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On‐chip spectral imaging based on engineered spectral modulation and computational spectral reconstruction provides a promising scheme for portable spectral cameras. However, the angle dependence of modulation units results in the angle sensitivity of spectral imaging, which limits its practical applications. Here, metal is utilized instead of dielectric materials to realize on‐chip angle‐robust computational spectral imaging based on a group of topology‐optimized plasmonic metasurface units under a 30° field‐of‐view, and demonstrate angle‐insensitive spectral imaging in the wavelength range of 450–750 nm for average polarization. Furthermore, it is experimentally verified that the angle‐insensitive spectral filtering effects of the fabricated metasurface units, and demonstrated angle‐robust spectral reconstruction with a fidelity of over 98% as well as spectral imaging for a standard color checker. This approach expands the application scale of spectral imaging, which has great potential on metal‐based on‐chip multimodal imaging with dimension of depth, polarization, spectrum and so on. [ABSTRACT FROM AUTHOR]

Metastable austenitic stainless steels (MASS) are widely used in various industrial applications due to their exceptional mechanical properties. The microstructure of MASS can be regulated through severe plastic deformation, heat treatments, and surface treatments to achieve further improvement of mechanical properties. Herein, nanograin and quasiheterostructure are prepared by cold rolling and annealing process in Fe–17Cr–6Ni MASS. The mechanical response, deformation mechanism, and strengthening contribution of the two steels with representative microstructure are studied. The results indicate that nanograin austenitic steel (803 MPa, 22%) has superior yield strength and similar ductility compared with that of quasiheterostructure steel (600 MPa, 25%). In the nanograin steel, the deformation mechanism is mainly dislocation slip and deformation‐induced martensite transformation, while in quasiheterostructure steel, the deformation twinning is also included for the relative lower stacking fault energy in the coarse grain. The yield strength of nanograin structural steel is about 200 MPa higher than that of quasiheterostructure steel, which can be attributed to the differences in grain refinement strengthening, phase‐transformation strengthening, and precipitation strengthening of Cu particles. The findings presented in this study are informative for modulating MASS properties and ultimately improving the lifespan in a variety of industrial settings. [ABSTRACT FROM AUTHOR]

Both epidemiological investigation and animal experiments demonstrated that pre-/postnatal exposure to perfluorooctane sulfonic acid (PFOS) could induce neurodevelopmental disorders. Previous studies showed that astrocyte was involved in PFOS-induced neurotoxicity, while little information is available. In the present study, the role of astrocyte-derived calmodulin-dependent protein kinase II (CaMKII)-phosphorylated discs large homolog 1 (DLG1) signaling in PFOS eliciting cytotoxicity in neuron was explored with primary cultured hippocampal astrocyte and neuron. The application of PFOS showed a decreased cell viability, synapse length and glutamate transporter 1 (GLT-1) expression, but an increased CaMKII, DLG1 and cyclic AMP response element binding protein (CREB) expression in primary cultured astrocyte. With 2-(2-hydroxyethylamino)-6-aminohexylcarbamic acid tert-butyl ester-9-isopropylpurine (CK59), the CaMKII inhibitor, the disturbed cell viability and molecules induced by PFOS could be alleviated (CREB expression was excluded) in astrocytes. The cytotoxic effect of neuron exposed to astrocyte conditional medium collected from PFOS (PFOS-ACM) pretreated with CK59 was also decreased. These results indicated that PFOS mediated GLT-1 expression through astrocyte-derived CaMKII-DLG signaling, which might be associated with injuries on neurons. The present study gave an insight in further exploration of mechanism in PFOS-induced neurotoxicity. [ABSTRACT FROM AUTHOR]

BiVO 4 photoanode shows severe recombination of photogenerated carriers, which limits its water oxidation performance for photoelectrochemical (PEC) water splitting. In this paper, the surface of BiVO 4 photoanode was modified through the coupling of phosphotungstic acid (H 3 O 40 PW 12 ·xH 2 O, denotes as PWO) with CoOOH to address such drawbacks. At 1.23 V RHE , the photocurrent density of BiVO 4 /PWO/CoOOH photoanode reaches 2.80 mA/cm2, which is 2.59-fold that of bare BiVO 4 photoanode. Moreover, the initial onset potential of BiVO 4 /PWO/CoOOH photoanode is also negatively decreased by 270 mV than that of bare BiVO 4 photoanode. Detailed structural and electrochemical characterizations demonstrate that the decoration of PWO serves as an intermediate layer to promote the transport of holes from BiVO 4 to CoOOH, while the deposition of CoOOH acts as a protective layer as well as cocatalyst to reduce the photocorrosion and improve the PEC activities of BiVO 4. The synergistic coupling of PWO and CoOOH not only makes efficient bulk and surface charge separation, but also takes full advantage of photo-generated holes on the surface, which accelerates water oxidation kinetics, ultimately achieving an improvement in PEC water oxidation. PWO and CoOOH, which widens the range of light absorption and effectively enhances the capacity of light absorption, can therefore induce BiVO 4 to produce more photogenerated charge. Meanwhile, CoOOH facilitates the kinetics of water oxidation. The synergistic effect of PWO and CoOOH improves the PEC performance of BiVO 4. [Display omitted] • BiVO 4 /PWO/CoOOH composite photoanode is reasonably designed and synthesized. • BiVO 4 /PWO/CoOOH photoanode shows excellent stability for PEC. • BiVO 4 /PWO/CoOOH electrode exhibited a high PCD of 2.8 mA/cm2 at 1.23 V RHE. • Synergistic effects of PWO and CoOOH deposition improved the performance. [ABSTRACT FROM AUTHOR]

To assess visually and quantitatively the contributions of the adhesive layer photopolymerization and the subsequent resin composite increment to spatio-temporal maps of temperature at five different cavity locations, subjected to two irradiance curing protocols: standard and ultra-high. Caries-free molars were used to obtain 40, 2 mm thick dentin slices, randomly assigned to groups (n = 5). These slices were incorporated within 3D-printed model cavites, 4 mm deep, restored with Adhese® Universal bonding agent and 2 mm thick Tetric® Powerfill resin composite, and photocured sequentially, as follows: G1: control-empty cavity; G2: adhesive layer; G3 composite layer with no adhesive; and G4 composite layer with adhesive. The main four groups were subdivided based on two curing protocols, exposed either to standard 10 s (1.2 W/cm2) or Ultra high 3 s (3 W/cm2) irradiance modes using a Bluephase PowerCure LCU. Temperature maps were obtained, via a thermal imaging camera, and numerically analyzed at 5 locations. The data were analyzed using two-way ANOVA followed by multiple one-way ANOVA, independent t-tests and Tukey post-hoc tests (α = 0.05). T max , ΔT, T int (integrated area under the curve) and time-to-reach-maximum-temperature were evaluated. Two-way ANOVA showed that there was no significant interaction between light-curing time and location on the measured parameters (p > 0.05), except for the time-to-reach-maximum-temperature (p < 0.05). Curing the adhesive layer alone with the 10 s protocol resulted in a significantly increased pulpal roof temperature compared to 3 s cure (p < 0.05). Independent T-tests between G3 and G4, between 3 s and 10 s, confirmed that the adhesive agent caused no significant increases (p > 0.05) on the measured parameters. The ultra-high light-curing protocol significantly increased ΔT in composite compared to 10 s curing (p < 0.05). When the adhesive layer was photocured alone in a cavity, with a 2 mm thick dentin floor, the exothermal release of energy resulted in higher temperatures with a 10 s curing protocol, compared to a 3 s high irradiance. But when subsequently photocuring a 2 mm layer of composite, the resultant temperatures generated at pulpal roof location from the two curing protocols were similar and therefore there was no increased hazard to the dental pulp from the immediately prior adhesive photopolymerization, cured via the ultra-high irradiation protocol. [ABSTRACT FROM AUTHOR]

