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The enhancement of photo-Fenton performance in BiOBr via a synergistic approach of efficient adsorption and reduced recombination of photogenerated carriers is an innovative strategy. This research employs an in-situ precipitation process to incorporate modified TiO₂ onto the surface of BiOBr, while integrating polyethylene glycol to create an S-type heterojunction material (FT-OVs/PEG-BiOBr). The incorporation of hydroxyl groups by PEG facilitates π-π conjugation and hydrogen bonding, thereby enhancing the adsorption capacity of FT-OVs/PEG-BiOBr. The presence of oxygen vacancies extends the absorption spectrum into the infrared region, and the electron trap mechanism substantially decreases the recombination of photogenerated carriers. The developed S-type heterojunction structure efficiently directs electron transport along a specific pathway. Photoluminescence (PL) studies reveal a further reduction in the recombination of photogenerated carriers. Remarkably, there is a significant synergistic effect between adsorption and low recombination of photogenerated carriers. A profound photo-Fenton synergistic effect is observed at pH 3, leading to degradation rates of RhB and TCH of approximately 98% and 85%, respectively, within 10 min. The degradation pathway of RhB has been investigated using LC-MS analysis, and 1O₂ has been identified as the primary active species through ESR testing. This study presents a novel structural approach to significantly enhance the efficiency of catalysts in Fenton systems. [ABSTRACT FROM AUTHOR]

The precise theranostics of rheumatoid arthritis (RA) remains a formidable challenge in clinical practice. Exploring novel applications of contemporary therapeutic approaches like chemo‐radiotherapy is promising as a highly effective strategy for RA. Herein, a novel activatable nanoradiosensitizer‐40 (denoted as IRnR‐40) is developed, based on encapsulating the clinically approved drugs cisplatin (DDP) and indocyanine green (ICG) within a gelatin shell to achieve second near‐infrared fluorescence (NIR‐II FL) imaging‐guided safe‐dose synergetic chemo‐radiotherapy. The high concentration of matrix metalloproteinase‐9 (MMP‐9) in the RA microenvironment plays a pivotal role in triggering the responsive degradation of IRnR‐40, leading to the rapid release of functional molecules DDP and ICG. The released ICG serves the dual purpose of illuminating the inflamed joints to facilitate accurate target volume delineation for guiding radiotherapy, as well as acting as a real‐time reporter for quantifying the release of DDP to monitor efficacy. Meanwhile, the released DDP achieves highly effective synergistic chemotherapy and radiosensitization for RA via the dual reactive oxygen species (ROS)‐mediated mitochondrial apoptotic pathway. To sum up, this activatable nanoradiosensitizer IRnR‐40 is believed to be the first attempt to achieve efficient NIR‐II FL imaging‐guided safe‐dose chemo‐radiotherapy for RA, which provides a new paradigm for precise theranostics of refractory benign diseases. [ABSTRACT FROM AUTHOR]

A novel elastomer-based composite material with enhanced piezoelectric performances is proposed in this paper, which is composed of the top and bottom polytetrafluoroethylene (PTFE) solid films with the middle PTFE nanoparticle–polydimethylsiloxane (PDMS) sponge layer. To enhance the charge retention capability of elastomers, PTFE nanoparticles are introduced to form PTFE–PDMS interfaces, which can trap charges with longevity. Besides, PTFE solid films take on the role of the charge blocking layers to further improve the piezoelectric performances. As a result, the PTFE–PDMS sandwich structure shows the advantages of remarkable sensitivity (1053 pC/N), high stability, and flexibility. After a 6 h of annealing treatment at the temperature of 100 °C, no significant deterioration of the piezoelectric properties can be observed, which reveals the great thermal stability of the sandwich structure. In addition, the sandwich structure can be immersed in water for 24 h without any loss of piezoelectric activity. Finally, the experiment of lighting one LED by hand pressing successfully demonstrates that the sandwich structure has good applicability in the field of energy harvesting. Considering the excellent electrical and mechanical features, the PTFE–PDMS sandwich structure has promising applications in sensing, energy harvesting, and actuation. [ABSTRACT FROM AUTHOR]

This paper presents a multi-algorithm fusion model (StackingGroup) based on the Stacking ensemble learning framework to address the variable selection problem in high-dimensional group structure data. The proposed algorithm takes into account the differences in data observation and training principles of different algorithms. It leverages the strengths of each model and incorporates Stacking ensemble learning with multiple group structure regularization methods. The main approach involves dividing the data set into K parts on average, using more than 10 algorithms as basic learning models, and selecting the base learner based on low correlation, strong prediction ability, and small model error. Finally, we selected the grSubset + grLasso, grLasso, and grSCAD algorithms as the base learners for the Stacking algorithm. The Lasso algorithm was used as the meta-learner to create a comprehensive algorithm called StackingGroup. This algorithm is designed to handle high-dimensional group structure data. Simulation experiments showed that the proposed method outperformed other R2, RMSE, and MAE prediction methods. Lastly, we applied the proposed algorithm to investigate the risk factors of low birth weight in infants and young children. The final results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.508 and a root mean square error (RMSE) of 0.668. The obtained values are smaller compared to those obtained from a single model, indicating that the proposed method surpasses other algorithms in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Thermal transport within nanostructures is highly confined by interfaces, and non-trivial physics can emerge at boundaries. Theoretical studies have shown that different phonon modes can exhibit varying thermal resistances at interfaces. Experimental observation of these variations, however, is lacking. Using the steady-state Raman thermometry, the E 2 g 1 and A1g vibrational modes of MoS2 were utilized to characterize the thermal transport properties across the MoS2/Si interface. Our results revealed distinct temperature rises associated with different modes, indicating various mode contributions in the interfacial thermal conductance. Combining experimental and numerical simulations, the out-of-plane mode in MoS2 was found to contribute less to the interfacial transport, by 21.5%, attributed to the less variational mode mismatch of the in-plane phonon, compared to the in-plane mode. Furthermore, our results confirmed a 26.9% higher thermal conductivity from the out-of-plane mode than the in-plane one. [ABSTRACT FROM AUTHOR]

