanishing limit of Hall coefficient is also justified. [ABSTRACT FROM AUTHOR]

Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO 2 content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions. [Display omitted] • Methanation reaction conditions were predictively modeled by machine learning. • ELM-STASA optimization improved the stability and accuracy of model prediction. • Very small error was obtained between predicted and experimental results. • Machine learning showed advantages with saving time, labor and material resources. [ABSTRACT FROM AUTHOR]

With the increasing proportion of solar grid-connected, the establishment of an accurate photovoltaic (PV) power prediction model is very important for safe operation and efficient dispatching of a power grid. Considering the multi-level periodicity of PV power caused by many factors, such as seasons and weather, a short-term PV power prediction model based on transfer component analysis is designed by introducing the idea of transfer learning. In order to measure the uncertainty of numerical weather prediction (NWP) and power sequence, a novel algorithm considering weather similarity and power trend similarity is proposed. First, the intrinsic trend is measured by extracting permutation entropy, variance, and mean from the historical PV power sequence. Second, weighting of NWP is accomplished based on the Pearson correlation coefficient. PV power data are divided into different clusters by K-medoids clustering. At the same time, the transfer component analysis alleviates the time-varying problem of data distribution caused by multi-level time periodicity and effectively improves the prediction accuracy of the model. Finally, simulation experiments are carried out on the PV power output dataset (PVOD). The results show that the prediction accuracy of the proposed method is better than the traditional methods, and the accuracy and applicability of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Sm-Nd analyses of seven scheelite samples from scheelite-quartz veins in the Honghuaerji scheelite deposit produce a well-defined linear array on an isochron diagram with a mean square weighted deviation (MSWD) of 0.87 corresponding to an age of 178.4 ± 2.9 Ma with εNd(t) = + 1.50. This age is interpreted to represent the age of scheelite mineralization. The scheelite Sm-Nd age is in good agreement with U–Pb ages obtained from a mineralization-related granite (179.4 ~ 178.6 Ma), indicating that there is no apparent age gap between granite crystallization and ore formation. Rare earth element (REE) abundances in the scheelite were determined by in situ laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and the scheelite samples contain elevated REE concentrations with total ΣREE + Y contents in the range of 3339 to 6321 ppm. The chondrite-normalized REE distribution patterns of all scheelites are middle REE (MREE)-enriched, with strong negative Eu-anomalies (Eu/Eu* = 0.09 ~ 0.23). The REE characteristics of the Honghuaerji scheelite suggest that REE 3 + substituted into the Ca site along with Na and Nb (dominated by Na), whereas Eu is predominantly present as Eu 2 + in the scheelite and may have crystallized from relatively reduced fluids. All sulfur isotope data (δ 34 S) for sulfide separates range from + 2.0 to + 3.8‰, with an average of 3.2‰. Measured 206 Pb/ 204 Pb, 207 Pb/ 204 Pb, and 208 Pb/ 204 Pb ratios vary from 18.243 to 18.451, 15.494 to 15.574, and 37.933 to 38.340, respectively. On the basis of common Sr-Nd-Pb-Hf isotopic characteristics between the scheelite and the host granite and the positive initial Nd isotope ratios (+ 1.46 − + 1.52), low 87 Sr/ 86 Sr ratios (0.704983–0.705297) in the scheelites, it is inferred that the hydrothermal fluids responsible for tungsten mineralization at Honghuaerji were of magmatic origin with a mantle-derived signature. Based on the regional geology, tectonic evolution, and geochemical evidence, the Honghuaerji granite is thought to have been derived from the partial melting of thickened lower crust produced during post-collisional orogenic events associated with the closure of the Mongol-Okhotsk Ocean. [ABSTRACT FROM AUTHOR]