It is significant to develop a green ammonia preparation method for decreasing carbon emission. This work demonstrates a zero-carbon method for preparing ammonia feedstock consisting of a ratio of H 2 :N 2 ≥ 3:1 by direct electrolysis of air and steam as fuel gas through solid oxide electrolysis cell (SOEC). Through adjusting the working current and proportion of air/steam, the ratio of H 2 and N 2 can be achieved to more than 4:1, and the fuel is pure enough to be used for synthesizing ammonia. The production rate of ammonia feedstock reaches 1.79 × 10−6 mol/ (cm2·s), and the energy conversion efficiency (ECE) reaches 69.30 %. A stable 100 h electrolysis operation is carried out to generate H 2 /N 2 required for ammonia production. This work provides a new efficient method for the synthesis of green ammonia. • One-step method to produce feedstock for green ammonia is developed. • H 2 :N 2 ≥ 3:1 is achieved by direct electrolysis of air/steam through solid oxide cells. • The ammonia production rate reached to 1.79 × 10−6 mol/ (cm2·s). • A stable operation is observed for 100 h as required for ammonia production. • Partial Ni oxidation near the fuel inlet is the main cause of cell degradation. [ABSTRACT FROM AUTHOR]

This study has developed and optimized a machine learning model to accurately predict the final colors of CAD-CAM ceramics and determine their required minimum thicknesses to cover different clinical backgrounds. A total of 120 ceramic specimens (2 mm, 1 mm and 0.5 mm thickness; n = 10) of four CAD-CAM ceramics - IPS e.max, IPS ZirCAD, Upcera Li CAD and Upcera TT CAD - were studied. The CIELab coordinates (L*, a* and b*) of each specimen were obtained over seven different clinical backgrounds (A1, A2, A3.5, ND2, ND7, cobalt-chromium alloy (CC) and medium precious alloy (MPA)) using a digital spectrophotometer. The color difference (ΔE) and lightness difference (ΔL) results were submitted to 39 different models. The prediction results from the top-performing models were used to develop a fusion model via the Stacking integrated learning method for best-fitting prediction. The SHapley Additive exPlanation (SHAP) was performed to interpret the feature importance. The fusion model, which combined the ExtraTreesRegressor (ET) and XGBRegressor (XGB) models, demonstrated minimal prediction errors (R2 = 0.9) in the external testing sets. Among the investigated variables, thickness and background colors (CC and MPA) majorly influenced the final color of restoration. To achieve perfect aesthetic restoration (ΔE<2.6), at least 1.9 mm IPS ZirCAD or 1.6 mm Upcera TT CAD were required to cover the CC background, while two tested glass-ceramics did not meet the requirements even with thicknesses over 2 mm. The fusion model provided a promising tool for automate decision-making in material selection with minimal thickness over various clinical background. [Display omitted] • Fusion Machine Learning (ML) model accurately predicts the final color of CAD-CAM restoration. • Ceramic thickness is the most influential factor in predicting the desired color outcome. • ML model assists in material selection and determining optimal restoration thickness over various clinical backgrounds. [ABSTRACT FROM AUTHOR]

Latin hypercube design (LHD) is one of the most frequently used sampling methods. However, most LHDs generate data samples in a manner that hinders computational efficiency and space-filling performance when high dimensions and large samples are involved. Therefore, a sequential recursive evolution Latin hypercube design (RELHD) is proposed in this article, which adopts a permutation inheritance algorithm to update and optimize the LHD. A recursive split algorithm is also proposed and used to enhance the computational efficiency by dividing the sample set into smaller subsets. Numerical experiments demonstrate that the space-filling quality of the RELHD compares well with the enhanced stochastic evolutionary algorithm (ESE) in complex problems with large samples and high dimensions, with RELHD having a significantly higher computational efficiency than ESE. Finally, the sequential approach of RELHD proves to be a more efficient strategy when dealing with sampling-based analysis problems. [ABSTRACT FROM AUTHOR]

In-office bleaching, using hydrogen peroxide, is effective to remove dental enamel stains. However, bleaching agents can deteriorate surface properties of CAD-CAM materials. This in vitro study aimed to investigate the effect of in-office bleaching agents on Vickers hardness and surface topography of polished and unpolished dental CAD-CAM composite materials (Grandio blocs, Lava Ultimate, BRILLIANT Crios, Cerasmart), and a polymer-infiltrated ceramic network block (Vita Enamic). The specimens were randomly divided into two groups: unpolished or polished. The micro-hardness and surface topography of each group were measured before bleaching, after a 60 min bleaching period, and 24-h and one-month post-bleaching. In-office bleaching significantly influenced the Vickers hardness of both the polished and unpolished CAD/CAM composite blocks, with Vita Enamic exhibiting the least hardness stability among all groups. Furthermore, in-office bleaching significantly influenced the surface roughness of unpolished CAD/CAM composite blocks. There was a significant difference in hardness reduction between the polished and unpolished specimens for most of the investigated materials at different time points. The bleaching did not influence the surface roughness of the investigated polished group, except for Vita Enamic and Lava Ultimate. However, it did influence the surface roughness of the investigated materials in the unpolished group. [ABSTRACT FROM AUTHOR]

Unsupervised k -nearest-neighbor-based outlier detectors play a vital role in data science research. However, the detectors' performance relies on the choice of the parameter k. However, autonomous selection of the optimal k is poorly documented in literature as it is very challenging. Conventional methods prove ineffective and lack universality as they fail to account for both application and detector factors simultaneously. This article proposes neighborhood consistency , a new concept which tackles the existing issues of selecting the optimal k by considering both application and detector factors. This concept was used to develop a method termed k finder based on neighborhood c onsistency (KFC). KFC does not rely on any extra parameter and has linear time complexity. Simulations show that KFC outperformed baselines and had a good generality to different datasets and detectors. The implementation of the proposed methods can be found on www.OutlierNet.com for reproducibility. • Neighborhood consistency (NC) that an object and its k-NN should have consistent outlier scores is introduced. • Based on NC, we proposed a framework called KFC for automatically selecting k for k-NN-based outlier detectors. • We reviewed the optimum-k-searching methods published between 2000 and 2022. [ABSTRACT FROM AUTHOR]

In-office bleaching, using hydrogen peroxide, is effective to remove dental enamel stains. However, bleaching agents can deteriorate surface properties of CAD-CAM materials. This in vitro study aimed to investigate the effect of in-office bleaching agents on Vickers hardness and surface topography of polished and unpolished dental CAD-CAM composite materials (Grandio blocs, Lava Ultimate, BRILLIANT Crios, Cerasmart), and a polymer-infiltrated ceramic network block (Vita Enamic). The specimens were randomly divided into two groups: unpolished or polished. The micro-hardness and surface topography of each group were measured before bleaching, after a 60 min bleaching period, and 24-h and one-month post-bleaching. In-office bleaching significantly influenced the Vickers hardness of both the polished and unpolished CAD/CAM composite blocks, with Vita Enamic exhibiting the least hardness stability among all groups. Furthermore, in-office bleaching significantly influenced the surface roughness of unpolished CAD/CAM composite blocks. There was a significant difference in hardness reduction between the polished and unpolished specimens for most of the investigated materials at different time points. The bleaching did not influence the surface roughness of the investigated polished group, except for Vita Enamic and Lava Ultimate. However, it did influence the surface roughness of the investigated materials in the unpolished group. [ABSTRACT FROM AUTHOR]

To disentangle the sources of bank inefficiency, this paper presents an extended two-stage network multi-directional efficiency analysis (NMEA) approach by taking the internal structure of the banking system into account. The proposed two-stage NMEA approach extends the conventional "black-box" MEA approach, providing a unique efficiency decomposition and identifying which variables drive the inefficiency for banking systems with a two-stage network structure. An empirical application of Chinese listed banks from 2016 to 2020 during the 13th Five-year Plan reveals that the overall inefficiency of sample banks is primarily sourced from the deposit-generating subsystem. Additionally, different types of banks display differentiated evolution modes over different dimensions, confirming the importance of applying the proposed two-stage NMEA approach. [ABSTRACT FROM AUTHOR]