The detection and feedback of displacement and velocity significantly impact the control accuracy of the linear feed system. In this study, we propose a flexible and self-powered displacement sensor based on the triboelectric effect, designed for seamless integration into linear feed systems. The displacement sensor comprises two parts, the mover and stator, operating in a sliding mode. This sensor can precisely detect the displacement of the linear feed system with a large detection range. Additionally, the sensor is capable of real-time velocity detection of linear feed systems, with an error rate below 0.5%. It also offers advantages, such as excellent flexibility, compact size, stability, easy fabrication, and seamless integration, with linear feed systems. These results highlight the potential of the self-powered displacement sensor for various applications in linear feed systems. [ABSTRACT FROM AUTHOR]

Grouted sleeves (GSs) are a type of precast joint that can effectively connect steel rebars with excellent performance. However, the grouting debonding problem, which can occur due to the leakage of the glue plug, can seriously affect the properties of GSs. In this paper, a guided-wave-based structural health monitoring (SHM) method is used to detect debonding in GSs. The axisymmetric longitudinal mode is selected as the incident wave since it is sensitive to axial damage. Eight piezoelectrics (PZTs) are then symmetrically installed to actuate signals. The proposed samples are GSs with four different debonding sizes. First, the relationship between the arrival time of the first wave packet and the debonding size is explored through theoretical derivation. The arrival time decreases linearly with an increasing debonding size. A similar trend is observed when the relationship is verified via a numerical simulation and experimental results. This method will provide a reference for detecting debonding in similar GS multilayer structures. [ABSTRACT FROM AUTHOR]

This study introduces methodologies for constructing joint probability distribution functions utilizing the Copula function and neural networks, and evaluates their efficacy in marine and civil engineering projects. Through an analytical comparison of both models using a numerical example, it is revealed that the neural network model exhibits superior adaptability to large sample sizes. This adaptability is attributed to the neural network's ability to learn complex relationships within the data, which is especially beneficial when dealing with large datasets. The neural network model also demonstrates higher accuracy in constructing joint probability distribution functions compared to the Copula function model. In marine and civil engineering, the adaptability and accuracy of neural networks are of paramount importance due to the variable and complex nature of weather patterns. A practical engineering application is presented, wherein a joint probabilistic distribution neural network model of wind velocity and rain intensity is established for the Lanzhou–Xinjiang high-speed railroad in China. This model illustrates the promising application of neural networks in engineering projects where weather factors play a critical role. Subsequent to the construction of the joint probability distribution functions, a feature importance analysis is incorporated to quantify the contribution of different weather parameters such as wind velocity and rain intensity to the joint distribution function. This analysis provides an objective assessment of the relative importance of various weather factors and offers data-driven insights that are essential for engineering applications where weather conditions are a significant consideration. The study concludes by highlighting the potential benefits of neural network models in marine and civil engineering, suggesting areas for future exploration. [ABSTRACT FROM AUTHOR]

Steel slag is a solid waste produced in crude steel smelting, and a typical management option is stockpiling in slag disposal yards. Over the years, the massive production of steel slags and the continuous use of residue yards have led to vast occupation of land resources and caused severe environmental concerns. Steel slag particles can potentially be used as aggregates in concrete production. However, the volume stability of steel slag is poor, and the direct use of untreated steel slag aggregate (SSA) may cause cracking and spalling of concrete. The present research summarizes, analyzes, and compares the chemical, physical, and mechanical properties of steel slags. The mechanism and treatment methods of volume expansion are introduced, and the advantages, disadvantages, and applicable targets of these methods are discussed. Then, the latest research progress of steel slag aggregate concrete (SSAC) is reviewed. Using SSA leads to an increase in the density of concrete and a decrease in workability, but the mechanical properties and durability of SSAC are superior to natural aggregate concrete (NAC). Finally, future research in this field is proposed to motivate further studies and guide decision-making. [ABSTRACT FROM AUTHOR]

Yangmai-13 (YM13) is a wheat cultivar with weak gluten fractions. In contrast, Zhenmai-168 (ZM168) is an elite wheat cultivar known for its strong gluten fractions and has been widely used in a number of breeding programs. However, the genetic mechanisms underlying the gluten signatures of ZM168 remain largely unclear. To address this, we combined RNA-seq and PacBio full-length sequencing technology to unveil the potential mechanisms of ZM168 grain quality. A total of 44,709 transcripts were identified in Y13N (YM13 treated with nitrogen) and 51,942 transcripts in Z168N (ZM168 treated with nitrogen), including 28,016 and 28,626 novel isoforms in Y13N and Z168N, respectively. Five hundred and eighty-four differential alternative splicing (AS) events and 491 long noncoding RNAs (lncRNAs) were discovered. Incorporating the sodium-dodecyl-sulfate (SDS) sedimentation volume (SSV) trait, both weighted gene coexpression network analysis (WGCNA) and multiscale embedded gene coexpression network analysis (MEGENA) were employed for network construction and prediction of key drivers. Fifteen new candidates have emerged in association with SSV, including 4 transcription factors (TFs) and 11 transcripts that partake in the post-translational modification pathway. The transcriptome atlas provides new perspectives on wheat grain quality and would be beneficial for developing promising strategies for breeding programs. [ABSTRACT FROM AUTHOR]

