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Kui Zhang,1,* Yan Liu,2,* Zhenrui Zhao,1 Xuewen Shi,1 Ruihao Zhang,1 Yixiang He,1 Huaibin Zhang,1 Wenji Wang3 1The First Clinical Medical College of Lanzhou University, Lanzhou, People’s Republic of China; 2Department of Gynecology, First Affiliated Hospital of Xi ‘an Medical College, Xi’ an, People’s Republic of China; 3Department of Orthopedics, the First Hospital of Lanzhou University, Lanzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wenji Wang, The First Hospital of Lanzhou University, Lanzhou, 730000, People’s Republic of China, Tel +8613893221698, Email ldyyjzwwj@outlook.comBackground: Polyvinyl alcohol/Chitosan hydrogel is often employed as a carrier because it is non-toxic, biodegradable, and has a three-dimensional network structure. Meanwhile, Magnesium-doped nano-hydroxyapatite(Mg-nHA) demonstrated high characterization to promote the osteogenic differentiation of bone marrow derived mesenchymal stem cell(BMSCs). Therefore, in order to develop a porous hydrogel scaffold for the application of bone tissue engineering, an appropriate-type Mg-nHA hydrogel scaffold was developed and evaluated.Methods: A composite hydrogel containing magnesium-doped nano-hydroxyapatite (Mg-nHA/PVA/CS) was developed using a magnetic stirring-ion exchange method and cyclic freeze-thaw method design, with polyvinyl alcohol and chitosan as the main components. Fourier transform infrared spectra (FTIR), electron energy dispersive spectroscopy (EDS), X-ray photoelectron spectrometer (XPS) and scanning electron microscopy (SEM) were employed to analyze the chemical structure, porosity, and elemental composition of each hydrogels. The equilibrium swelling degree, moisture content, pH change, potential for biomineralization, biocompatibility, the osteogenic potential and magnesium ion release rate of the composite hydrogel were also evaluated.Results: SEM analysis revealed a well-defined 3D spatial str

The outbreak of Coronavirus Disease 2019 (COVID-19) has prompted the scientific community to explore potential treatments or vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the illness. While SARS-CoV-2 is mostly considered a respiratory pathogen, several neurological complications have been reported, raising questions about how it may enter the Central Nervous System (CNS). Receptors such as ACE2, CD147, TMPRSS2, and NRP1 have been identified in brain cells and may be involved in facilitating SARS-CoV-2 entry into the CNS. Moreover, proteins like P2X7 and Panx-1 may contribute to the pathogenesis of COVID-19. Additionally, the role of the immune system in the gravity of COVID-19 has been investigated with respect to both innate and adaptive immune responses caused by SARS-CoV-2 infection, which can lead to a cytokine storm, tissue damage, and neurological manifestations. A redox imbalance has also been linked to the pathogenesis of COVID-19, potentially causing mitochondrial dysfunction, and generating proinflammatory cytokines. This review summarizes different mechanisms of reactive oxygen species and neuro-inflammation that may contribute to the development of severe COVID-19, and recent progress in the study of immunological events and redox imbalance in neurological complications of COVID-19, and the role of bioinformatics in the study of neurological implications of COVID-19.

The widespread use of face recognition technology has given rise to privacy concerns, as many individuals are worried about the collection and utilization of their facial data. To address these concerns, researchers are actively exploring the concept of ``unlearnable examples", by adding imperceptible perturbation to data in the model training stage, which aims to prevent the model from learning discriminate features of the target face. However, current methods are inefficient and cannot guarantee transferability and robustness at the same time, causing impracticality in the real world. To remedy it, we propose a novel method called Segue: Side-information guided generative unlearnable examples. Specifically, we leverage a once-trained multiple-used model to generate the desired perturbation rather than the time-consuming gradient-based method. To improve transferability, we introduce side information such as true labels and pseudo labels, which are inherently consistent across different scenarios. For robustness enhancement, a distortion layer is integrated into the training pipeline. Extensive experiments demonstrate that the proposed Segue is much faster than previous methods (1000$\times$) and achieves transferable effectiveness across different datasets and model architectures. Furthermore, it can resist JPEG compression, adversarial training, and some standard data augmentations.