Metamodels are popular approaches to improve design efficiency in practical-engineering problems through replacing the time-consuming simulation models with easy-to-evaluate numerical models. To improve the generalization performance of the radial basis function (RBF) metamodel, this work sets up an anisotropic radial basis function metamodeling algorithm enhanced by recursive evolution Latin hypercube design and fast K-fold cross-validation method. First, the recursive evolution Latin hypercube design method splits the large-sample design into several small-sample designs to reduce computation cost and naturally splits the training samples into several folds for K-fold cross-validation. Then, the training samples are utilized to build the anisotropic RBF model, where the sensibility of model to each dimension is considered. In anisotropic RBF method, the optimization of hyperparameters in RBF model is transformed to anisotropic-scaling factors to improve the performance and computational efficiency. Afterwards, the evolutionary algorithm and fast K-fold cross-validation method are employed to optimize the anisotropic-scaling factors, and thus, the final metamodel is constructed. Finally, several numerical functions and an engineering case of cylindrical stiffened shell performance prediction are conducted to test the proposed enhanced anisotropic RBF metamodel. Results indicate that the proposed method is competitive compared with other popular metamodel methods and state-of-the-art multi-width RBF modeling methods, and can effectively deal with practical-engineering problems. [ABSTRACT FROM AUTHOR]

[Display omitted] Nanograins and pores, as two common microstructural defects, are capable to impede phonon transport. However, thus far, the stability of nanograins at elevated temperature and the feasibility of porosity in boosting the figure of merit ZT are still concerned in thermoelectrics. Herein, we report that a specifically designed microstructure mainly consisting of ultrafine grains within the nanocrystalline regime and randomly distributed pores in α-MgAgSb gives rise to an ultralow lattice thermal conductivity ∼0.46 W m−1 K−1 at 300 K that breaks the limit of the estimated minimum value. Associated with a slightly deteriorated electrical performance, an unprecedented performance, ZT ∼0.94 at 300 K and ZT ave ∼1.16, are realized. Benefiting from optimized α-MgAgSb in this work and Mg 3.2 (Bi, Sb) 2 material to fabricate a Peltier module, we can achieve a high maximum temperature difference ΔT max ∼52 K and a maximum coefficient of performance COP ∼8.3 at hot-side temperature T h = 300 K, outperforming these previously reported non-Bi 2 Te 3 modules. This is, to our knowledge, the first demonstration of high performance thermoelectric modules mediated by the utilization of ultrafine grains and nanoporous structures, which provides a swift pathway to accelerate the wide employment of thermoelectric cooling technology in modern society. [ABSTRACT FROM AUTHOR]

To evaluate the effects of background color, ceramic type and coping thickness on masking ability (ΔE) and translucency parameter (TP) of CAD/CAM lithium disilicate glass-ceramic/monolithic zirconia and, to determine the correlations between coping thickness, TP and ΔE. A total of 120 ceramic specimens (2 mm, 1 mm and 0.5 mm thickness; n = 10) of four CAD/CAM ceramics: IPS e.max, IPS ZirCAD, Upcera Li CAD and Upcera TT CAD, were studied. Specimens were tested over nine fabricated backgrounds: A1, A2, A3.5, ND2 and ND7 shade resin-based composites, cobalt-chromium alloy (CC), medium precious alloy (MPA), black (B) and white (W). CIELab values (L*, a* and b*), color difference (ΔE) and translucency parameter (TP) were measured and calculated using a digital spectrophotometer (color i7, X-Rite, Pantone®). Data were analyzed using the Shapiro-Wilk test, one-way ANOVA, three-way ANOVA, independent t-tests and Tukey post-hoc tests (p < 0.05). 2 mm CAD/CAM ceramics showed ideal color matching (ΔE<2.6) over different backgrounds, except for CC, B and W backgrounds. Monolithic zirconia had lower ΔE values than lithium disilicate glass-ceramics. ΔE and TP values significantly reduced with the increased coping thickness (p < 0.05). Regardless of ceramic type and thickness, a strong TP-SD of ΔE correlation was observed. CAD/CAM ceramic restoration color was significantly affected by background color, ceramic type and coping thickness. TP is a promising predictor for appropriate ceramic selection to receive an acceptable CAD/CAM ceramic restoration aesthetic. [Display omitted] • CAD/CAM ceramic restoration color was significantly affected by background color, ceramic type and coping thickness. • TP is a promising parameter for appropriate ceramic selection, independent of ceramic crystallization. • TP is a potential indicator helping dentists to determine the tooth preparation amount over different backgrounds. [ABSTRACT FROM AUTHOR]

To determine degree of conversion (DC), maximum polymerization rate (RP max), polymerization shrinkage (PS), maximum shrinkage rate (PS R max) and fracture toughness (K IC) of different types of bulk-fill (BF) composites plus the effect of viscosity reduction techniques. BF specimens were created in 2 mm deep molds: SonicFill 3 (SF3), Viscalor (VC), One Bulk Fill (OBF) and Beautifil Bulk (BBR). SF3 was applied via sonic insertion using a SonicFill handpiece (Kerr Corp. USA). Viscalor was pre-heated in a Caps Warmer in T3 mode (at 68 °C) for 30 s (T3–30 s) and 3 min (T3–3 min), respectively. Specimens were irradiated at zero distance from the upper surface with an Elipar S10 LED unit (3 M ESPE, USA) of mean irradiance 1200 mW/cm2 for 40 s. Real-time polymerization kinetics and DC at 5 min and 24 h post-irradiation (DC 5 min and DC 24 h) were measured using ATR-FTIR (n = 3). PS was measured up to 1 h on 1 mm thick discs via the bonded-disk technique (n = 3) and PS R max obtained by numerical differentiation (n = 3). For fracture toughness, single-edge-notched specimens (32 × 6 × 3 mm) of each BF composite were prepared and measured by three-point bending after 7 d water storage (n = 5). Data were analysed using One-way ANOVA, independent T-tests and Tukey post-hoc tests (p < 0.05). SF3 showed the significantly highest DC 5 min , DC 24 h and RP max (p < 0.05), followed by OBF (p < 0.05). Regardless of pre-heating, VC showed comparable conversion kinetics to BBR (p > 0.05). There was no significant difference in PS of these BF composites, except OBF had the highest PS (p > 0.05). However, PS Rmax significantly varied among materials (p = 0.047) and SF3 had the highest PS R max. Regarding fracture toughness, BBR had the lowest K IC (p < 0.05), whereas other composites showed similar K IC (p > 0.05). Strong correlations of filler content (wt%)-PS/K IC were found. Different pre-heating times had no significant influences on DC %, RP max , PS, PS R max and K IC of VC (p > 0.05). Significance. Different types of bulk-fill composites showed comparable shrinkage. A highly filled BF giomer composite (BBR) had the lowest fracture toughness, whereas others had similar K IC. Pre-heating had no adverse effects on Viscalor properties. Sonication and pre-heating are beneficial techniques to enhance composite flowability without either increasing shrinkage or reducing fracture toughness. [ABSTRACT FROM AUTHOR]

Precipitation of future climate models is critical for the assessments of future drought but contains large systematic biases over the Tibetan Plateau. Although the common precipitation bias correction method, quantile mapping has achieved remarkable results in terms of temporal bias correction, it does not consider the spatial distribution of bias. Furthermore, the extent to which precipitation bias affects drought estimation remains unclear. In our study, we take the Qinghai–Tibet Plateau (QHTP) as the case study and quantify the impact of corrected precipitation bias for seven Coupled Model Intercomparison Project Phase 6 (CMIP6) models on drought assessment in historical and future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). To improve the accuracy of drought prediction, potential evapotranspiration (PET) was also corrected. Firstly, the histogram matching-quantile mapping (HQ) algorithm considering spatial correction is established to correct precipitation and PET. Then, we quantified the effects of precipitation and potential evapotranspiration correction on the change of drought intensity, and finally analyzed the spatiotemporal trends of precipitation, PET, and SPEI over the QHTP in the future. The results show that the HQ method can effectively improve the simulation ability of the model, especially the simulation accuracy of the ensemble model. After correction, the average annual total precipitation (TP) declined by 64.262% in 99.952% of QHTP, the average PET increased in 11.902% of the area and decreased in 88.098% of the area, while the intensity of the drought in 81.331% of the area increased by 2.875% and the 18.669% area decreased by 1.139%. Therefore, the uncorrected simulation data overestimated the future increase trend in precipitation and underestimated the future decrease trend in SPEI. The trend of HQ-corrected TP increased by 3.730 mm/10a, 7.190 mm/10a, and 12.790 mm/10a, and the trend of SPEI (TP and PET corrected) decreased by 0.143/100a, 0.397/100a, and 0.675/100a, respectively. Therefore, quantifying the changing relationship between precipitation bias correction and drought assessments is useful for understanding regional climate change. [ABSTRACT FROM AUTHOR]

