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Soil constitutive model parameters can be identified from triaxial test data. The identification is frequently performed by fitting a constitutive model to triaxial test data from a purely statistical or probabilistic perspective based on an assumption that measurements are independent. This ignores the sequential attribute of triaxial test data and is, hence, not realistic. In this paper, a probabilistic state space model (SSM) is proposed for undrained triaxial test data analysis, with which the sequential data attribute is explicitly considered and a constitutive model (physics) is linked to the SSM model (statistics) in a natural way. Then, constitutive model parameters can be rigorously learned under a Bayesian framework based on the SSM without artificially augmenting them into hidden variables of SSM. Without loss of generality, the modified Cam Clay (MCC) model is taken as an example to develop the SSM and to demonstrate the proposed Bayesian framework, which is illustrated using simulated and real-life data. Results from the proposed Bayesian framework based on the SSM include not only the best estimates of MCC model parameters but also their posterior distributions for quantifying the identification uncertainty, based on which the identifiability of MCC model parameters is discussed and highlighted.

Purpose In recent decades, customer experiences (CXs) have gained considerable attention from both academics and service industries. However, the role of digital technologies in CXs is largely ignored. This study aims to explore the manners of digital experience (DX) and to leverage these manners for service design. Design/methodology/approach A total of 2,178 journal articles published as of January 2022 were collected from nine high-impact service journals. Social network analysis and cluster analysis were used to analyze 5,289 keywords and 22,645 edges. Findings Four DX manners (utility, sociability, informativeness and arousal) are identified from the keywords of the qualifying articles. Originality/value A digital experience service model is developed based on the concept of CX and DX manners. Academic and managerial contributions are discussed.

Although demonstrating great success, previous multi-view unsupervised feature selection (MV-UFS) methods often construct a view-specific similarity graph and characterize the local structure of data within each single view. In such a way, the cross-view information could be ignored. In addition, they usually assume that different feature views are projected from a latent feature space while the diversity of different views cannot be fully captured. In this work, we resent a MV-UFS model via cross-view local structure preserved diversity and consensus learning, referred to as CvLP-DCL briefly. In order to exploit both the shared and distinguishing information across different views, we project each view into a label space, which consists of a consensus part and a view-specific part. Therefore, we regularize the fact that different views represent same samples. Meanwhile, a cross-view similarity graph learning term with matrix-induced regularization is embedded to preserve the local structure of data in the label space. By imposing the $l_{2,1}$ -norm on the feature projection matrices for constraining row sparsity, discriminative features can be selected from different views. An efficient algorithm is designed to solve the resultant optimization problem and extensive experiments on six publicly datasets are conducted to validate the effectiveness of the proposed CvLP-DCL.

Acetone is a solvent used in many laboratories and factories. Serious problems will occur when it is exposed to the environment. Therefore, a new design hydrophobicity bimetallic metal material (10-Fe1Mn1-USY) was prepared for acetone adsorption under conditions of high humidity. Hydrophobic Y type zeolite was used to prepare bimetallic metal materials and the effect of different operating parameters (including different material, humidity, temperature, pollutant concentration, residence time, and regeneration) on the efficiency of acetone treatment was examined. Isothermal adsorption model, kinetics and thermodynamic model analysis were also used to establish the reaction mechanism. The 10-Fe1Mn1-USY material has good adsorption capacity (133 mg g−1) for acetone under a relative humidity of 50%. The main factors affecting the adsorption capacity are the contact angle, hydrophobicity, specific surface area, and Si/Al of the material. The isothermal adsorption and the kinetic adsorption behavior of 10-Fe1Mn1-USY material for acetone are more suitable for the Temkin isotherm adsorption model and the pseudo-first-order kinetic model. The adsorption of acetone by 10-Fe1Mn1-USY material is dominated by intra-particle diffusion. According to the thermodynamic analysis results, the adsorption behavior of 10-Fe1Mn1-USY material for acetone is a type of physical adsorption, and a spontaneous and non-sequential reaction.