Deep 3D point cloud models are sensitive to adversarial attacks, which poses threats to safety-critical applications such as autonomous driving. Robust training and defend-by-denoising are typical strategies for defending adversarial perturbations. However, they either induce massive computational overhead or rely heavily upon specified priors, limiting generalized robustness against attacks of all kinds. To remedy it, this paper introduces a novel distortion-aware defense framework that can rebuild the pristine data distribution with a tailored intensity estimator and a diffusion model. To perform distortion-aware forward diffusion, we design a distortion estimation algorithm that is obtained by summing the distance of each point to the best-fitting plane of its local neighboring points, which is based on the observation of the local spatial properties of the adversarial point cloud. By iterative diffusion and reverse denoising, the perturbed point cloud under various distortions can be restored back to a clean distribution. This approach enables effective defense against adaptive attacks with varying noise budgets, enhancing the robustness of existing 3D deep recognition models., Comment: Accepted by ACM MM 2023

Notwithstanding the prominent performance achieved in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbations. In this paper, we delve into boosting the general robustness of point cloud recognition models and propose Point-Cloud Contrastive Adversarial Training (PointCAT). The main intuition of PointCAT is encouraging the target recognition model to narrow the decision gap between clean point clouds and corrupted point clouds. Specifically, we leverage a supervised contrastive loss to facilitate the alignment and uniformity of the hypersphere features extracted by the recognition model, and design a pair of centralizing losses with the dynamic prototype guidance to avoid these features deviating from their belonging category clusters. To provide the more challenging corrupted point clouds, we adversarially train a noise generator along with the recognition model from the scratch, instead of using gradient-based attack as the inner loop like previous adversarial training methods. Comprehensive experiments show that the proposed PointCAT outperforms the baseline methods and dramatically boosts the robustness of different point cloud recognition models, under a variety of corruptions including isotropic point noises, the LiDAR simulated noises, random point dropping and adversarial perturbations.

Pengyun Yan, Kui Zhang, Jian Cao, Ran Dong Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Ran DongDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People’s Republic of ChinaTel +86-10-64456411Fax +86-10-64442234Email dongran6618@hotmail.comBackground: The relationship between abnormal left ventricular (LV) structure and adverse outcomes has been confirmed in diverse patient groups in previous studies. However, it remains uncertain whether LV structure has predictive implications in heart failure with reduced ejection fraction (HFrEF) patients with coronary artery bypass grafting (CABG).Methods: This study retrospectively enrolled patients who had HFrEF and underwent CABG between January 2013 and July 2019. According to LV hypertrophy (LVH) and LV enlargement (LVE) assessed by echocardiography, patients were classified into four LV structure types: (-)LVH/(-)LVE, (+)LVH/(-)LVE, (-)LVH/(+)LVE, and (+)LVH/(+)LVE.Results: A total of 435 consecutive patients (mean age: 59.4 ± 9.6 years; 14.9% female) were enrolled in the present study. Examined independently, either LVH (p < 0.001) or LVE (p < 0.001) was independently associated with postoperative mortality in multivariate analysis. When LVH and LVE were analyzed in combination, the risk of mortality after CABG was lowest in (-)LVH/(-)LVE and increased with (+)LVH/(-)LVE (odds ratio [OR]: 7.525; 95% confidence interval [CI]: 1.827– 30.679, p = 0.004), (-)LVH/(+)LVE (OR: 7.253; 95% CI: 1.950– 27.185, p = 0.003), and (+)LVH/(+)LVE (OR: 9.547; 95% CI: 2.726– 34.805, p < 0.001), independent of other risk factors. Adding LV structural types to the baseline model gained an incremental effect on the predictive value for postoperative mortality (AUC: baseline model, 0.838 vs baseline model + LV structural types, 0.901, p for comparison = 0.010; category‐free net reclass

Recently, l-4-cyanotryptophan has been shown to be an efficient blue fluorescence emitter, with the potential to enable novel applications in biological spectroscopy and microscopy. However, lack of facile synthetic routes to this unnatural amino acid limits its wide use. Herein, we describe an expedient approach to synthesize Fmoc protected l-4-cyanotryptophan with high optical purity (>99%). Additionally, we test the utility of this blue fluorophore in imaging cell-membrane-bound peptides and in determining peptide-membrane binding constants.