Currently, the occurrence of toxic levels of metals in soils is a serious environmental issue worldwide. Phytoremediation is getting much attention to control metals soil pollution because it is economic and environmentally friendly. However, the methods used to detect metals in plants are not uniform and have depicted poor comparability of the research investigations. Therefore, the present overview is designed to discuss the possible chemical forms of metals in various environmental matrixes and the detection methods employed to identify the chemical forms of metals in plants. Moreover, the in situ and indirect methods to detect metals in plants have also been discussed herein. In addition, the pros and cons of the available techniques have also been critically analyzed and discussed. Finally, key points/challenges and future perspectives of these methods have been highlighted for the scientific community. Novelty statement In the current review, the possible chemical forms of metals in various environmental matrixes are discussed in detail. Various extraction agents and their efficiency for extracting metals from plants have been clearly illustrated. Further, all the available methods for analyzing the chemical forms of metals in plants have been compared. [ABSTRACT FROM AUTHOR]

Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led to a conclusion that conventional trajectory outlier detectors suffered from drawbacks, either due to the detectors themselves or the pre-processing methods for the variable-length trajectory inputs utilized by detectors. To address these issues, we proposed a feature extraction method called middle polar coordinates (MiPo). MiPo extracted tabular features from trajectory data prior to the application of conventional outlier detectors to detect trajectory outliers. By representing variable-length trajectory data as fixed-length tabular data, MiPo granted tabular outlier detectors the ability to detect trajectory outliers, which was previously impossible. Experiments with real-world datasets showed that MiPo outperformed all baseline methods with 0.99 AUC on average; however, it only required approximately 10% of the computing time of the existing industrial best. MiPo exhibited linear time and space complexity. The features extracted by MiPo may aid other trajectory data mining tasks. We believe that MiPo has the potential to revolutionize the field of trajectory outlier detection. [ABSTRACT FROM AUTHOR]

Purpose: Automated liver tumor segmentation from computed tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on fully convolutional network (FCN) for medical image segmentation drew on the success of learning discriminative pyramid features. In this paper, we propose a decoupled pyramid correlation network (DPC‐Net) that exploits attention mechanisms to fully leverage both low‐ and high‐level features embedded in FCN to segment liver tumor. Methods: We first design a powerful pyramid feature encoder (PFE) to extract multilevel features from input images. Then we decouple the characteristics of features concerning spatial dimension (i.e., height, width, depth) and semantic dimension (i.e., channel). On top of that, we present two types of attention modules, spatial correlation (SpaCor) and semantic correlation (SemCor) modules, to recursively measure the correlation of multilevel features. The former selectively emphasizes global semantic information in low‐level features with the guidance of high‐level ones. The latter adaptively enhance spatial details in high‐level features with the guidance of low‐level ones. Results: We evaluate the DPC‐Net on MICCAI 2017 LiTS Liver Tumor Segmentation (LiTS) challenge data set. Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) are employed for evaluation. The proposed method obtains a DSC of 76.4% and an ASSD of 0.838 mm for liver tumor segmentation, outperforming the state‐of‐the‐art methods. It also achieves a competitive result with a DSC of 96.0% and an ASSD of 1.636 mm for liver segmentation. Conclusions: The experimental results show promising performance of DPC‐Net for liver and tumor segmentation from CT images. Furthermore, the proposed SemCor and SpaCor can effectively model the multilevel correlation from both semantic and spatial dimensions. The proposed attention modules are lightweight and can be easily extended to other multilevel methods in an end‐to‐end manner. [ABSTRACT FROM AUTHOR]

• We analyzed the isolation-forest-based methods' problem of lacking efficacy in selecting suitable hyperplanes to split data. • We proposed to use random projection techniques to improve isolation forest's efficacy in selecting suitable hyperplanes. • Experimental results on 30 real-world datasets show that the proposed methods outperform 12 SOTA outlier detectors. [Display omitted] Isolation Forest has a low computational complexity, hence has been widely applied to detect outliers in large-scale data. However, it suffers from the artifacts caused by the hyperplanes chosen, thereby failing to detect outliers in some specific regions. To tackle this problem, we propose the random-projection-based Isolation Forest, which works in two steps. First, we transform the data using the random projection technique. Then, we employ the Isolation Forest to identify outliers using the transformed data. Experimental results show that the proposed methods outperform 12 state-of-the-art outlier detectors. [ABSTRACT FROM AUTHOR]

The prediction of Alzheimer's disease (AD) progression plays a very important role in the early intervention of patients and the improvement of life quality. Cognitive scales are commonly used to assess the patient's status. However, due to the complicated pathogenesis of AD and the individual differences in AD, the prediction of AD progression is challenging. This paper proposes a novel coupling model (P-E model) that takes into account the processes of physiological degradation and emotional state transition of AD patients. We conduct experiments on synthetic data to validate the effectiveness of the proposed P-E model. Next, we conduct experiments on 134 subjects with more than 10 follow-ups from the Alzheimer's Disease Neuroimaging Initiative. The prediction performance of the P-E model is significantly better than other state-of-the-art methods, which achieves the mean squared error of 7.137 ± 0.035. The experimental results show that the P-E model can well characterize the non-monotonic properties of AD cognitive data and can also have a good predictive ability for time series data with individual differences. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced augmented radial basis function (eARBF) metamodel combining radial basis function and polynomial chaos expansions for global sensitivity analysis. First, the conditions required for the augmented radial basis function are constructed from the perspective of variance decomposition, after which the analytical expressions of the Sobol' indices are deduced. To improve the generalization performance of ARBF, an anisotropic technique is proposed based on the local density of sample points. Furthermore, the recursive evolution LHD and efficient K-fold cross-validation method are adopted to reduce computational efforts. Five cases are presented to demonstrate the performance of the proposed approach. In all cases, eARBF yields satisfactory results with lower computational effort. The results indicate that the approach proposed in this paper is promising for GSA of engineering problems. [ABSTRACT FROM AUTHOR]

The ability to quickly and accurately determine soil organic matter (SOM) content is critical for effective soil management decisions. Using the colour of soil images captured by smartphones to predict SOM content has emerged as a promising alternative to traditional wet chemistry methods. However, natural environments can present a complex array of light conditions that can compromise the accuracy and consistency of soil image colour acquisition, thus limiting the method's applicability. To address this issue, we propose five colour calibration patterns (C0 (no calibration), C1 (neutral grey), C2 (RGB), C3 (RGBCMY), and C4 (24 colours)), based on a 24-colour standard card. These patterns were used to calibrate the images of 352 original soil samples obtained from smartphones in three different lighting environments - L1 (100–2000 lx), L2 (35,000–40,000 lx), and L3 (75,000–80,000 lx). Random forest models were used to construct predictive models of soil organic matter (SOM) content based on images. Our findings indicate that smartphones exhibit complex spectral response characteristics, which result in poor image accuracy and stability of uncalibrated (C0) images under varying lighting conditions. The uncalibrated (C0) soil images in different lighting environments exhibited high colour difference (∆E mean = 14.11), resulting in poor SOM model sharing performance (R2 mean = 0.52 and RMSE mean = 20.33 g/kg). The use of colour calibration methods reduced the colour difference between soil images (∆E mean = 8.19) and improved the shared accuracy of the model (R2 mean = 0.61 and RMSE mean = 12.00 g/kg). The pattern of colour calibration has a key impact on the performance of the model application. The model sharing accuracy was found to be higher for the same or similar colour calibration pattern combination compared to different combinations of colour calibration patterns. Overall, the richer the colour calibration blocks, the better the model's shared performance. The findings of this research can enhance the application performance of soil attribute prediction models based on the colour of objects captured by smartphones in natural environments. • Differences in light intensity affect the colour stability of smartphone images. • Colour calibration reduces image colour differences in different light for phones. • Colour calibration boosts the shared ability of the attribute prediction model. • Colour calibration patterns with rich colours for better model-sharing ability. [ABSTRACT FROM AUTHOR]