Biodegradable polylactic acid (PLA) foams with open-cell structures are good candidates for oil–water separation. However, the foaming of PLA with high-expansion and uniform cell morphology by the traditional supercritical carbon dioxide microcellular foaming method remains a big challenge due to its low melting strength. Herein, a green facile strategy for the fabrication of open-cell fully biodegradable PLA-based foams is proposed by introducing the unique stereocomplexation mechanism between PLLA and synthesized star-shaped PDLA for the first time. A series of star-shaped PDLA with eight arms (8-s-PDLA) was synthesized with different molecular weights and added into the PLLA as modifiers. PLLA/8-s-PDLA foams with open-cells structure and high expansion ratios were fabricated by microcellular foaming with green supercritical carbon dioxide. In detail, the influences of induced 8-s-PDLA on the crystallization behavior, rheological properties, cell morphology and consequential oil–water separation performance of PLA-based foam were investigated systemically. The addition of 8-s-PDLA induced the formation of SC-PLA, enhancing crystallization by acting as nucleation sites and improving the melting strength through acting as physical cross-linking points. The further microcellular foaming of PLLA/8-s-PDLA resulted in open-cell foams of high porosity and high expansion ratios. With an optimized foaming condition, the PLLA/8-s-PDLA-13K foam exhibited an average cell size of about 61.7 μm and expansion ratio of 24. Furthermore, due to the high porosity of the interconnected open cells, the high-absorption performance of the carbon tetrachloride was up to 37 g/g. This work provides a facile green fabrication strategy for the development of environmentally friendly PLA foams with stable open-cell structures and high expansion ratios for oil–water separation. [ABSTRACT FROM AUTHOR]

To improve the rigidity of large composite fuselage panels and reduce the deformation caused by factors such as gravity during the assembly locating process, this paper presents a multi-objective optimisation method of the discrete fixturing layout based on the integration of a back-propagation neural network (BPNN) with the elitist nondominated sorting genetic algorithm (NSGA-II), which considers the characteristics of the composite fuselage panel, such as its complex structure and high tendency to undergo damage when subjected to force, in addition to the control the fixturing system cost. This method can simultaneously determine the optimal number and positions of tooling fixturing points. First, a binary integer variable is proposed to represent the fixturing layout, to realise the coupling representation of the number and positions of the fixturing points. To improve the optimisation efficiency, after comprehensively comparing the prediction accuracies of several commonly used surrogate models, a BPNN prediction model is constructed to describe the nonlinear mapping relationship between the fixturing layout and the maximum deformation (maximum Mises stress) of the composite fuselage panel based on the limited samples obtained by finite element analysis (FEA) and minimax Latin hypercube sampling (MLHS). Thereafter, the allowable deformation of the composite fuselage panel is considered the constraint; the number of fixturing points and the stress of the panel are used as the optimisation objectives. The Pareto solution set of the fixturing layout is determined using NSGA-II. Finally, a composite front fuselage panel case study of a wide-body airliner is considered to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences, namely translating sequence to preference. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shared dictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) a posterior distribution estimator using a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user's dynamic preferences conditioned on his/her past behaviors. Qualitative studies on the Netflix dataset demonstrate that the proposed method can capture the user preference drifts over time and quantitative studies on multiple real-world datasets demonstrate that the proposed method can achieve higher accuracy compared with state-of-the-art factorization and neural sequential recommendation methods. [ABSTRACT FROM AUTHOR]

Titanium alloy extruded profiles with complex cross-section are usually used to manufacture special-shaped structural parts in the aviation industry. In this paper, a Ti-6Al-4 V profile with asymmetric Y shape was manufactured by a direct extrusion process. A comparative study was conducted at the different lubrication conditions, in which the different process deformation mechanisms, resulting microstructures, and mechanical properties of profiles have been investigated by finite element simulation and a series of experiments, i.e., scanning electron microscope (SEM) and electron backscatter diffraction (EBSD). It was revealed that the metal flow is blocked at the narrow die orifice due to the deterioration of lubrication, resulting in the continuous shrinkage defect. The metal deformation coordination mechanism becomes gradually significant due to poor friction contact, which promotes the internal stress in the cross-sectional center to transfer outward to balance the friction at the edge of the orifice, resulting in the reduction of the effective strain in the center. The heat generated by friction weakens the heat exchange behavior between the metal and the die, which helps the profile retain a more uniform temperature field. In the sectional center of the profiles made from the two different lubrications, extrusion with good lubrication results in greater grain/colony refinement due to greater effective strains, and a slightly greater dislocation density was found due to the uniform deformation. The tensile tests revealed that compared with poor lubrication, due to the improvement of Hall–Petch strengthening and dislocation strengthening in the cross-sectional center with good lubrication, the variation range of yield strength and tensile strength is reduced to 11 MPa and 7 MPa, respectively, and the variation range of microhardness is reduced to 44 HV and 32 HV, respectively. The improvement of lubrication not only helps to reduce forming defects but also can obtain uniform mechanical properties. [ABSTRACT FROM AUTHOR]

Summary: The surface defect detection of welded rebar is an important part of construction handover inspection, which is mainly done manually. To improve the efficiency and objectivity of this work, deep learning‐based defect detection methods can be used, but surface defects of welded rebar have the characteristics of insignificant pixel change and small defect areas. Therefore, this paper proposes a high‐frequency feature enhanced network (HFFENet) for detecting defects on the welded surface of rebar. The proposed method uses an autoencoder as the base network and inserts a high‐frequency feature enhanced module (HFFEM). The module extracts high‐frequency components of low‐level features by utilizing discrete cosine transform (DCT) and transforming them into feature enhanced coding, which is fused into the upsampling layers by means of concatenation. The proposed method is tested with a welded rebar surface defect dataset, and the results show that the proposed method can achieve a mIoU of 86.2%. Finally, it is experimentally demonstrated that high‐frequency features are more important for defect detection tasks of small areas and more widely distributed pixel histograms. [ABSTRACT FROM AUTHOR]

Summary: Digital twin bridges are virtual replicas of real physical entity bridges in computers. A digital twin bridge in the form of a finite element model can help in making sense of the structural responses monitored by the structure health monitoring system. This study proposes the structure health hybrid monitoring method, which provides a mean for synthesizing monitoring data and finite element model to reconstruct the un‐monitoring structure responses, in developing a digital twin of a cable‐stayed bridge. The considered structure is the orthotropic steel deck, in which the welding residual stress is an important cause of fatigue cracking. The submodel technology is employed to study the distribution characteristics of welding residual stress and the coupling effect with vehicle‐induced stress and temperature‐induced stress near the weld in the U‐ribs to top deck joint in orthotropic steel deck. Aiming at the defect that cannot consider welding residual stress for the S‐N curves based on fatigue evaluation and life prediction method, a nonlinear fatigue damage model based on the continuum damage mechanics, which has been verified by orthotropic steel deck fatigue test, is employed to evaluate the fatigue performance of U‐ribs to top deck joint in orthotropic steel deck for an in‐service cable‐stayed bridge. [ABSTRACT FROM AUTHOR]