Band selection is one of the most effective methods to reduce the band redundancy of hyperspectral images (HSIs). Most existing band selection methods tend to regard each band as a whole, and then explore the band redundancy with the pixel-wise features directly. However, since the regions of HSIs corresponding to different objects have diverse spectral properties and spatial structure, such above scheme limits the performance of hyperspectral band selection due to the lack of spatial information. To address above issues, a novel band selection method via region-aware latent features fusion based clustering (RLFFC) is proposed. Specifically, we employ the superpixel segmentation to segment HSIs into multiple regions so that the spatial information of HSIs can be fully preserved. In order to capture the priori information, we construct its corresponding Laplacian matrix from which a group of low dimensional latent features are generated to further enhance the separability among different bands. Then, a shared latent feature representation of HSIs is obtained by fusing region-aware latent features to effectively capture the band redundancy of HSIs. Finally, the k -means clustering algorithm is utilized to obtain the index of the selected bands from the shared latent feature representation. As a result, the spectral and spatial properties are well exploited in the proposed method. Extensive experiments on four public hyperspectral datasets show that the proposed method achieves superior performance when compared with other state-of-the-art ones.

The embedded immobilized enzymes (Rhizopus-oryzae) on the magnetic nanoparticles (Fe3O4-NPs) is a new application for the sustainable production of high-quality biodiesel. In this study, biodiesel is derived from Kapok oil via ultrasonication (US)-assisted catalytic transesterification method. A novel attempt is made to prepare magnetic nanoparticles embedded by an immobilized enzyme to solve the problem of enzyme denaturation. This innovative method resulted in optimum biodiesel conversion of 89 ± 1.17% under reactant molar ratio (methanol: oil) of 6:1, catalyst loading 10 wt% with a reaction time of 4 h at 60 °C. The kinetic and thermal study reveals that conversion of Kapok oil to biodiesel follows a pseudo first-order reaction kinetic with a lower ΔE of 30.79 kJ mol−1. The ΔH was found to be 28.06 kJ mol−1 with a corresponding ΔS of −237.12 J mol−1 K−1 for Fatty Acid Methyl Ester formation. The ΔG was calculated to be from 102.28 to 109.40 kJ mol−1 for temperature from 313 K to 343 K. The positive value of ΔH and ΔG is an indication of endothermic and non-spontaneous reaction. A negative ΔS indicates the reactant in the transition state possesses a higher degree of ordered geometry than in its ground state. The immobilized catalysts provided great advantages towards product separation and efficient biodiesel production. Highlights: 1. Effective catalytic transesterification assisted by the ultrasonication method was used for bi-odiesel production. 2. Magnetite nanoparticles synthesized by the co-precipitation method were used as heteroge-neous catalysts. 3. An immobilized enzyme (Rhizopus-oryzae) was embedded in the heterogeneous catalyst, as it is reusable and cost-effective. 4. The maximum biodiesel yield obtained from Kapok oil was 93 ± 1.04% by catalytic trans-esterification reactions.

Albeit great success has been achieved in image defocus blur detection, there are still several unsolved challenges, e.g., interference of background clutter, scale sensitivity and missing boundary details of blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We first fuse the features from different layers of FCN as shallow features and semantic features, respectively. Then, the fused shallow features are propagated to deep layers for refining the details of detected defocus blur regions, and the fused semantic features are propagated to shallow layers to assist in better locating blur regions. The fusion and refinement are carried out recurrently. In order to narrow the gap between low-level and high-level features, we embed a feature adaptation module before feature propagating to exploit the complementary information as well as reduce the contradictory response of different feature layers. Since different feature channels are with different extents of discrimination for detecting blur regions, we design a channel attention module to select discriminative features for feature refinement. Finally, the output of each layer at last recurrent step are fused to obtain the final result. We collect a new dataset consists of various challenging images and their pixel-wise annotations for promoting further study. Extensive experiments on two commonly used datasets and our newly collected one are conducted to demonstrate both the efficacy and efficiency of DeFusionNet.

Cholangiocarcinoma (CCA) is a highly malignant tumor of the hepatobiliary system that has failed to respond to many traditional therapies to a certain extent, including surgery, chemotherapy and radiotherapy. In recent years, the new therapeutic schemes based on immunology have fundamentally changed the systemic treatment of various malignant tumors to a certain extent. In view of the immunogenicity of CCA, during the occurrence and development of CCA, some immunosuppressive substances are released from cells and immunosuppressive microenvironment is formed to promote the escape immune response of its own cells, thus enhancing the malignancy of the tumor and reducing the sensitivity of the tumor to drugs. Some immunotherapy regimens for cholangiocarcinoma have produced good clinical effects. Immunotherapy has more precise characteristics and less adverse reactions compared with traditional treatment approaches. However, due to the unique immune characteristics of CCA, some patients with CCA may not benefit in the long term or not benefit at all after current immunotherapy. At present, the immunotherapy of CCA that have been clinically studied mainly include molecular therapy and cell therapy. In this article, we generalized and summarized the current status of immunotherapy strategies including molecular therapy and cell therapy in CCA in clinical studies, and we outlined our understanding of how to enhance the clinical application of these immunotherapy strategies.