Metasurfaces have an exceptional capacity to manipulate the phase, amplitude, polarization, or spectrum of light. However, unit cells, or meta‐atoms, of metasurfaces are conventionally designed using regular shapes, limiting performance improvement. The utilization of metasurfaces with freeform shaped meta‐atoms for on‐chip ultraspectral imaging is proposed, where the freeform shaped patterns are generated with controllable feature sizes and boundary curvatures for feasible fabrication. These patterns broaden design diversity and enrich metasurface‐unit spectral response with complicated Bloch modes, thus improving spectral imaging performance with enhanced spectrum recovery precision for broadband spectra and smaller center‐wavelength deviation for narrowband spectra. A snapshot on‐chip ultraspectral imaging, with 356 × 436 spectral pixels is experimentally demonstrated. Spectral resolution is state‐of‐the‐art, at 0.5 nm, and mean fidelity of spectral reconstruction for a standard color board reaches 98.78%. These results support future applications in the field of precise intelligent perception. Moreover, the generating method for freeform shaped patterns also benefits for the forward and inverse designs for high‐performance metasurfaces. [ABSTRACT FROM AUTHOR]

Here we systematically investigate the crystallographic orientation relationship (OR) between MC-type precipitates (M, metal; C, carbon) and ferrite matrix in the Ti-Mo microalloyed steel with different processing. In the specimens without austenite deformation, the interphase precipitation can be obtained, and the precipitates obey Baker–Nutting (BN) OR with ferrite matrix. By contrast, in the specimens with austenite deformation, the supersaturated precipitates were formed in ferrite grains, which can obey BN, Nishiyama–Wasserman (NW), Kurdjumov–Sachs (KS) and Pitsch (P) ORs simultaneously. The cooling rate after austenite deformation can influence the OR between carbides and ferrite in the MC/ferrite system. At the cooling rate of 80 °C/s, carbides and ferrite can roughly satisfy these OR with the deviation ≥ 10°, while at the cooling rate of 20 °C/s, carbides and ferrite can strictly obey the specific OR. The energy accumulated in the deformation process and maintained in the fast-cooling process (80 °C/s) can offset the formation energy of the carbides. Thus, the carbides formed in the specimen with the cooling rate of 80 °C/s do not strictly satisfy the specific ORs to meet the rule of lowest energy, and then deviate by a small angle based on the specific ORs. [ABSTRACT FROM AUTHOR]

This paper proposes a lexicographic decomposition strategy for the two-stage network DEA model. Instead of assigning a priority over the two component stages, our proposed approach employs a lexicographic algorithm in such a way that the system efficiencies of the DMUs are lexicographically computed for each of their possible sequences while maintaining the efficiencies of those units already investigated unchanged. In particular, the system efficiency and the two component efficiencies can be uniquely determined under our proposed decomposition approach. Besides, prior to implement the proposed lexicographic evaluation procedure, we also highlight a potential infeasibility problem arising from the normalization constraint, and develop a modified iterative approach that facilitates us to guarantee a feasible search procedure. Finally, we use a numerical example to illustrate the effectiveness of the proposed approach, and conduct an empirical study to analyze China's regional high-tech innovation systems for the period of 12th Five-Year Plan (2011–2015). [ABSTRACT FROM AUTHOR]

Although the importance of music for individuals has long been recognized, the effect of music on employee behavior in the workplace has not yet been fully realized. We developed a theoretical model emphasizing the mediating role of psychological safety in the relationship between pleasant music and voice behavior. A survey was conducted with 241 hotel employees in China. Structural equation modeling analysis results show that pleasant music was positively related to psychological safety, which was positively related to voice behavior. Further, psychological safety fully mediated the relationship between pleasant music and voice behavior. We discuss the implications of our findings to understand the mechanisms through which pleasant music boosts employees' voice behavior, and suggest future research directions. [ABSTRACT FROM AUTHOR]

Privacy preserving is a severe challenge in machine learning and artificial intelligence. Recently, many works have been devoted to solving this problem by proposing various federated learning frameworks and introducing local differential privacy. However, applying local differential privacy to federated learning has lower utility after perturbing the parameters. Therefore, to improve the accuracy and communication efficiency of the model while having strict privacy preserving, we propose CLFLDP, a communication-efficient layer clipping federated learning model with differential privacy preserving. First, a novel adaptive privacy budget allocation scheme is proposed to allocate the privacy budget based on the communication rounds and client relevance which can reduce the loss of privacy budget and the size of model noise. Second, a layer-based top k parameter selection method and aggregation scheme are proposed. The communication cost of the system is reduced by uploading layers with higher client-side relevance and excluding layers with lower relevance. Therefore, the proposed framework can achieve better balance between privacy preserving, communication efficiency and model accuracy of federated learning. Theoretical analysis and experiments on various commonly used image datasets demonstrate the superiority of our framework over the state-of-the-art federated learning frameworks. [ABSTRACT FROM AUTHOR]

Aircraft optimization design problems are mostly computationally intensive. These complicated problems probably contain mixed-variables, while most research has focused on continuous variables. This article sets up a mixed-variable surrogate-based optimization algorithm framework that includes a mixed-variable experiment design method and an inaccurate infilling method. The mixed-variable experiment design method combines improved successive local enumeration with enhanced stochastic evolution to deal directly with discrete variables. The inaccurate infilling method tends to find points with better fitness value and relatively low sample density so as to balance exploration and exploitation. Several numerical functions and a mixed-variable solid rocket motor performance matching design problem are solved using the modified surrogate-based optimization method. The results indicate that the proposed method is competitive compared with other heuristic algorithms and surrogate-based algorithms, and can deal with mixed-variable aircraft design problems effectively. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese medicine (TCM) has a long history of treating glaucoma with remarkable effects, but there is no clear conclusion on its mechanism. Methods: Network pharmacology and molecular docking were used to analyze the mechanism and targets of TCM in the treatment of glaucoma, and baicalein was used to treat chronic ocular hypertension animal models rats for observation. Results: The results of animal experiments showed that baicalein could significantly reduce intraocular pressure (IOP) in a rat model of chronic ocular hypertension and protect the structure of the retina and optic nerve, as shown by hematoxylin–eosin (H&E) staining and transmission electron microscopy (TEM). Reducing the apoptosis of retinal ganglion cells (RGCs) by upregulating the expression of the antiapoptotic protein BCL-2 is basically consistent with the results of molecular docking. In the network pharmacology analysis, many key proteins of biological pathways involved in the herbal therapeutic processes in glaucoma, such as threonine kinase 1 (AKT1, core protein of PI3K/AKT signaling), tumor protein p53 (TP53, a tumor suppressor gene coding tumor protein P53), signal transducer and activator of transcription 3 (STAT3, core protein of JAK/STAT signaling), interleukin 6 (IL-6) and interleukin 17 (IL-17, proinflammatory factors), were identified. Their interactions built complicated chain reactions in the process of glaucoma. Conclusion: By combining the analysis of network pharmacology and animal experimental results, baicalein could effectively improve the symptoms of glaucoma and reduce RGC apoptosis, suggesting that the potential mechanism of TCM in treating glaucoma is related to regulating inflammation and cellular immunity and reducing apoptosis. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of (i) a high-irradiance (3s) light-curing protocol versus (ii) two standard-irradiance (10s) protocols on 2D temperature maps during intra-dental photo-irradiation within a molar cavity restored with either Ultra-Rapid Photo-Polymerized Bulk Fill (URPBF) composites or a pre-heated thermo-viscous bulk-fill composite, compared to a standard bulk-fill resin-based-composite (RBC). The specific objectives included visual assessment of the temperature maps and quantitative assessment of several temperature/time plots at four different locations. A caries-free lower first molar cavity served as a natural tooth mold. Resin composites were placed without intermediary adhesive. Two URPBF composites (PFill; PFlow) and one pre-heated thermo-viscous bulk-fill composite (Viscalor : VC) were compared to a contemporary bulk-fill composite (One Bulk Fill: OBF). Two LED-LCU devices were used: Bluephase PowerCure (PC) and Elipar S10 (S10), with three light-irradiation protocols (PC-3s, PC-10s and S10-10s). 2D temperature maps over the entire coronal area were recorded for 120 s during and after irradiation using a thermal imaging camera. Changes at four different levels were selected from the data sets: (0, 2 and 4 mm from the cavity top and at 1 mm below the dentin cavity floor). The maximum temperature attained (T max), the mean temperature rise (ΔT), the time (s) to reach maximum temperature and the integrated areas (°C s) under the temperature/time (T/t) plots were identified. Data were analysed via three-way ANOVA, One-way ANOVA, independent t-tests and Tukey post-hoc tests (p < 0.05). All RBCs showed qualitatively similar temperature-time profiles. PFlow reached T max in the shortest time. PC-3s (3000 mW/cm2) generated comparable ΔT to S10-10s, except with PFill, where ΔT was greater. Despite the same irradiance (1200 mW/cm2), Elipar S10 led to higher T max and ΔT compared to PC-10s. The highest T max and ΔT were observed at the 2 mm level, and the lowest were at 1 mm depth into the underlying dentin. Coronal 2D temperature maps showed rises largely confined within the bulk-fill RBC materials, with maxima at 2 mm rather than 4 mm depth indicating some extent of thermal insulation for the underlying dentin and pulp. RBCs polymerized via different irradiation protocols showed similar temperature changes. With the PC-3s protocol – also with pre-heated VC – minimal temperature rises at 1 mm within dentin suggest their clinical safety when sufficient remaining dentin thickness is present. [ABSTRACT FROM AUTHOR]