• Anthropogenic groundwater recharge altered the recharge source of FSGD. • The flow pathways of SGD were significantly impacted by anthropogenic activities. • The contribution ratio of seawater exceeds 60% in most intertidal zones. • Stable isotopic is an effective tool for identifying the process of SGD. Submarine groundwater discharge (SGD) is recognised as a crucial factor influencing coastal ecosystems and biogeochemical processes. Anthropogenic activities not only discharge a large amount of nutrients and pollutants into oceans but also alter the hydrological processes of SGD. Changes in the recharge source and flow pathway of submarine groundwater under anthropogenic activities were revealed in this study by investigating the spatial and temporal isotopic, geochemical and hydrometric characteristics of various water types. The interaction between salt water and fresh water in the intertidal zone was also explored based on the law of isotopic mass conservation and the model of end-member mixing. Our results indicated that the shallow groundwater at depths of 4 m and 13 m is mainly recharged by local precipitation. The fresh submarine groundwater, river water, lake water and terrestrial groundwater at depths of 23 m and 60 m are largely recharged by the Yangtze River. The fresh submarine groundwater is mainly affected by artificially recharged with water from the Yangtze River, and its recharge pathway is relatively deep due to the impact of dam construction. Seawater contributes to more than 60 % of the SGD in most intertidal zones through the discharge processes of mixed salt water and seawater in the upper saline plume. Our study contributes to an improved understanding of anthropogenic impacts on SGD and has crucial implications for accurately tracing and quantifying the hydrological processes of SGD. [ABSTRACT FROM AUTHOR]

Currently, it has long been considered a challenge to provide sustainable additives for polylactide (PLA) in green way to endow it excellent comprehensive properties. Given the flammability and unsatisfactory crystallization performance of PLA, a furan-based phosphate furfurylamine trimethylphosphate (FATMP) was synthesized from 2-furfurylamine and amino trimethylphosphonic acid by a simple hydration reaction, and the PLA/FATMP composites were prepared by melting blending process. The tensile performance, crystallization behaviors, flame retardancy, and flame-retardant mechanism received special attention. Results showed that the incorporation of only 3 wt% FATMP could indeed increase the LOI value of PLA from 19.8 to 27.3 %, and simultaneously acquired V-0 rating in the vertical burning test owing to the favorable synergistic effect between the vapor phase and the condensed phase. Additionally, the half-crystallization time of PLA was decreased from 12.4 to 5.1 mins with the addition of FATMP, which acted as a nucleating agent. More appealingly, the tensile performance of PLA/FATMP composites was also well maintained. In general, the PLA/FATMP composites we proposed could be promising candidates in application fields where favorable flame retardancy and crystallization ability are required. [Display omitted] • A novel renewable furan-based phosphate furfurylamine trimethylphosphate (FATMP) was synthesized. • FATMP effectively enhanced flame retardancy and crystallization rate of polylactide (PLA) simultaneously. • PLA/FATMP composites retained favorable tensile performance. [ABSTRACT FROM AUTHOR]

Objective. To detect EGFR/KRAS genes in pleural effusion cell-free DNA in patients with advanced non-small-cell lung cancer (NSCLC) and to explore the clinical significance of EGFR/KRAS mutation status in pleural effusion. Methods. A retrospective collection was performed on the specimens of pleural effusion and matched tissues from 50 patients with advanced NSCLC admitted to the hospital between January 2019 and January 2021. DNA mutation status of EGFR/KRAS in different specimens was detected and compared by pyrosequencing. The clinicopathological data and follow-up data of survival were collected. The relationship between DNA mutation and clinicopathological characteristics and prognosis was analyzed. Results. In the 50 pleural effusion specimens, there were 22 cases (44.00%) with EGFR mutations (19/21 exon mutations), including 12 cases with EGFR19 deletion mutation and 10 cases with EGFR21 exon L858R mutation. There were 6 cases (12.00%) with KRAS mutations (single-base substitution mutations), including 4 cases with 12-codon mutation and 2 cases with 13-codon mutation. In the 50 tissue specimens, there were 24 cases (48.00%) with EGFR mutations and 4 cases (8.00%) with KRAS mutations. There was no significant difference between pleural effusion specimens and tissue specimens, with good consistency (kappa = 0.920–0.779, P > 0.05). EGFR mutation in pleural effusion was related to smoking history, types of pathological tissues, and lymph node metastasis (P < 0.05). The incidence of EGFR mutation was higher in nonsmokers, patients with lung adenocarcinoma, and patients with lymph node metastasis. The carcinoembryonic antigen (CEA) in patients with EGFR mutation was higher than that with wild-type EGFR, while the level of cytokeratin 19 fragment (Cy21-1) was lower than that with wild-type EGFR (P < 0.05). The 1-year overall survival rate in the EGFR mutation group was significantly higher than that in the EGFR wild group (68.18% vs. 42.86%) (HR = 0.419, 95% CI = 0.178–0.989, and P < 0.001). Conclusion. For the detection of EGFR gene mutation, the results of the pleural effusion specimens and the tumor pathological tissue specimens were well consistent and the detection of pleural effusion could be used as an alternative method when tissue specimens cannot be obtained. EGFR gene mutations are present in majority in patients with advanced NSCLC. The incidence of EGFR mutation is higher in nonsmokers, patients with lung adenocarcinoma, those with lymph node metastasis, those with high-expression CEA, and those with low-expression Cy21-1. The prognosis is better in patients with EGFR mutation. [ABSTRACT FROM AUTHOR]