Two new models of liquid entrainment through a vertical tee branch are proposed based on theoretical analyses and experimental data, which can well predict the test results of the prototype facility of CAP1400 and the scaled down facility of AP1000. Moreover, in order to verify the applicability of the new entrainment models, these models are implemented to RELAP5 through the safety analyses with a 2 in. cold leg SBLOCA in AP1000 comparing with the original RELAP5. The contrastive results show that the RELAP5 with proposed entrainment models has more reasonable results. In addition, the modified RELAP5 can calculate less primary coolant inventory which is more conservative. Besides, the less coolant inventory is accord with the beyond design basis accident test of APEX facility (Advanced Plant Experiment). Therefore, the proposed entrainment models are more appropriate for the ADS-4 entrainment safety analyses in large advanced PWR., QC 20200203

Cobalt catalyst has been widely used for Fischer-Tropsch (FT) Synthesis in Industry. The most common method to prepare cobalt catalyst is impregnations. Metal is deposited on porous support by contacting dry support with solution containing dissolved cobalt precursor. This step will follow by drying, calcination and reduction. The heating step used in this conventional method, however, may lead to the formation of metal silicate which is inactive site for catalysis. In this study, author explore the use of microwave to prepare catalyst compared to conventional drying method. Cobalt catalyst with SiO2 support was prepared and characterized. Particle size, surface area, and cobalt content were investigated. Crystallite size of 3-8 nm was formed which was reported to be the optimum size for cobalt catalyst in FT Synthesis. Scanning Electron Microscope (SEM) and Transmission Electron Microscopy (TEM) image revealed that microwave catalyst showed better uniformity and cobalt dispersion on silica support. Thermo-Gravimetric Analysis (TGA) study also indicated that this catalyst has good stability at Low Temperature Fischer-Tropsch Synthesis. The catalysts were then applied plasma assisted FT process over a range of power plasma (20-60W) to investigate the effect on the conversion and selectivity. The results showed that microwave catalyst exhibit lower CO conversion at 42.06% compared to conventional method at 68.32%. However, microwave catalyst is more favourable for long chain hydrocarbon selectivity.

Development of high-performance and low-cost nonprecious metal electrocatalysts is critical for eco-friendly hydrogen production through electrolysis. Herein, a novel nanoflower-like electrocatalyst comprising few-layer nitrogen-doped graphene-encapsulated nickel–copper alloy directly on a porous nitrogen-doped graphic carbon framework (denoted as Ni x Cu y @ NG-NC) is successfully synthesized using a facile and scalable method through calcinating the carbon, copper, and nickel hydroxy carbonate composite under inert atmosphere. The introduction of Cu can effectively modulate the morphologies and hydrogen evolution reaction (HER) performance. Moreover, the calcination temperature is an important factor to tune the thickness of graphene layers of the Ni x Cu y @ NG-NC composites and the associated electrocatalytic performance. Due to the collective effects including unique porous flowered architecture and the synergetic effect between the bimetallic alloy core and graphene shell, the Ni 3 Cu 1 @ NG-NC electrocatalyst obtained under optimized conditions exhibits highly efficient and ultrastable activity toward HER in harsh environments, i.e., a low overpotential of 122 mV to achieve a current density of 10 mA cm −2 with a low Tafel slope of 84.2 mV dec −1 in alkaline media, and a low overpotential of 95 mV to achieve a current density of 10 mA cm −2 with a low Tafel slope of 77.1 mV dec −1 in acidic electrolyte. © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Many natural products and medicinal drugs are heterocyclic amines possessing a chiral quaternary carbon atom in their heterocyclic ring. Herein, we report the first catalytic and asymmetric Staudinger-aza-Wittig reaction for the desymmetrization of ketones. This highly enantioselective transformation proceeds at room temperature to provide high yields-even on multigram scales-of nitrogen heterocycles featuring a chiral quaternary center. The products of this reaction are potential precursors for the synthesis of pharmaceuticals. A commercially available small P-chiral phosphine catalyst, HypPhos, induces the asymmetry and is recycled through in situ reduction of its oxide, mediated by phenylsilane in the presence of a carboxylic acid. The efficiency, selectivity, scalability, mild reaction conditions, and broad substrate scope portend that this process will expedite the syntheses of chiral heterocyclic amines of significance to chemistry, biology, and medicine.