TiO2 mesoporous microspheres were prepared by water heating reflux of TiO2 precursors obtained via hydrolysis of tetrabutyl titanate. TiO2 microspheres were printed on FTO glass substrate and sensitized with CdS nanoparticles for forming CdS/TiO2 thin film via a successive ionic layer adsorption and reaction process. TiO2 microspheres and CdS/TiO2 thin film were characterized using field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction analysis, N2 adsorption desorption measurements, X-ray photoelectron spectroscopy, and UV–Vis absorption spectra. The results indicated that water heating reflux contribute to the crystallization of TiO2 from the amorphous to anatase phase. TiO2 mesoporous microspheres with higher surface area of 268.53 m2/g were formed during the water heating reflux process. A photoresponsive test suggested that the introduction of CdS improves light utilization of TiO2 mesoporous microspheres and reduces recombination of photogenerated electron–hole pairs. Photocurrent density of TiO2 microspheres sensitized with CdS nanoparticles reached 0.2 mA/cm2 while pure TiO2 only obtained 0.036 mA/cm2. The photodegradation performance of the CdS/TiO2 thin film was further investigated. Photodegradation efficiency of CdS/TiO2 thin film reach about 95% within 120 min due to the synergistic effect of CdS and TiO2 heterostructure. The possible catalytic mechanism was also proposed. [ABSTRACT FROM AUTHOR]

The grinding process of the ball mill is an essential operation in metallurgical concentration plants. Generally, the model of the process is established as a multivariable system characterized with strong coupling and time delay. In previous research, a two-input-two-output model was applied to describe the system, in which some key indicators of the process were ignored. To this end, a three-input-three-output system is proposed to improve the model accuracy. Moreover, some practical and effective control strategies have been studied. The common control methods, including model predictive control (MPC), disturbance observer (DO), and so on, show poor performance when strong external and internal disturbances exist. In this paper, a composite control strategy based on MPC-DO is put forward to realize the control of the three-input-three-output ball mill system. The disturbances of the system consist of external disturbances including fluctuation of ore hardness and internal disturbances including model mismatches and strong couplings. The proposed MPC-DO controller includes a feedback control component based on MPC and a feed-forward compensation component based on DO. The simulation results indicate that the composite control scheme based on MPC-DO has good performance of tracking and anti-interference in process control of the ball mill. [ABSTRACT FROM AUTHOR]

The anisotropic heat transfer properties of two-dimensional materials play an important role in controlled heat transfer and intelligent heat management. At present, there are many references on anisotropic heat transfer of two-dimensional materials, but less systematic review of their development status, problems, and future directions. In this paper, intrinsic anisotropic heat transfer of two-dimensional materials, influencing factors and control means are introduced. The preparation methods of thin film with two-dimensional material and the influence factors of macroscopic anisotropic thermal properties are summarized. The technology of two-dimensional material oriented arrangement in matrix and the influence factors of macroscopic anisotropic thermal properties of the composite are outlined. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is regarded as one of the most effective techniques for supervised learning, while the Gaussian kernel SVM is widely utilized due to its excellent performance capabilities. To ensure high performance of models, hyperparameters, i.e. kernel width and penalty factor must be determined appropriately. This paper studies the influence of hyperparameters on the Gaussian kernel SVM when such hyperparameters attain an extreme value (0 or ∞). In order to improve computing efficiency, a parameter optimization method based on the local density and accuracy of Leave-One-Out (LOO) method are proposed. Kernel width of each sample is determined based on the local density needed to ensure a higher separability in feature space while the penalty parameter is determined by an improved grid search using the LOO method. A comparison with grid method is conducted to verify validity of the proposed method. The classification accuracy of five real-life datasets from UCI database are 0.9733, 0.9933, 0.7270, 0.6101 and 0.8867, which is slightly superior to the grid method. The results also demonstrate that this proposed method is computationally cheaper by 1 order of magnitude when compared to the grid method. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a leading cause of irreversible blindness globally. Qidengmingmu Capsule (QC) is a Chinese patent medicine used to treat DR, but the molecular mechanism of the treatment remains unknown. In this study, we identified and validated potential molecular mechanisms involved in the treatment of DR with QC via network pharmacology and molecular docking methods. The results of Ingredient-DR Target Network showed that 134 common targets and 20 active ingredients of QC were involved. According to the results of enrichment analysis, 2307 biological processes and 40 pathways were related to the treatment effects. Most of these processes and pathways were important for cell survival and were associated with many key factors in DR, such as vascular endothelial growth factor-A (VEGFA), hypoxia-inducible factor-1A (HIF-1Α), and tumor necrosis factor-α (TNFα). Based on the results of the PPI network and KEGG enrichment analyses, we selected AKT1, HIF-1α, VEGFA, TNFα and their corresponding active ingredients for molecular docking. According to the molecular docking results, several key targets of DR (including AKT1, HIF-1α, VEGFA, and TNFα) can form stable bonds with the corresponding active ingredients of QC. In conclusion, through network pharmacology methods, we found that potential biological mechanisms involved in the alleviation of DR by QC are related to multiple biological processes and signaling pathways. The molecular docking results also provide us with sound directions for further experiments. [ABSTRACT FROM AUTHOR]