Background: Although immunotherapy has shown clinical activity in lung adenocarcinoma (LUAD), LUAD prognosis has been a perplexing problem. We aimed to construct an immune-related lncRNA pairs (IRLPs) score for LUAD and identify what immunosuppressor are appropriate for which group of people with LUAD.Methods: Based on The Cancer Genome Atlas (TCGA)-LUAD cohort, IRLPs were identified to construct an IRLPs scoring system by Cox regression and validated in the Gene Expression Omnibus (GEO) dataset using log-rank test and the receiver operating characteristic curve (ROC). Next, we used spearman's correlation analysis, t-test, signaling pathways analysis and gene mutation analysis to explore immune and molecular characteristics in different IRLP subgroups. The "pRRophetic" package was used to predict the sensitivity of immunosuppressant.Results: The IRLPs score was constructed based on eight IRLPs calculated as 2.12 × (MIR31HG|RRN3P2) + 0.43 × (NKX2-1-AS1|AC083949.1) + 1.79 × (TMPO-AS1|LPP-AS2) + 1.60 × (TMPO-AS1|MGC32805) + 1.79 × (TMPO-AS1|PINK1-AS) + 0.65 × (SH3BP5-AS1|LINC01137) + 0.51 × (LINC01004|SH3PXD2A-AS1) + 0.62 × (LINC00339|AGAP2-AS1). Patients with a lower IRLPs risk score had a better overall survival (OS) (Log-rank test P TCGA train dataset < 0.001, P TCGA test dataset = 0.017, P GEO dataset = 0.027) and similar results were observed in the AUCs of TCGA dataset and GEO dataset (AUC TCGA train dataset = 0.777, AUC TCGA test dataset = 0.685, AUC TCGA total dataset = 0.733, AUC GEO dataset = 0.680). Immune score (Cor = -0.18893, P < 0.001), stoma score (Cor = -0.24804, P < 0.001), and microenvironment score (Cor = -0.22338, P < 0.001) were significantly decreased in the patients with the higher IRLP risk score. The gene set enrichment analysis found that high-risk group enriched in molecular changes in DNA and chromosomes signaling pathways, and in this group the tumor mutation burden (TMB) was higher than in the low-risk group (P = 0.0015). Immunosuppressor methotrexate sensitivity was higher in the high-risk group (P = 0.0052), whereas parthenolide (P < 0.001) and rapamycin (P = 0.013) sensitivity were lower in the high-risk group.Conclusions: Our study established an IRLPs scoring system as a biomarker to help in the prognosis, the identification of molecular and immune characteristics, and the patient-tailored selection of the most suitable immunosuppressor for LUAD therapy. [ABSTRACT FROM AUTHOR]

In this study, we consider the multiple traveling salesman problem (MTSP) with multiple depots, closed paths, and a constraint on the number of cities visited by each traveling salesman. In our previous study, we proposed a state transition simulated annealing algorithm (STASA) that employs the Metropolitan criterion for simulated annealing to solve the traveling salesman problem (TSP). The results obtained were significantly improved according to our previous experiments. In the present study, we propose a new intelligent operator to solve the MTSP called an elementary breakpoint operator, which can generate the solution space for MTSP through elementary matrix operations. A neighborhood search structure called 2opt, which can improve the quality of the solution by connecting two shorter non-adjacent arcs, is then introduced into STASA to enhance the ability to capture the global optimal solution. Finally, comparative experiments were conducted based on a wide range of TSPLIB benchmark instances using our proposed algorithm and other algorithms, and the results demonstrated the superior performance of our proposed approach compared with all other state-of-the-art approaches for MTSP. [ABSTRACT FROM AUTHOR]