Because of the emerging variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in different regions of the world, the battle with infectious coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has been seesawing. Therefore, the identification of antiviral drugs is of particular importance. In order to rapidly identify inhibitors for SARS-CoV-2 3-chymotrypsin-like protease (3CLpro), an enzyme essential for viral replication, we combined the fluorescence polarization (FP) technique with biotin-avidin system (BAS) and developed a novel sandwich-like FP screening assay. Through high-throughput screening, two hits of 3CLpro inhibitors, ginkgolic acid (GA) and anacardic acid (AA) were identified, which showed IC50 values of 11.29 ± 0.48 and 12.19 ± 0.50 μM, respectively. Their binding modes were evaluated by HPLC-Q-TOF–MS. There was no mass increase detected for SARS-CoV-2 3CLpro incubated with either GA or AA, indicating the absence of covalent adducts. The kinetic analysis clearly demonstrated that both GA and AA inhibit SARS-CoV-2 3CLpro via reversible and mixed-inhibition manner. Our results argue against conclusion that GA and AA act as irreversible and covalent inhibitors against SARS-CoV-2 3CLpro, which is based on the studies by Chen et al. [ABSTRACT FROM AUTHOR]

Sign-based Stochastic Gradient Descents (Sign-based SGDs) use the signs of the stochastic gradients for communication costs reduction. Nevertheless, current convergence results of sign-based SGDs applied to the finite sum optimization are established on the bounded assumption of the gradient, which fails to hold in various cases. This paper presents a convergence framework about sign-based SGDs with the elimination of the bounded gradient assumption. The ergodic convergence rates are provided only with the smooth assumption of the objective functions. The Sign Stochastic Gradient Descent (signSGD) and its two variants, including majority vote and zeroth-order version, are developed for different application settings. Our framework also removes the bounded gradient assumption used in the previous analysis of these three algorithms. [ABSTRACT FROM AUTHOR]

Structural response prediction with desirable accuracy is considerably essential for the health monitoring of bridges. However, it appears to be difficult in accurately extracting structural response features on account of complex on-site environment and noise disturbance, resulting in poor prediction accuracy of the response values. To address this issue, a Transformer-based bridge structural response prediction framework was proposed in this paper. The framework contains multi-layer encoder modules and attention modules that can precisely capture the history-dependent features in time-series data. The effectiveness of the proposed method was validated with the use of six-month strain response data of a concrete bridge, and the results are also compared with those of the most commonly used Long Short-Term Memory (LSTM)-based structural response prediction framework. The analysis indicated that the proposed method was effective in predicting structural response, with the prediction error less than 50% of the LSTM-based framework. The proposed method can be applied in damage diagnosis and disaster warning of bridges. [ABSTRACT FROM AUTHOR]

The ability to discern subtle image changes over time is useful in applications such as product quality control, civil engineering structure evaluation, medical video analysis, music entertainment, and so on. However, tiny yet useful variations are often combined with large motions, which severely distorts current video amplification methods bounded by external constraints. This paper presents a novel use of spectra to make motion magnification robust to large movements. By exploiting spectra, artificial limitations and the magnification of small motions are avoided at similar frequency levels while ignoring large ones at distinct spectral pixels. To achieve this, this paper constructs spline-kerneled chirplet transform (SCT) into an empirical Bayesian paradigm that applies to the entire time series, giving powerful spectral resolution and robust performance to noise in nonstationary nonlinear signal analysis. The important advance reported is Bayesian-rule embedded SCT (BE-SCT); two numerical experiments show its superiority over current approaches. For applying to spectrum-aware motion magnification, an elaborate analytical framework is established that captures global motion, and use of the proposed BE-SCT for dynamic filtering enables a frequency-based motion isolation. Our approach is demonstrated on real-world and synthetic videos. This approach shows superior qualitative and quantitative results with less visual artifacts and more local details over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the time periodic pulse electroosmotic flow (EOF) of Jeffreys fluids through a parallel plate microchannel. The driving mode of pulse EOF here is considered as a rectangle pulse. Using the method of Laplace transform, the analytical solution of velocity and volumetric flow rate of pulse EOF is derived. The effects of pertinent dimensionless parameters on the velocity and volumetric flow rate are discussed in detail. Results show that the pulse width a¯$\bar a$ is a valuable parameter for analyzing the influence of relaxation time λ¯1${\bar \lambda _1}$ and retardation time λ¯2${\bar \lambda _2}$ on magnitude of velocity and volumetric flow rate when the fluid flow reaches the steady state. Increasing the relaxation time λ¯1${\bar \lambda _1}$ reduces he magnitude of velocity and volumetric flow rate at low value pulse width a¯$\bar a$, yet the trend is opposite for larger pulse width a¯$\bar a$. The increase of retardation time λ¯2${\bar \lambda _2}$ will reduce the magnitude of velocity and volumetric flow rate no matter whether the value of pulse width a¯$\bar a$ is large or not. Furthermore, the frequency of velocity profile and volumetric flow rate is different because of the different pulse width a¯$\bar a$. With the increase of pulse width a¯$\bar a$, the different change frequency of velocity profile and volumetric flow rate slows down, which means a long cycle time. And as the relaxation time λ¯1${\bar \lambda _1}$ and pulse width a¯$\bar a$ grow, the time required for the fluid flow to reach a steady state will become longer. The conclusions have theoretical significance for biofluid based microfluidic transport systems driven by pulsed electric field. [ABSTRACT FROM AUTHOR]

We used the Laplace transform method to investigate the time periodic pulse electroosmotic flow (EOF) of linear viscoelastic fluids through a circular microchannel driven by pulse. Among them, the viscoelastic fluids and pulse are described by Jeffrey fluids and rectangle pulse, respectively. In addition, we focus on the depletion effect caused by the interaction of Jeffrey fluids macro-molecules with the channel surface. The overall flow is divided into two layers: the depletion layer (noted as layer I) and the bulk flow outside the depletion layer (noted as layer II). And the velocity expressions of the above two layers are obtained by calculation respectively. The research results show that for any pulse width ā, the velocity in different layers will tend to a steady state with time. The larger the pulse width ā, the longer it takes to reach a steady state. In the layer II, longer retardation time λ ¯ 2 will result in the fluid taking a short time from a static state to a flowing state. The time required for the fluid in layer II to reach the flowing state is longer than that in layer I. Increasing the density ratio ρ and viscosity ratio γ will lead to the velocity amplitude to decrease. The effect of these two parameters on the velocity amplitude will gradually weaken with the passage of time. The above conclusions have certain theoretical guiding significance for the transportation of biological fluids in microfluidic systems driven by pulses. [ABSTRACT FROM AUTHOR]