• Smartphones show excellent ability to predict original soil properties. • Image segmentation significantly improves soil property prediction accuracy. • Significant differences in dominant regions between segmentation methods. • The K-means clustering segmentation method outperforms the OUST method. Prediction of soil properties based on images of original soil acquired from smartphones is considered a potential alternative to conventional soil property determination due to its convenience and low cost. However, images of the original soil may contain distracting information such as fissures, shadows, and rocks, which may affect the image feature extraction and property prediction results. Four unsupervised image segmentation methods, namely the maximum interclass variance (OUST) method, the local adaptive threshold segmentation (LATS) method, the K-means clustering segmentation (KMCS) method, and the natural break point segmentation (NBPS) method, were used to segment 62 images of original soil taken by smartphones in outdoor sunny conditions. The number of segmentation levels (N) for KMCS and NBPS varied from 2 to 10. The dominant region, determined as the region with the highest percentage of image area after segmentation, was selected for subsequent property prediction. The results of the segmentation methods revealed significant differences in the dominant regions, leading to significant variations in color and texture features (p < 0.05). The random forest approach was used to construct predictive models for soil Munsell colour, soil organic matter (SOM) content, and soil texture content. The validation results demonstrated that the image segmentation methods improved the prediction accuracy of all three soil properties compared to that of the unsegmented images. Among the segmentation methods, KMCS (N = 3 and 4) exhibited the highest segmentation performance, surpassing the widely used OUST method. This research highlights the significance of image segmentation methods in predicting soil properties based on images of original soil samples and recommended it as an essential process for future soil image processing. The findings of this study have the potential to advance the development of smartphone-based prediction of properties of original soils. [ABSTRACT FROM AUTHOR]

In recent years, bioleaching has emerged as a cost-effective technology for enhancing the dewaterability of sludge. However, the lengthy treatment time involved in sludge bioleaching processes limits daily treatment capacity for sludge. Here, a novel approach was developed through a short time of sludge bioleaching with A. ferrooxidans LX5 (A. f) and A. thiooxidans TS6 (A. t) followed by polyferric sulfate (PFS) flocculation (A. f + A. t + PFS). After 12.5 h of the A. f + A. t + PFS treatment (30% A. f, 10% A. t , 40 mg/g DS S0, 60 mg/g DS FeSO 4 •7H 2 O, and 120 mg/g DS PFS), the reduction efficiency of specific resistance to filtration (SRF) and sludge cake moisture content reached 94.0% and 11.6%, respectively, which were comparable to the results achieved through 24 h of completed bioleaching treatment. In pilot-scale applications, the mechanical dewatering performance was notably improved following A. f + A. t + PFS treatment, with the low moisture content of the treated sludge cake (∼59.2%). This study provides new insights into the A. f + A. t + PFS process and holds potential for developing efficient and promising sludge dewatering strategies in engineering application. [Display omitted] • Introducing PFS flocculation promoted sludge bioleaching dewatering efficiency. • Reduction efficiency of SRF reached 94.0% after 12.5 h of A. f + A. t + PFS treatment. • Bio-acidification, bio-substitution and PFS flocculation simultaneously occurred. • Co-driven bioleaching of A. ferrooxidans and A. thiooxidans caused low sludge pH. • In pilot-scale applications, the moisture content of treated sludge cake was ∼59.2%. [ABSTRACT FROM AUTHOR]

• We firstly uncovered the functional gene variation under cadaverine and putrescine. • Cadaverine significantly reduced the diversity of C cycling genes. • Putrescine significantly increased diversity of C fixation genes only in sediment. • Total carbon and total nitrogen were negatively associated with C and N cycling. The response patterns of microbial functional genes involved in biogeochemical cycles to cadaver decay is a central topic of recent environmental sciences. However, the response mechanisms and pathways of the functional genes associated with the carbon (C) and nitrogen (N) cycling to cadaveric substances such as cadaverine and putrescine remain unclear. This study explored the variation of functional genes associated with C fixation, C degradation and N cycling and their influencing factors under cadaverine, putrescine and mixed treatments. Our results showed only putrescine significantly increased the alpha diversity of C fixation genes, while reducing the alpha diversity of N cycling genes in sediment. For the C cycling, the mixed treatment significantly decreased the total abundance of reductive acetyl-CoA pathway genes (i.e., acsB and acsE) and lig gene linked to lignin degradation in water, while only significantly increasing the hydroxypropionate-hydroxybutylate cycle (i.e., accA) gene abundance in sediment. For the N cycling, mixed treatment significantly decreased the abundance of the nitrification (i.e., amoB), denitrification (i.e., nirS3) genes in water and the assimilation pathway gene (i.e., gdhA) in sediment. Environmental factors (i.e., total carbon and total nitrogen) were all negatively associated with the genes of C and N cycling. Therefore, cadaverine and putrescine exposure may inhibit the pathway in C fixation and N cycling, while promoting C degradation. These findings can offer some new insight for the management of amine pollution caused by animal cadavers. [Display omitted] [ABSTRACT FROM AUTHOR]

To evaluate the effects of pre-heating time and exposure duration on the degree of conversion (DC), maximum rate of polymerization (RP max), polymerization shrinkage strain (PS) and surface micro-hardness (VHN) of Viscalor. Viscalor syringes were pre-heated using a Caps Warmer (VOCO, Germany) in T3 mode (at 68 °C) for 30 s (T3-30s) and 3 min (T3-3min) and then the composite paste was extruded into appropriately sized molds. Light irradiation was applied at zero distance from the upper surface with a LED-LCU of mean irradiance 1200 mW/cm2 for either 20 or 40 s. The real-time polymerization kinetics and DC at 5 min and 24 h post-irradiation (DC 5min and DC 24h) were measured using ATR-FTIR (n = 3). PS was obtained with the bonded-disk technique (n = 3). Top and bottom Vickers micro-hardness (VHN top and VHN bottom) were measured at 5 min post-irradiation and after 24 h dry storage (n = 5). Data were analysed using one-way ANOVA, two-way ANOVA, independent t -test and Tukey post hoc tests (p < 0.05). Polymerization kinetic curves of Viscalor from 0 to 15 min were similar for different pre-heating times and exposure durations. Pre-heated Viscalor (T3-30s and T3-3min) with 40 s exposure had greater VHN top and VHN bottom than for Viscalor (no heat) (p < 0.05). Exposure duration did not significantly affect DC, RP max and PS (p > 0.05). After 24 h storage, DC and VHN increased. Pre-heating did not increase the DC 24h , relative to no pre-heating (p > 0.05). Two-way ANOVA showed that there was no significant interaction between pre-heating time and exposure duration (p > 0.05). Increasing irradiation time from 20 to 40 s did not affect DC, RP max or PS, but increased VHN top. Composite pre-heating had no adverse effect through any premature polymerization. For Viscalor , 3 min pre-heating and 20 s irradiation were sufficient to provide adequate hardness, without increasing PS or compromising polymerization kinetics. [ABSTRACT FROM AUTHOR]

To measure temperature effects on stickiness and packability of representative resin-based composites and the effect of pre-heating time on pre-cure properties of Viscalor , including extrusion force. Five resin-based composites (RBC) and an additional RBC, Viscalor, used with a Caps Warmer (VOCO, Germany) were studied. The extrusion force (N) and extruded mass (g) were measured from Viscalor compules heated in T3 mode for 30 s (T3-30 s) and 3 min (T3-3 min). For stickiness and packability measurements, RBCs were packed into a brass cylindrical cavity controlled at 22 and 37 °C. A flat-ended probe was lowered into the RBC pastes at constant speed. Stickiness: F max (N) and W s (N mm), and packability: F p (N), were measured. Viscalor was LED photo-cured at 1200 mW/cm2 for 40 s. The degrees of conversion at 5 min and 24 h post cure (DC 5min and DC 24h) of Viscalor (no heat, T3-30 s and T3-3 min) were measured by ATR-FTIR. Data were analysed by one-way ANOVA, independent t -test and Tukey post-hoc tests (p < 0.05). The maximum temperature of the Caps Warmer, in T3 mode, reached 68 °C in 20 min. Viscalor temperatures of 34.5 °C and 60.6 °C were recorded after 30 s and 3 min pre-heating, respectively. Pre-heating significantly reduced extrusion force and increased extruded mass, especially after 3 min. RBCs varied in F max , W s and F p (p < 0.05). Temperature also affected F max (p = 0.000), W s (p = 0.002) and F p (p = 0.000). Pre-heating Viscalor for either 30 s or 3 min did not increase the post-cure DC at either 5 min or 24 h, relative to no pre-heating (p > 0.05). The composites varied to an extent in stickiness and packability but the overall magnitudes remained within a clinically acceptable range. Pre-heating was beneficial in placement of Viscalor and caused no adverse effects through premature polymerization. [ABSTRACT FROM AUTHOR]