The Fe-catalyzed Fischer-Tropsch (FT) reaction constitutes the core of the coal-to-liquids (CTL) process, which converts coal into liquid fuels. Conventional Fe-based catalysts typically convert 30% of the CO feed to CO2 in the FT unit. Decreasing the CO2 release in the FT step will reduce costs and enhance productivity of the overall process. In this context, we synthesize phase-pure ε(′)-Fe2C catalysts exhibiting low CO2 selectivity by carefully controlling the pretreatment and carburization conditions. Kinetic data reveal that liquid fuels can be obtained free from primary CO2. These catalysts displayed stable FT performance at 23 bar and 235°C for at least 150 hours. Notably, in situ characterization emphasizes the high durability of pure ε(′)-Fe2C in an industrial pilot test. These findings contribute to the development of new Fe-based FT catalysts for next-generation CTL processes.

Emergent physical properties often arise at interfaces of complex oxide heterostructures due to the interplay between various degrees of freedom, especially those with polar discontinuities. It is desirable to explore if these structures may generate pure and controllable spin currents, which are needed to attain unmatched performance and energy efficiency in the next-generation spintronic devices. Here we report the emergence of a spin-polarized two-dimensional electron gas (SP-2DEG) at the interface of two insulators, SrTiO3 and PbZr0.2Ti0.8O3. This SP-2DEG is strongly localized at the interfacial Ti atoms, due to the interplay between Coulomb interaction and band bending, and can be tuned by the ferroelectric polarization. Our findings open a door for engineering ferroelectric/insulator interfaces to create tunable ferroic orders for magnetoelectric device applications and provide opportunities for designing multiferroic materials in heterostructures.

RST/Fundamental Aspects of Materials and Energy

In this paper, generalised estimators are proposed to estimate seasonal indices for certain forms of additive and mixed seasonality. The estimators combine one of two group seasonal indices methods—Dalhart's group method and Withycombe's group method—with a shrinkage method in different ways. Optimal shrinkage parameters are derived to maximise the performance of the estimators. Then, the generalised estimators, with the optimal shrinkage parameters, are evaluated based on forecasting accuracy. Moreover, the effects of three factors are examined, namely, the length of data history, variance of random components and the number of series. Finally, a simulation experiment is conducted to support the evaluation

In social sciences, there is currently rare consensus on the underlying mechanism for cultural evolution, partially due to lack of suitable data. The evolution of first names of newborn babies offers a remarkable example for such researches. In this paper, we employ the historical data on baby names from the United States to investigate the evolutionary process of culture, in particular focusing on how inequality among baby names changes over time. Then we propose a stochastic model where individual choice is determined by both individual preference and social influence, and show that the decrease in the strength of social influence can account for all the observed empirical features. Therefore, we claim that the weakening of social influence drives cultural evolution.

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm based on the coupled social networks, which considers the effects of both social influence and personalized preference. Experimental results on two real datasets, \emph{Epinions} and \emph{Friendfeed}, show that hybrid pattern can not only provide more accurate recommendations, but also can enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding structure and function of coupled social networks.

Background Drinking alcohol has a long tradition in Chinese culture. However, data on the prevalence and patterns of alcohol consumption in China, and its main correlates, are limited.

In this paper, we focus on critical event monitoring in wireless sensor networks (WSNs), where only a small number of packets need to be transmitted most of the time. When a critical event occurs, an alarm message should be broadcast to the entire network as soon as possible. To prolong the network lifetime, some sleep scheduling methods are always employed in WSNs, resulting in significant broadcasting delay, especially in large scale WSNs. In this paper, we propose a novel sleep scheduling method to reduce the delay of alarm broadcasting from any sensor node in WSNs. Specifically, we design two determined traffic paths for the transmission of alarm message, and level-by-level offset based wake-up pattern according to the paths, respectively. When a critical event occurs, an alarm is quickly transmitted along one of the traffic paths to a center node, and then it is immediately broadcast by the center node along another path without collision. Therefore, two of the big contributions are that the broadcasting delay is independent of the density of nodes and its energy consumption is ultra low. Exactly, the upper bound of the broadcasting delay is only 3D+2L, where D is the maximum hop of nodes to the center node, L is the length of sleeping duty cycle, and the unit is the size of time slot. Extensive simulations are conducted to evaluate these notable performances of the proposed method compared with existing works. © 2006 IEEE.