This study prepares a novel phosphorene (PN) and loads it onto TiO2 to fabricate PN-TiO2 and effectively photodegrade the hydrophobic environmental hormone 17β-estradiol in aqueous solutions. The effect of the PN on degradation efficiency is systematically investigated. It is observed that the doping of TiO2 with PN significantly enhances its photocatalytic and adsorption properties compared with that in the absence of PN; that is, the addition improves the adsorption capability of the composite. The optimal PN weight content is found to be 0.5%. The performance of the PN-TiO2 photocatalyst in degrading E2 is around 67.5%. However, its photodegradation efficiency gradually decreases when the PN content is further increased. This optimal PN content directly suggests synergistic interactions affecting the photodegrading efficiency. Compared with other PN-based photocatalysts mentioned in the literature, this PN-based material possesses striking advantages, such as higher energy efficiency, greater removal capacity, and superior cost-effectiveness. Further, the decrease in the biotoxicity of the water after treatment is evident in observing the development of zebrafish embryos. The studies of the catalyst performed on the zebrafish show that it results in a higher mortality rate at 96 h with a superior hatching rate and healthy fish development. In summary, the prepared PN-based materials exhibited promising photocatalytic capabilities for the removal and biotoxicity reduction of 17β-estradiol in aqueous solutions.

Multi-view clustering, which exploits the multi-view information to partition data into their clusters, has attracted intense attention. However, most existing methods directly learn a similarity graph from original multi-view features, which inevitably contain noises and redundancy information. The learned similarity graph is inaccurate and is insufficient to depict the underlying cluster structure of multi-view data. To address this issue, we propose a novel multi-view clustering method that is able to construct an essential similarity graph in a spectral embedding space instead of the original feature space. Concretely, we first obtain multiple spectral embedding matrices from the view-specific similarity graphs, and reorganize the gram matrices constructed by the inner product of the normalized spectral embedding matrices into a tensor form. Then, we impose a weighted tensor nuclear norm constraint on the tensor to capture highorder consistent information among multiple views. Furthermore, we unify the spectral embedding and low rank tensor learning into a unified optimization framework to determine the spectral embedding matrices and tensor representation jointly. Finally, we obtain the consensus similarity graph from the gram matrices via an adaptive neighbor manner. An efficient optimization algorithm is designed to solve the resultant optimization problem. Extensive experiments on six benchmark datasets are conducted to verify the efficacy of the proposed method.

The rapid advancement of the Internet has brought a exponential growth in network traffic. At present, devices deployed at edge nodes process huge amount of data, extract key features of network traffic and then forward them to the cloud server/data center. However, since the efficiency of mobile terminal devices in identifying and classifying encrypted and malicious traffic lags behind, how to identify network traffic more efficiently and accurately remains a challenging problem. We design a convolutional neural network model: One-dimensional convolutional neural network with hexadecimal data (HexCNN-1D) that combines normalized processing and attention mechanisms. By adding the attention mechanism modules Global Attention Block (GAB) and Category Attention Block (CAB), network traffic is classified and identified. By extracting effective load information from hexadecimal network traffic, our model can identify most categories of network traffic including encrypted and malicious traffic data. The experimental results show that the average accuracy is 98.8%. Our model can greatly improve the accuracy of network traffic data recognition.

We study the two-dimensional motion of a magnetic skyrmion driven by a ratchetlike polarized electric current that is periodic in both space and time. Some general cases are considered, in each of which,in the low temperature and adiabatic limit, regardless of the details of the driving current, the time and statistical average velocity along any direction is topologically quantized as a Chern number, multiplied by a basic unit. We make two approaches, one based on identifying the drift direction, and the other based on the nonhermitian adiabatic perturbation theory developed for the Fokker-Planck operator. Both approach applies in the case of periodicity along the direction of the driving current and homogeneity in the transverse direction, for which the analytical result is confirmed by our numerical simulation on the constituent spins,and a convenient experiment is proposed., Comment: 32 pages, preprint format