[Display omitted] • Aerotolerant bioanode was constructed with small external resistance or high shear stress. • Aerotolerant biofilm was flat and compact with a large laminar boundary layer for reducing oxygen diffusion. • Compact electrode assembly MFC with aerotolerant bioanode achieved a power density of 4300 mW/m2. Reducing electrode spacing is considered the most effective way to maximize the power density of microbial fuel cell (MFC) due to the effective reduction of internal resistance. However, reducing electrode spacing presents the challenge of diffusing oxygen from the cathode to the anode, thereby reducing anode performance. Constructing aerotolerant anode may be one of the effective ways to minimize the effect of oxygen on anode performance. Here, we report that using small external resistance or maintaining high nitrogen sparging rate during bioanode enrichment could significantly enhance the aerotolerant characteristic of the bioanode. The voltage loss affected by oxygen was significantly reduced and, as a result, the power density of the compact electrode assembly MFC increased by 85% reaching 4300 mW/m2. The aerotolerant bioanode had a flat and compact biofilm structure and a larger laminar boundary layer (over 300 μm) to reduce oxygen diffusion, while the aerointolerant bioanode had a rough and porous biofilm structure and a smaller laminar boundary layer (less than 80 μm). The microbial community of all the bioanodes was dominated by Geobacter (over 55%). Therefore, this study provides a sample way to construct an aerotolerant bioanode for high power generation in MFCs. [ABSTRACT FROM AUTHOR]

• Surface horizon delineation affect topsoil organic carbon density (TSOCD) results. • TSOCD rises with an increase in the thickness of surface horizon delineation. • Diagnostic surface horizon (DSH) proven to be effective in assessing the TSOCD. • Conventional surface horizon (CSH) methods may incorrectly assess the TSOCD. • Some CSH-based thicknesses are recommended under certain conditions. Accurate assessment of the topsoil organic carbon density (TSOCD) is of great importance for the management and conservation of the topsoil organic carbon pools. The conventional surface horizon (CSH) delineation method, which involves specifying a uniform topsoil thickness for all sampling points across a study area, carries the potential risk of inaccurate TSOCD estimation. Chinese soil scientists developed the definition and determination criteria for diagnostic surface horizon (DSH) in Chinese Soil Taxonomy (CST), which were based on the distinctive characteristics of topsoil in China. This study employed a DSH-based topsoil classification method to evaluate the TSOCD of China, based on the findings of the recent national soil survey. Also, TSOCD was calculated for all sample points based on the CSH method, where the thickness of CSH was defined as 5, 10, 15, 20, 30, and 60 cm from the surface. The results showed that DSH-based TSOCD in China ranged from 0.03 to 74.63 kg m−2 with a mean value of 4.34 kg m−2. The standardized topsoil delineation based on the DSH method shows great potential for improving the accuracy of TSOCD assessment and different DSH types exhibited very significant differences in TSOCD (p < 0.01). There was a significant difference between the TSOCD results based on the six CSH delineation thicknesses (p < 0.01). Excessive or insufficient uniform CSH thickness settings may result in over- or underestimation of TSOCD values in comparison to DSH. This study provides evidence that specific uniform thickness delineations are suitable for particular soil types, climatic conditions, and land use patterns, and that the mean TSOCD results derived from these delineations do not significantly differ (p < 0.01) from those based on DSH. The findings of this study have the potential to support the development of more accurate surface horizon delineation methods for managing and making decisions related to topsoil organic carbon pools. [ABSTRACT FROM AUTHOR]

Inoculum is critical for the start-up and performance of microbial fuel cells (MFCs). The effluent of mature MFCs is usually used as inoculum for the start-up of immature MFCs. However, the effluent of mature MFCs contains bacteria both from anode biofilm (ASB) and cathode biofilm (CSB). Here, ASB and CSB and their mixtures were used as inoculum in the start-up of MFCs in order to gain more insight into the influence of CSB on the start-up of MFCs. Compared to anode inoculum-enriched MFCs, using cathode inoculum reduced start-up time from 5 d to 3 d. The time needed for scavenging oxygen was reduced from 900 min to 600 min, maximum power density was 19% lower (691 mW/m2 vs 823 mW/m2), and the charge transfer resistance increased from 29.0 Ω to 48.3 Ω. The decreased start-up time and power generation of cathode inoculum-enriched MFCs was attributed to the increasing abundance of Azospirillum (80.02% vs. 12.68%) and the decreasing abundance of Geobacter (9.08% vs. 61.25%). This research suggested that CSB in the effluent of mature MFCs, when used as inoculum, has a side-effect on the start-up of MFCs. [ABSTRACT FROM AUTHOR]

Abstract Hydrogels have been developed since the 1960s for applications in personal care, medicine, and engineering. Evidence has accumulated that hydrogels under prolonged loads suffer fatigue. Symptoms include change in properties, as well as nucleation and growth of cracks. This article is the first review on the fatigue of hydrogels. Emphasis is placed on the chemistry of fatigue—concepts and experiments that link symptoms of fatigue to processes of molecules. Symptoms of fatigue are characterized by testing samples with and without precut cracks, subject to prolonged static and cyclic loads. We describe the use of energy release rate for samples with precut cracks, under the conditions of large-scale inelasticity, for hydrogels of complex rheology. Highlighted are three experimental setups: pure shear, tear, and peel, where energy release rate is readily obtained for materials of arbitrary rheology. We describe chemistries of bonds and topologies of networks. Noncovalent bonds and some covalent bonds are reversible: they reform after breaking under relevant conditions. Most covalent bonds are irreversible. Each topology of networks is a way to connect reversible and irreversible bonds. We review experimental data of hydrogels of five representative topologies of networks. We compare the Lake-Thomas threshold, the cyclic-fatigue threshold, and the static-fatigue threshold. Fatigue is a molecular disease. All symptoms of fatigue originate from one fundamental cause: molecular units of a hydrogel change neighbors under prolonged loads. Fatigue correlates with rheology, according to which we distinguish poroelastic fatigue, viscoelastic fatigue, and elastic-plastic fatigue. Many hydrogels have sacrificial bonds that act as tougheners. We distinguish tougheners of two types according to their stress-relaxation behavior under a prolonged static stretch. A liquid-like toughener relaxes to zero stress, and increases neither static-fatigue threshold nor cyclic-fatigue threshold. A solid-like toughener relaxes to a nonzero stress, increases static-fatigue threshold, but does not increase cyclic-fatigue threshold. We outline a strategy to create hydrogels of high endurance. Because of the molecular diversity among hydrogels, the chemistry of fatigue holds the key to the discovery of hydrogels of properties previously unimagined. It is hoped that this review helps to connect chemists and mechanicians. Highlights • Symptoms of fatigue, and use of energy release rate for large-scale inelasticity and complex rheology. • Chemistries of bonds, topologies of networks, and data of five representative hydrogels. • Lake-Thomas threshold, cyclic-fatigue threshold, and static-fatigue threshold of hydrogels with liquid-like and solid-like tougheners. • Poroelastic fatigue, viscoelastic fatigue, and elastic-plastic fatigue. • Strategy for hydrogels of high endurance. [ABSTRACT FROM AUTHOR]

A plane strain model, an axisymmetric model and a lumped-pass three-dimensional (3D) model were employed to simulate the residual stresses in a narrow-gap welding 300 mm-thick curved steel mockup. The lumped-pass 3D model was built with the lumping scheme based on the two-dimensional (2D) temperature field result. The residual stresses on the top surface of the mockup were measured using the hole-drilling method and compared with the simulated stresses. Results show that the plane strain model cannot capture the asymmetric conditions of the curved mockup; the through-thickness transverse stress within the weld zone presents a self-equilibrated distribution, and the through-thickness hoop stress within the weld zone are almost tensile with the peak value occurring at a certain depth (8 mm) beneath the top and bottom surfaces. [ABSTRACT FROM AUTHOR]