Point cloud registration precision degrades when the scene contains symmetric structures or repeated patches. Also, the common operation of continuous downsampling in existing registration methods causes the loss of detailed information and leads to further registration precision degradation. In this paper, we propose a high precision point cloud registration network (HPNet) to learn discriminative features by considering both multi-scale information and position information. We first propose a multi-scale feature extraction module similar to the feature pyramid that allows the extracted features to contain multi-scale information. Then, an information interaction is performed on the features to learn global contextual information by using Transformer with a hybrid position encoding, which takes into account both absolute and relative positions of points. Finally, the features obtained from the information interaction module are directly used to predict the point correspondences. Comprehensive experiments on 3DMatch, 3DLoMatch, ModelNet, and ModelLoNet datasets demonstrate the state-of-the-art performance of the proposed method and the implementation of HPNet in SLAM shows its effectiveness in real application. [Display omitted] • Point cloud registration loses detail information during continuous downsampling. • Feature fusion of low-level detail information and high-level semantic information. • Hybrid position encoding consider both absolute and relative positions. positions. • Point cloud registration algorithms applied to SLAM. [ABSTRACT FROM AUTHOR]

[Display omitted] • A series of hydrostyryl pyridinium derivatives with dual-state emission behaviors were employed for synthesis of fluorescent polymers resulting unique fluorescent behaviors. • Significant solvent- and excitation-dependent fluorescent emissions were observed and their multichromatic behaviors caused by enol-to-keto transition of phenol group and aggregation were addressed by using quantum computation. To synthesize fluorescent polymers, hydrostyryl pyridinium derivatives were methacrylated and afforded monomers of MA-SP-OH-X (X = NO 2 , F, substituted group on phenyl ring) followed by free-radically homo- and copolymerized with methyl methacrylate (MMA), butyl acrylate (BA) and styrene (St), respectively. The resulting copolymers showed green, orange and red emissions with significant multichromatic behaviors and solvent- and excitation-dependence. Moreover, P(BA- co -MA-SP-OH-NO 2) behaved as a thermal elastomer and showed high stretchability and self-heal performance. In addition, with the copolymers as macro-additives, fluorescent poly(vinylidene difluoride) (PVDF) films were fabricated by using electrospinning technique. The unexpected multichromatic fluorescent behaviors showed by the copolymers were attributed to the enol-to-keto transition of phenol group in SP-OH moiety and significant aggregation of fluorophore moieties. [ABSTRACT FROM AUTHOR]

The moth search algorithm (MS) is a novel intelligent optimization algorithm based on moth population behavior, which can solve many problems in different fields. However, the algorithm is easy to fall into local optimization when solving complex optimization problems. This study develops a new hybrid moth search-fireworks algorithm (MSFWA) to solve numerical and constrained engineering optimization problems. The explosion and mutation operators from the fireworks algorithm are introduced into the MS, which not only preserves the advantages of fast convergence and strong exploitation capability of the algorithm, but also significantly enhances the exploration capability. The performance of the MSFWA is tested using 23 benchmark functions. The hybrid algorithm is superior to other highly advanced metaheuristic algorithms for most benchmark functions, demonstrating the characteristics of fast convergence and high stability. Finally, the ability of the MSFWA to solve practical constrained problems is evaluated on six well-known engineering application problems. Compared with other optimization algorithms, the MSFWA is very competitive in its solution of these complex and constrained practical problems. [ABSTRACT FROM AUTHOR]