We consider a class of nonconvex regularized optimization problems, which appear frequently in machine learning and data processing. Due to the structure of the problems, the iteratively reweighted algorithm was developed and applied to the consensus optimization. In this paper, we propose the acceleration of this scheme by adding an inertial term in each iteration. The proposed algorithms inherit the advantages of classical decentralized algorithms: they can be implemented over a connected network, in which the agents communicate with their neighbors and perform local computations. We also employ the diminishing stepsizes technique for the iteratively reweighted algorithm and consider its acceleration. In specific cases, our algorithms reduce to existing decentralized schemes and also indicate novel ones. Mathematically, we prove the convergence for both algorithms with several assumptions on the objective functions. With Kurdyka-Łojasiewicz property, convergence rates can be derived for constant stepsize case. Numerical results demonstrate the efficiency of the algorithms. [ABSTRACT FROM AUTHOR]

Entity resolution identifies all records in a database that refer to the same entity. In this paper, we propose an unsupervised framework for entity resolution using blocking and graph algorithms. The records are partitioned into blocks with no redundancy for efficiency improvement. For intra-block data processing, we propose a graph-theoretic fusion framework with two components, namely ITER and CliqueRank. Specifically, ITER constructs a weighted bipartite graph between terms and record-record pairs and iteratively propagates the node salience until convergence. Subsequently, CliqueRank constructs a record graph to estimate the likelihood of two records resident in the same clique. The derived likelihood from CliqueRank is fed back to ITER to rectify the edge weight until a joint optimum can be reached. Experimental evaluation was conducted with 4 real datasets. Results show that our unsupervised framework is comparable or even superior to state-of-the-art deep learning approaches. [ABSTRACT FROM AUTHOR]

[Display omitted] Heterojunction engineering has been regarded as a promising strategy to sufficiently utilizing photogenerated charge carriers, thus benefiting the improvement of photocatalytic performance. Herein, Ag 3 PO 4 /WO 3 S-scheme heterojunction was synthesized via a simple deposition-precipitation process, and its photocatalytic activity was evaluated by monitoring water splitting and pollutant degradation under visible light. As a result, Ag 3 PO 4 /WO 3 with optimized ratio photocatalyst showed enhanced photocatalytic activity in oxygen production (306.6 μmol·L-1·h−1) relative to pure Ag 3 PO 4 (204.4 μmol·L-1·h−1). Additionally, it also exhibits rapid toxicity elimination efficiency over hexavalent chromium ions (Cr6+) and ciprofloxacin (CIP) with degrading rate of 72% and 83% within 30 min, respectively. According to a series characterization, a possible S-scheme photocatalytic mechanism of Ag 3 PO 4 /WO 3 was demonstrated in detail, which endowed the heterojunction with strong redox abilities to provide powerful diving force towards the photocatalytic reaction. This work presents an innovative perspective to construct Ag 3 PO 4 -based S-scheme heterojunctions for boosting photocatalytic performance for various applications. [ABSTRACT FROM AUTHOR]

Heterojunction engineering is considered as a hopeful approach to ameliorate the separation of photogenerated carriers of photocatalysts, realizing efficient water-splitting performance. In this study, an organic-inorganic S-scheme of a one-dimensional g-C 3 N 4 nanotube (TCN)/Ag 3 PO 4 photocatalytic system with high photocatalytic water oxidation activity was designed by coupling g-C 3 N 4 nanotubes over Ag 3 PO 4 particles through a chemical coprecipitation method. The TCN/Ag 3 PO 4 heterojunction demonstrated excellent photocatalytic O 2 production with an O 2 evolution rate of up to 370.2 μmol·L−1·h−1. X-ray photoelectron spectroscopy analysis showed that electron migration between TCN and Ag 3 PO 4 led to the formation of an internal electric field pointing from TCN to Ag 3 PO 4 , which drove the S-scheme charge transfer mode between TCN and Ag 3 PO 4. Accordingly, the TCN/Ag 3 PO 4 heterojunction possessed fast charge separation and high redox ability, leading to high photoactivity and photostability. This research provides a new strategy for fabricating highly efficient inorganic-organic S-scheme photocatalysts for O 2 production. [ABSTRACT FROM AUTHOR]

The performance of photocatalysts need to be further improved in sewage treatment. In this study, polyethylene glycol (PEG) and zinc nitrate hexahydrate (Zn(NO 3) 2 •6 H 2 O) co-modified BiOBr with deposition of amorphous FeOOH (AF/ZPBB) is synthesized via precipitation-immersion method. Amazingly, the adsorption capacity of catalysts for organic pollutants is enhanced through surface hydroxyl (-OH) induced by PEG and Zn2+. Meanwhile, the photocatalytic performance of AF/ZPBB is improved by reducing its energy gap (E g) and suppressing the recombination of photogenerated carriers owing to the presence of amorphous FeOOH. Specifically, the degradation rates for rhodamine B (RhB) and tetracycline hydrochloride (TCH) are 5.5 times and 4.2 times higher than those of pure BiOBr catalyst, respectively. In addition, AF/ZPBB displays pH stability in the pH range of 2–10. After 5 cycles of reaction, only a slight catalytic performance is reduced. The reason is mainly attributed to the reduction of the -OH content, thus inhibiting the synergistic effect of adsorption and photocatalysis. Finally, •O 2 - is confirmed to be the main active species using ESR and active species capture tests. This work provides a new design approach for effectively enhancing catalytic performances of inorganic semiconductor photocatalysts. [Display omitted] • The adsorption capacity of BiOBr is enhanced induced by hydroxyl group. • The deposition of amorphous FeOOH contributes to the boosted catalytic performance. • The catalytic ability of AF/ZPBB maintains stable at pH of 2–10. [ABSTRACT FROM AUTHOR]