In this paper, we propose a novel distributed navigation algorithm for people to escape from critical event region in wireless sensor networks (WSNs). Unlike existing works, the scenario discussed in the paper has no goal or exit as guidance, leading to a big challenge for the navigation problem. To solve it, our proposed navigation algorithm computes the convex hull of the event region just by some topological methods. With the reference of the convex hull, people can be easily navigated out of the event region. Both the computation complexity and communication overhead of the proposed algorithm are very low, as it only needs to flood two shortest path trees in a limited area around the event region with a distance ⌊L/2π⌋ + 1 to the event boundary, where L is the length of the boundary. Conducted simulations have verified the effectiveness and scalability of the proposed algorithm. © 2011 IEEE.

Differential radar interferometry (DInSAR) has demonstrated its ability to monitor ground deformation. DInSAR uses two synthetic aperture radar (SAR) images acquired over the same region to generate a differential interferogram. A continuous ground displacement map with large coverage can be extracted from a high quality differential interferogram, which makes DInSAR extremely competitive compared to traditional ground survey techniques. DInSAR has also been recently extended to monitor the temporal evolution of ground deformation through the use of advanced DInSAR techniques. Such techniques make use of stacked differential interferograms to generate accurate deformation time series. Nowadays, in order to achieve higher resolution and wider spatial coverage, large sized SAR datasets are increasingly used in DInSAR applications. Unfortunately the significantly increased size of the datasets causes many difficulties in DInSAR processing. In this dissertation a series of algorithms have been developed in order to address the DInSAR processing problems caused by large data file sizes. To make large dataset processing more automated, an improved DEM coregistration strategy has been designed. Compared to the conventional automation method, it has an improved efficiency and a higher accuracy, permitting large datasets to be processed more smoothly. To break the computational bottleneck of large dataset processing, a two-stage optimisation (TSO) phase unwrapping algorithm has been developed. The TSO algorithm resolves the block partition and parallelisation problems in phase unwrapping. To improve the uniformity of multi-track differential interferograms, an orbit error compensation approach has been proposed. It enables the fringe pattern inconsistency induced by orbit error to be eliminated, resulting in a better interpretation of multi-track DInSAR observations. To reduce the required storage resources in deformation time series analysis for large datasets, a new advanc

In the paper, we design a navigation system based on sensor network to guide a robot to walk out of event region. The navigation system does not require any exit or locations.

It is believed that epistatic interactions among loci contribute to variations in quantitative traits. Several methods are available to detect epistasis using population-based data. However, methods to characterize epistasis for quantitative traits in family-based association analysis are not well developed, especially for studying thousands of gene expression traits. Here, we proposed a linear mixed-model approach to detect epistasis for quantitative traits using family data. The proposed method was implemented in a widely used software program SOLAR. We evaluated the power of the method by simulation studies and applied this method to the analysis of the Centre d'Etude du Polymorphisme Humain family gene expression data provided by Genetics Analysis Workshop 15 (GAW15).

The radioactivity levels of U and Th were investigated in ion adsorption rare earth carbonate concentrates. Based on the law covering the distribution and movement of U and Th in the production of the ion adsorption concentrate, the pre-extraction and separation of U and Th from the rare earth leach liquor using naphthenic acid were studied. U and Th extraction rates of 98.6% and 33.3%, respectively, were obtained from a solution containing 1.75 mol/l rare earth oxides using 15 vol% naphthenic acid and 15 vol% isooctyl alcohol in sulphonated kerosene as extractant at pH 1.0-3.5 in a single-stage extraction process with an organic/aqueous ratio of 2:1 for 10 minutes and the addition of 0.6 mol/l NaOH solution at 6 ml/minute. The U and Th in the loaded organic phase could be completely stripped using 5 mol/l HCl solution in a three-stage countercurrent stripping process with an organic/aqueous ratio of 20:1., The radioactivity levels of U and Th were investigated in ion adsorption rare earth carbonate concentrates. Based on the law covering the distribution and movement of U and Th in the production of the ion adsorption concentrate, the pre-extraction and separation of U and Th from the rare earth leach liquor using naphthenic acid were studied. U and Th extraction rates of 98.6% and 33.3%, respectively, were obtained from a solution containing 1.75 mol/l rare earth oxides using 15 vol% naphthenic acid and 15 vol% isooctyl alcohol in sulphonated kerosene as extractant at pH 1.0-3.5 in a single-stage extraction process with an organic/aqueous ratio of 2:1 for 10 minutes and the addition of 0.6 mol/l NaOH solution at 6 ml/minute. The U and Th in the loaded organic phase could be completely stripped using 5 mol/l HCl solution in a three-stage countercurrent stripping process with an organic/aqueous ratio of 20:1.