Waste recycling and reuse will result in significant material and energy savings. In this research, usage of hospital sludge as a biochar adsorbent for wastewater treatment plants was investigated. Microwave carbonization was used to carbonize the sludge and then chemically activated with ZnCl2to increase surface area and porosity. A newly designed iron metal doped sludge biochar carbon (SBC) has effective adsorption of inorganic arsenic (As(III), As2O3) in water. The findings clearly demonstrate the viability and utility of using hospital sludge as a source of carbon to generate SBC. The adsorption mechanism of As(III) on SBC’s iron-metal-modified surface has been studied using density functional theory (DFT) to understand the impact of functional complexes on adsorption As(III). Tests showed physical as well as chemical adsorption of As(III) on Fe-SBC surface. Fe’s involvement in functional complexes greatly fostered SBC surface activity and it’s As(III) adsorption ability. The physical adsorption energies of As(III) with Fe functional complexes on the SBC surface were −42.3 KJ mol−1. Other hand, the chemical adsorption energies of As(III) on Fe-SBC surface was −325.5 KJ mol−1. As(III) is capable of interacting in a bidentate fashion with the dopants through the protonated oxygen atoms and this conformation of the cyclic structure is higher in the adsorption energy than the others.

Co-catalyst free SrTiO3 was prepared by hydrothermal method and it’s calcinated by different temperatures such as 300, 500, 700, and 900 °C to improve the photocatalytic hydrogen production. The SrTiO3 exhibits a cubic crystal structure which was revealed through X-ray diffraction (XRD) analysis. Scanning electron microscopy (SEM) images show the combination of nano spherical-needle morphology changed into nano-cubic morphology when the temperature varied from 300 to 900 °C. Further, transmission electron microscopy (TEM) result also confirms the nano-cubic crystal structure with well-resolved edges. The elemental mapping analysis shows the Sr, Ti, O elements were distributed uniformly in the prepared SrTiO3. The X-ray photoelectron spectroscopy (XPS) confirms the presence of chemical compositions and valance state of Sr, Ti, O elements in SrTiO3. The self-trapped excitons recombination of SrTiO3 was analyzed by photoluminescence (PL) analysis. The Electrochemical Impedance Spectroscopy (EIS) analysis of SrTiO3-900 °C shows the lowest electron transfer resistance which promotes effective charge separation. Among the different catalysts, SrTiO3-900 °C gives 6.8 mmol g−1 h−1 of hydrogen under Ultra-Violet irradiation.

This study used refining slag (RS), ground granulated blast furnace slag (GGBS), steel slag (SS), and desulfurized gypsum (DG) to prepare a mine-filling cementitious material. The developed cementitious material and tailings sand were mixed to prepare a novel mine backfill material with better performance and a lower cost. The macroscopic properties and hydration mechanism of the cemented solid waste-based backfill were investigated when RS content was 0, 5%, 10%, 15%, 20%, 30% and 40%. The results showed that introducing RS could reduce the bleeding rate and shorten the setting time of backfill slurry while significantly enhancing the 3-day compressive strength of backfill. Compared to JL-0, the bleeding rate decreased by 50.3% as the RS content was raised to 15%, while the setting time was shortened by 36.5%, and the 3-day compressive strength increased by 4.3 times. As the RS content did not exceed 20%, the 28-day compressive strength of the backfill was not lower than that of the cement backfill (4.3 MPa). The results of microanalysis (including XRD, FT-IR, SEM, TG-DSC, and heat of hydration) revealed that the hydration products of the RS-GGBS-SS-DG quaternary material are primarily C-(A)-S-H gels and AFt. The main effect of RS is to improve the content of aluminates, accelerating and increasing the production of AFt, thus leading to faster overall hydration. This research can provide data support for the application of RS in the mine-filling field. Applying quaternary solid waste-based cementitious materials in the mine-filling field has good economic benefits.

Hyperspectral band selection aims to identify an optimal subset of bands for hyperspectral images (HSIs). For most existing clustering-based band selection methods, they directly stretch each band into a single feature vector and employ the pixelwise features to address band redundancy. In this way, they do not take full consideration of the spatial information and deal with the importance of different regions in HSIs, which leads to a nonoptimal selection. To address these issues, a region-aware hierarchical latent feature representation learning-guided clustering (HLFC) method is proposed. Specifically, in order to fully preserve the spatial information of HSIs, the superpixel segmentation algorithm is adopted to segment HSIs into multiple regions first. For each segmented region, the similarity graph is constructed to reflect the bands-wise similarity, and its corresponding Laplacian matrix is generated for learning low-dimensional latent features in a hierarchical way. All latent features are then fused to form a unified feature representation of HSIs. Finally, k -means clustering is utilized on the unified feature representation matrix to generate multiple clusters from which the band with maximum information entropy is selected to form the final subset of bands. Extensive experimental results demonstrate that the proposed clustering method can achieve superior performance than the state-of-the-art representative methods on the band selection. The demo code of this work is publicly available at https://github.com/WangJun2023/HLFC.