Catalyst deactivation and carbon loss are frequently encountered problems in furfural conversion in the aqueous phase. Although several mechanisms were proposed, an in-depth experimental study on the spent catalysts and condensates formed on catalyst surfaces is essential for the development of catalysts with high resistance to condensation. A series of Cu catalysts with various alkali or alkaline earth metal additives was synthesized and tested for furfural conversion to cyclopentanone and cyclopentanol. The addition of alkali or alkaline earth metals significantly inhibited the condensate formation, likely due to the stabilization effect of the important carbocation intermediate. Characterization techniques including FT-IR, XRD, thermogravimetric analysis, and pyrolysis GC-MS were used to analyze the spent catalysts. The results revealed that the condensates formed on the catalyst surface were highly conjugated polymers resulting from the polymerization of furfuryl alcohol. The conclusions of this research are applicable to other furfural hydroconversions in the aqueous phase in acidic or neutral environments. [ABSTRACT FROM AUTHOR]

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly transmissible and has caused a pandemic named coronavirus disease 2019 (COVID-19), which has quickly spread worldwide. Although several therapeutic agents have been evaluated or approved for the treatment of COVID-19 patients, efficacious antiviral agents are still lacking. An attractive therapeutic target for SARS-CoV-2 is the main protease (Mpro), as this highly conserved enzyme plays a key role in viral polyprotein processing and genomic RNA replication. Therefore, the identification of efficacious antiviral agents against SARS-CoV-2 Mpro using a rapid, miniaturized and economical high-throughput screening (HTS) assay is of the highest importance at the present. Results: In this study, we first combined the fluorescence polarization (FP) technique with biotin-avidin system (BAS) to develop a novel and step-by-step sandwich-like FP screening assay to quickly identify SARS-CoV-2 Mpro inhibitors from a natural product library. Using this screening assay, dieckol, a natural phlorotannin component extracted from a Chinese traditional medicine Ecklonia cava, was identified as a novel competitive inhibitor against SARS-CoV-2 Mpro in vitro with an IC50 value of 4.5 ± 0.4 µM. Additionally, dieckol exhibited a high affinity with SARS-CoV-2 Mpro using surface plasmon resonance (SPR) analysis and could bind to the catalytic sites of Mpro through hydrogen-bond interactions in the predicted docking model. Conclusions: This innovative sandwich-like FP screening assay enables the rapid discovery of antiviral agents targeting viral proteases, and dieckol will be an excellent lead compound for generating more potent and selective antiviral agents targeting SARS-CoV-2 Mpro. [ABSTRACT FROM AUTHOR]

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly transmissible and has caused a pandemic named coronavirus disease 2019 (COVID-19), which has quickly spread worldwide. Although several therapeutic agents have been evaluated or approved for the treatment of COVID-19 patients, efficacious antiviral agents are still lacking. An attractive therapeutic target for SARS-CoV-2 is the main protease (Mpro), as this highly conserved enzyme plays a key role in viral polyprotein processing and genomic RNA replication. Therefore, the identification of efficacious antiviral agents against SARS-CoV-2 Mpro using a rapid, miniaturized and economical high-throughput screening (HTS) assay is of the highest importance at the present. Results: In this study, we first combined the fluorescence polarization (FP) technique with biotin-avidin system (BAS) to develop a novel and step-by-step sandwich-like FP screening assay to quickly identify SARS-CoV-2 Mpro inhibitors from a natural product library. Using this screening assay, dieckol, a natural phlorotannin component extracted from a Chinese traditional medicine Ecklonia cava, was identified as a novel competitive inhibitor against SARS-CoV-2 Mpro in vitro with an IC50 value of 4.5 ± 0.4 µM. Additionally, dieckol exhibited a high affinity with SARS-CoV-2 Mpro using surface plasmon resonance (SPR) analysis and could bind to the catalytic sites of Mpro through hydrogen-bond interactions in the predicted docking model. Conclusions: This innovative sandwich-like FP screening assay enables the rapid discovery of antiviral agents targeting viral proteases, and dieckol will be an excellent lead compound for generating more potent and selective antiviral agents targeting SARS-CoV-2 Mpro. [ABSTRACT FROM AUTHOR]

For many data-parallel computing systems like Spark, a job usually consists of multiple computation stages and inter-stage communication (i.e., coflows). Many efforts have been done to schedule coflows and jobs independently. The simple combination of coflow scheduling and job scheduling, however, would prolong the average job completion time (JCT) due to the conflict. For this reason, we propose a new abstraction of scheduling unit, named coBranch, which takes the dependency between computation stages and coflows into consideration, to schedule coflows and jobs jointly. Besides, mainstream coflow schedulers are order-preserving, i.e., all coflows of a high-priority job are prioritized than those of a low-priority job. We observe that the order-preserving constraint incurs low inter-job parallelism. To overcome the problem, we employ an urgency-based mechanism to schedule coBranches, which aims to decrease the average JCT by enhancing the inter-job parallelism. We implement the urgency-based coBranch Scheduling (BS) method on Apache Spark, conduct prototype-based experiments, and evaluate the performance of our method against the shortest-job-first critical-path method and the FIFO method. Results show that our method achieves around 10 and 15 percent reduction in the average JCT, respectively. Large-scale simulations based on the Google trace show that our method performs better and reduces JCT by 23 and 35 percent, respectively. [ABSTRACT FROM AUTHOR]

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases. [ABSTRACT FROM AUTHOR]