In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of similarity measurement. CMSM obtains the internal information of time series from the general perspective and local trend using the cloud model, which reduces the uncertainty of measurement. The neighbor set is selected from time series by CMSM and used to construct a prediction model based on least squares support vector machine. The proposed technique reduces the potential for overfitting and uncertainty and improves model prediction quality and generalization. Experiments were performed with four datasets selected from Time Series Data Library. The experimental results show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract A cognitive modelling based new inversion method, the successive differential evolution (DE-S) algorithm, is proposed to estimate the Q factor and velocity from the zero-offset vertical seismic profile (VSP) record for oil-gas reservoir exploration. The DE algorithm seeks optimal solutions by simulating the natural species evolution processes and makes the individuals become optimal. This algorithm is suitable for the high-dimensional nonseparable model space where the inversion leads to recognition and prediction of hydrocarbon reservoirs. The viscoelastic medium is split into layers whose thicknesses equal to the space between two successive VSP geophones, and the estimated parameters of each layer span the related subspace. All estimated parameters span to a high dimensional nonseparable model space. We develop bottom-up workflow, in which the Q factor and the velocity are estimated using the DE algorithm layer by layer. In order to improve the inversion precision, the crossover strategy is discarded and we derive the weighted mutation strategy. Additionally, two kinds of stopping criteria for effective iteration are proposed to speed up the computation. The new method has fast speed, good convergence and is no longer dependent on the initial values of model parameters. Experimental results on both synthetic and real zero-offset VSP data indicate that this method is noise robust and has great potential to derive reliable seismic attenuation and velocity, which is an important diagnostic tool for reservoir characterization. Highlights • We propose a heuristic optimization method to estimate Q and velocity in an up-bottom and bottom-up workflow. • We use both direct downgoing and primarily reflected upgoing wave of zero-offset VSP data to inverse. • A weighting factor as mutation strategy to improve inversion precision & 2 stopping criteria for efficiency/flexibility. • Independent on initial values and good feasibility for nonseparable model space, fast computation and good convergence. • The proposed method can eliminate the influence of upgoing wave at the interfaces even under certain degree of noise. [ABSTRACT FROM AUTHOR]

Abstract Convolutional neural networks (CNNs) have outperformed most state-of-the-art methods in object detection. However, CNNs suffer the difficulty of detecting objects with rotation, because the dataset used to train the CCNs often does not contain sufficient samples with various angles of orientation. In this paper, we propose a novel data-augmentation approach to handle samples with rotation, which utilizes the distribution of the object's orientation without the time-consuming process of rotating the sample images. Firstly, we present an orientation descriptor, named as "principal-axis orientation", to describe the orientation of the object's principal axis in an image and estimate the distribution of objects' principal-axis orientations (PODs) of the whole dataset. Secondly, we define a similarity metric to calculate the POD similarity between the training set and an additional dataset, which is built by randomly selecting images from the benchmark ImageNet ILSVRC2012 dataset. Finally, we optimize a cost function to obtain an optimal rotation angle, which indicates the highest POD similarity between the two aforementioned data sets. In order to evaluate our data augmentation method for object detection, experiments, conducted on the benchmark PASCAL VOC2007 dataset, show that with the training set augmented using our method, the average precision (AP) of the Faster RCNN in the TV-monitor is improved by 7.5%. In addition, our experimental results also demonstrate that new samples generated by random rotation are more likely to result in poor performance of object detection. [ABSTRACT FROM AUTHOR]

Introduction: This study explores the influence of artificial intelligence (A.I.) applications on the job performance of healthcare providers, based on data from standardised-trained residents in the First People's Hospital of Yunnan Province in China., Methods: The ordinary least squares model is employed to examine the relationship between A.I. applications and job performance. To address potential endogeneity and missing variables, we utilise the propensity score matching method and alternative regression models., Results: The findings indicate that the job performance of standardised-trained residents positively correlates with A.I. applications. This relationship remains robust after addressing endogenous and missing variables. Further discussion reveals that patients' support mediates the relationship between A.I. and job performance. Under identical conditions, the job performance of female residents empowered by A.I. is found to be significantly better than that of their male counterparts. Conversely, no heterogeneity is observed regarding the impact of A.I. on the job performance of medical practitioners and clinical medical technicians., Discussion: This study underscores the positive role of A.I. applications in enhancing the job performance of standardised-trained residents. The results highlight the mediating role of patient support and suggest gender-based differences in the efficacy of A.I. empowerment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Jin and Xu.)

Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (http://www.ncrdb.cn), a meticulously curated repository housing 1144 experimentally validated non-coding RNAs (ncRNAs) intricately linked with sepsis. ncRS offers comprehensive RNA data, exhaustive experimental insights, and integrated annotations from diverse databases. This resource empowers researchers and clinicians to decipher ncRNAs' roles in sepsis pathogenesis, potentially identifying vital biomarkers for early diagnosis and prognosis, thus facilitating personalized treatments., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work., (Copyright © 2024 Elsevier Ltd. All rights reserved.)