While various studies have focused on the significance of computational thinking (CT) for the future career paths of individuals in science, technology, engineering, and mathematics (STEM), few studies have focused on computational thinking for non-STEM college students. This study explores the relationship between computational thinking and learning satisfaction for non-STEM-major college students. A conceptual model is proposed to examine the structural relationships among computational thinking, self-efficacy, self-exploration, enjoyment and learning satisfaction in an AppInventor-based liberal education course. Collecting data from 190 undergraduate students from Taiwan and analyzing the data by using partial least squares (PLS) methods, the research framework confirms the six proposed hypotheses. These results show that both computational thinking and enjoyment play significant roles in both self-exploration and digital self-efficacy. Moreover, digital self-efficacy and self-exploration also have a significant positive influence on learning satisfaction. These findings have implications for influencing the learning outcomes of non-STEM-major college students, computational thinking course instructors, and computational thinking relevant policies.

The development of Chinese cemetery hills in Singapore reflects the changing dominance of the dialect groups between the 19th and 20th centuries. Heng San Ting 恒山亭 is the earliest cemetery hill of the Hokkien dialect group, and newly excavated burials indicate that early Singaporean Hokkien came not only from Zhangzhou 漳州 and Quanzhou 泉州 in southern Fujian 福建, but also from places such as Yongchun 永春 from the interior of Fujian. Apart from the Hokkien dialect group, the Cantonese 广东, Hakka 客家, Teochew 潮州 and Hainan 海南 communities also established their cemetery hills. In the early 19th century, the Chinese communities were divided into different dialect groups to form their representative cemetery hills, but the smaller communities within the dialect groups started to form and develop their own cemeteries due to increasing economic power from the mid to late 19th century. Scholars generally believe that the other four dialect groups, led by the Cantonese and Hakka dialect groups, formed a “United Front 联合阵线” to confront the Hokkien dialect group. However, this paper looks at the smaller communities under the five dialect groups and discovers that these communities developed and maintained their own cemetery hills and communicated with the smaller communities from different dialect groups. It was not a direct confrontation. In the 20th century, although the government introduced a series of policies to restrict the development of Chinese burial mounds, the different communities retained their autonomy under the government’s policies. From the development and changes of the Chinese dialect group cemeteries in Singapore, we see that the Chinese community still retains its own autonomy despite the rapid changes of society and the change of times.

Metallic Mimosa pudica , a three-dimensional (3D) biomimetic structure made of metallic glass, is formed via laser patterning: Blooming, closing, and reversing of the metallic M. pudica can be controlled by an applied magnetic field or by manual reshaping. An array of laser-crystallized lines is written in a metallic glass ribbon. Changes in density and/or elastic modulus due to laser patterning result in an appropriate size mismatch between the shrunken crystalline regions and the glassy matrix. The residual stress and elastic distortion energy make the composite material to buckle within the elastic limit and to obey the minimum elastic energy criterion. This work not only provides a programming route for constructing buckling structures of metallic glasses but also provides clues for the study of materials with automatic functions desired in robotics, electronic devices, and, especially, medical devices in the field of medicine, such as vessel scaffolds and vascular filters, which require contactless expansion and contraction functions.

Metallic

Norfloxacin (NF) is an emerging antibiotic contaminant due to its significant accumulation in the environment. Photocatalytic degradation is an effective method for removing emerging contaminant compounds in aqueous solution; however, it is not commonly applied because of the poor solubility of contaminant compounds in water. In this study, a photocatalytic degradation experiment was carried out on NF using a self-made ceria catalyst. At an initial concentration of NF of 2.5 mg L−1, the dosage of CeO2 was 0.1 g L−1 photocatalyst in water, and the initial pH of the NF solution was 8.0. With a reaction time of 180 min, the total removal rate of NF could reach 95%. Additionally, the studies on hydrogen production show that the maximum hydrogen production with 2% Fe–CeO2 can reach 25,670 μmol h−1 g−1 under close to 8 W of 365 nm, a methanol concentration of 20%, and a catalyst dose of 0.1 g L−1 photocatalyst in water. Furthermore, the intensities of photoluminescence (PL) emission peaks decreased with increased Fe-doped amounts on CeO2, suggesting that the irradiative recombination seemed to be weakened.