Investigations were carried out into the phase transformations and reaction mechanism during the roasting of ammonium jarosite residue containing 6.10% Zn, 2.88% Pb, 26.4% Fe, 0.0194% Ag and 0.0192% In. The optimum conditions were a temperature of 650 degrees C and a roasting tine of 2 h. The ammonium jarosite was decomposed to form Fe2O3, ZnSO4 and In2(SO4)3. The roasted residue was leached in water at room temperature for 0.5 h, and the leaching rates of Zn and In were 72.02% and 78.73%, respectively. The Fe leaching rate was only 4.42%, and Ag and Pb were enriched in the leach residue., Investigations were carried out into the phase transformations and reaction mechanism during the roasting of ammonium jarosite residue containing 6.10% Zn, 2.88% Pb, 26.4% Fe, 0.0194% Ag and 0.0192% In. The optimum conditions were a temperature of 650 degrees C and a roasting tine of 2 h. The ammonium jarosite was decomposed to form Fe2O3, ZnSO4 and In2(SO4)3. The roasted residue was leached in water at room temperature for 0.5 h, and the leaching rates of Zn and In were 72.02% and 78.73%, respectively. The Fe leaching rate was only 4.42%, and Ag and Pb were enriched in the leach residue.

The electrolysis of Eu3+ solution using an ion exchange membrane was studied to achieve the reduction of Eu3+. Effects of electrode type (anode and cathode), electrolyte composition (anode and cathode electrolyte), acidity and electrolytic process parameters on the reduction of Eu3+ were investigated. The optimum conditions were determined. Results show that a ruthenium based multi-component mixture coated the titanium anode and Ti cathode; a calcium chloride solution containing 1 mol/l HCl anolyte, a rich europium compound catholyte, a current of 500 A/m2 and electrolysis time of 7.5 h. The best result for the Eu3+ reduction rate could be as high as 95.5% and the current efficiency is more than 80% in this work, below the premise of stable cell voltage and with no loss of europium. Moreover, the reduction rate of Eu3+ is more than 94% in a continuous industrial operation., The electrolysis of Eu3+ solution using an ion exchange membrane was studied to achieve the reduction of Eu3+. Effects of electrode type (anode and cathode), electrolyte composition (anode and cathode electrolyte), acidity and electrolytic process parameters on the reduction of Eu3+ were investigated. The optimum conditions were determined. Results show that a ruthenium based multi-component mixture coated the titanium anode and Ti cathode; a calcium chloride solution containing 1 mol/l HCl anolyte, a rich europium compound catholyte, a current of 500 A/m2 and electrolysis time of 7.5 h. The best result for the Eu3+ reduction rate could be as high as 95.5% and the current efficiency is more than 80% in this work, below the premise of stable cell voltage and with no loss of europium. Moreover, the reduction rate of Eu3+ is more than 94% in a continuous industrial operation.

The influence of feed acidity, extractant P507 concentration, reaction time, phase ratio and temperature on the extraction and stripping of gallium was investigated. The results indicated that, under the optimum conditions of 15 vol% P507 plus kerosene in an O/A ratio of 1:4, the extraction rate of gallium was 98.56% after a 3-stage counter-current extraction. Gallium in the loaded organic phase was stripped using 60 g/l sulphuric acid solution, the stripping rate reaching 98.02% after a 5-stage counter-current stripping with an O/A ratio of 5:1. For the overall process, gallium was found to have undergone an almost 20-fold enrichment., The influence of feed acidity, extractant P507 concentration, reaction time, phase ratio and temperature on the extraction and stripping of gallium was investigated. The results indicated that, under the optimum conditions of 15 vol% P507 plus kerosene in an O/A ratio of 1:4, the extraction rate of gallium was 98.56% after a 3-stage counter-current extraction. Gallium in the loaded organic phase was stripped using 60 g/l sulphuric acid solution, the stripping rate reaching 98.02% after a 5-stage counter-current stripping with an O/A ratio of 5:1. For the overall process, gallium was found to have undergone an almost 20-fold enrichment.

The influence of feed acidity, extractant HBL121 concentration and extraction temperature, time and phase ratio on the extraction and the effect of sulphuric acid concentration and stripping temperature, time and phase ratio on the stripping of gallium were studied. Extraction and stripping isotherms and extractant transformation conditions were determined. The results indicated that, using 40% HBL121 (mass fraction) and 20% (volume fraction) in sulphonated kerosene as extractant, the gallium extraction rate from the solution with 108.67 g/l sulphuric acid reached 98.14% in four-stage countercurrent extraction with an O/A ratio of 1:1 at 25 degrees C for 10 min. Selective stripping of gallium in the loaded organic phase was achieved using 200 g/l sulphuric acid solution when the stripping rate reached 99.18% in five-stage countercurrent stripping with an O/A ratio of 4:1 at 25 degrees C for 8 min. Following the stripping of gallium, the iron in the loaded organic phase was stripped using 7 mol/l hydrochloric acid solution when the stripping rate reached 99.23% in three-stage countercurrent stripping with an O/A ratio of 1.5:1 at 25 degrees C for 2 min., The influence of feed acidity, extractant HBL121 concentration and extraction temperature, time and phase ratio on the extraction and the effect of sulphuric acid concentration and stripping temperature, time and phase ratio on the stripping of gallium were studied. Extraction and stripping isotherms and extractant transformation conditions were determined. The results indicated that, using 40% HBL121 (mass fraction) and 20% (volume fraction) in sulphonated kerosene as extractant, the gallium extraction rate from the solution with 108.67 g/l sulphuric acid reached 98.14% in four-stage countercurrent extraction with an O/A ratio of 1:1 at 25 degrees C for 10 min. Selective stripping of gallium in the loaded organic phase was achieved using 200 g/l sulphuric acid solution when the stripping rate reached 99.18% in five-stage countercurrent stripping with an O/A ratio of 4:1 at 25 degrees C for 8 min. Following the stripping of gallium, the iron in the loaded organic phase was stripped using 7 mol/l hydrochloric acid solution when the stripping rate reached 99.23% in three-stage countercurrent stripping with an O/A ratio of 1.5:1 at 25 degrees C for 2 min.

Feature selection method has become the focus of research in the area of engineering data processing where there exists a large amount of high-dimensional data from the high-frequency acquisition system. For high-dimensional data processing, engineers often resort to feature extraction methods and statistical theories to convert the original features into new features. However, the converted data always lose the engineering meaning of the original features and the choice and use of conversion methods are challenging. In this paper, a hybrid feature selection model is presented to select the most significant input features from all potentially relevant features. The algorithm combines a filter model with a wrapper model. In the filter model, four variable ranking methods are used to pre-rank the candidate features. These four methods including Pearson correlation coefficient, relief algorithm, Fisher score and class separability, measure features from various angles, which leads to different ranking results. Therefore, a weighted voting scheme is introduced to re-rank features based on the degree of significance of the four methods on the classification error rate of radial basis function (RBF) classifier. In wrapper model, a binary search (BS) method and a sequential backward search (SBS) method are utilized to minimize the number of relevant features when promising to keep the classification error rate of RBF classifier below a given threshold. To demonstrate the potential of applying the method to large-scale engineering data processing, a case study is conducted.

In social sciences, there is currently rare consensus on the underlying mechanism for cultural evolution, partially due to lack of suitable data. The evolution of first names of newborn babies offers a remarkable example for such researches. In this paper, we employ the historical data on baby names from the United States to investigate the evolutionary process of culture, in particular focusing on how inequality among baby names changes over time. Then we propose a stochastic model where individual choice is determined by both individual preference and social influence, and show that the decrease in the strength of social influence can account for all the observed empirical features. Therefore, we claim that the weakening of social influence drives cultural evolution.