In this study, both estimation and modification strategies of measurement uncertainty were investigated when using a guided waves (GWs) technique to quantify the corrosion level of a cylindrical rod induced by an accelerated setup, thus providing essential and reliable information for making decisions regarding necessary remedial work. First, the remnant diameters under corrosion were quantified based on the dispersion characteristics of GWs, and the measurement uncertainty derived from time-frequency analysis was specifically considered. After demonstrating the equivalence with the self-adaptive Monte Carlo method, the method in guide to the expression of uncertainty in measurement (GUM) was used as a more efficient approach for carrying out interval estimation of the corrosion level. Furthermore, a designed Bayesian dynamic linear model (BDLM) with a model monitoring mechanism was introduced to modify the measurement sequence. Thus, not only were more credible estimations achieved but discrepant sequences of measurement data could also be identified to avoid a misled judgement. [ABSTRACT FROM AUTHOR]

Under-allocating or over-allocating computation resources (e.g., CPU cores) can prolong the completion time of data-parallel jobs in a distributed system. We present a predictor, ReLocag, to find the near-optimal number of CPU cores to minimize job completion time (JCT). ReLocag includes a graph convolutional network (GCN) and a fully-connected network (FCNN). The GCN learns the dependency between operations from the workflow of a job, and then the FCNN takes the workflow dependency together with other features (e.g., the input size, the number of CPU cores, the amount of memory, and the number of computation tasks) as input for JCT prediction. The prediction result can guide the user to determine the near-optimal number of CPU cores. Besides, we propose two effective strategies to overcome the time-consuming issue of training sample collection in big data applications. First, we develop an adaptive sampling method to collect essential samples judiciously. Second, we further design a cross-application transfer learning model to exploit the training samples collected from other applications. We conduct extensive experiments in a Spark cluster for 7 types of exemplary Spark applications. Results show that ReLocag improves the JCT prediction accuracy by 4—14 percent. Moreover, the CPU core consumption decreases by 58.2 percent. [ABSTRACT FROM AUTHOR]

Reperfusion of blood flow during ischemic myocardium resuscitation induces ischemia/reperfusion (I/R) injury. Oxidative stress has been identified as a major cause in this process. Quercetin (QCT) is a member of the flavonoid family that exerts antioxidant effects. The aim of this study was to investigate the preventive effects of QCT on I/R injury and its underlying mechanism. To this end, H9c2 cardiomyocytes were treated with different concentrations of QCT (10, 20, and 40 μM) and subsequently subjected to oxygen-glucose deprivation/reperfusion (OGD/R) administration. The results indicated that OGD/R-induced oxidative stress, apoptosis, and mitochondrial dysfunction in H9c2 cardiomyocytes were aggravated following 40 μM QCT treatment and alleviated following the administration of 10 and 20 μM QCT prior to OGD/R treatment. In addition, OGD/R treatment inactivated ERK1/2 signaling activation. The effect was mitigated using 10 and 20 μM QCT prior to OGD/R treatment. In conclusion, these results suggested that low concentrations of QCT might alleviate I/R injury by suppressing oxidative stress and improving mitochondrial function through the regulation of ERK1/2-DRP1 signaling, providing a potential candidate for I/R injury prevention. [ABSTRACT FROM AUTHOR]

Background: Exosomes, internal proteins, lipids, and nucleic acids coated by phospholipid bilayer membranes, are one type of small extracellular vesicles, which can mediate cell-cell communication. In recent years, exosomes have gained considerable scientific interest due to their widely applied prospect in the diagnosis and therapeutics of human and animal diseases. In this study, we describe for the first time a feasible method designed to isolate and characterize exosomes from feline plasma, urine and adipose-derived mesenchymal stem cells. Results: Exosomes from feline plasma, urine and adipose-derived mesenchymal stem cells were successfully isolated by differential centrifugation. Quantification and sizing of exosomes were assessed by transmission electron microscopy, flow nano analysis and western blotting. Detected particles showed the normal size (30–100 nm) and morphology described for exosomes, as well as presence of the transmembrane protein (TSG101, CD9, CD63, and CD81) known as exosomal marker. Conclusions: The results suggest that differential centrifugation is a feasible method for isolation of exosomes from different types of feline samples. Moreover, these exosomes can be used to further diagnosis and therapeutics in veterinary pre-clinical and clinical studies. [ABSTRACT FROM AUTHOR]

Herein we report double-shell Na@Sn6L6@Sn3L3 clusters and their further assembly into a 2D layer, which belongs to a rare Sn-oxo coordination cage-based extended structure. Tetrahedral citrate ligands with multiple coordination sites play key roles in such hierarchical assembly. [ABSTRACT FROM AUTHOR]

Matrix approximation (MA) methods are integral parts of today's recommender systems. In standard MA methods, only one feature vector is learned for each user/item, which may not be accurate enough to characterize the diverse interests of users/items. For instance, users could have different opinions on a given item, so that they may need different feature vectors for the item to represent their unique interests. To this end, this article proposes a mixture matrix approximation (MMA) method, in which we assume that the user-item ratings follow mixture distributions and the user/item feature vectors vary among different stars to better characterize the diverse interests of users/items. Furthermore, we show that the proposed method can tackle both rating prediction and the top-N recommendation problems. Empirical studies on MovieLens, Netflix and Amazon datasets demonstrate that the proposed method can outperform state-of-the-art MA-based collaborative filtering methods in both rating prediction and top-N recommendation tasks. [ABSTRACT FROM AUTHOR]

Dengue virus (DENV) is spread from human to human through the bite of the female Aedes aegypti mosquito and leads to about 100 million clinical infections yearly. Treatment options and vaccine availability for DENV are limited. Defective interfering particles (DIPs) are considered a promising antiviral approach but infectious virus contamination has limited their development. Here, a DENV-derived DIP production cell line was developed that continuously produced DENV-free DIPs. The DIPs contained and could deliver to cells a DENV serotype 2 subgenomic defective-interfering RNA, which was originally discovered in DENV infected patients. The DIPs released into cell culture supernatant were purified and could potently inhibit replication of all DENV serotypes in cells. Antiviral therapeutics are limited for many viral infection. The DIP system described could be re-purposed to make antiviral DIPs for many other RNA viruses such as SARS-CoV-2, yellow fever, West Nile and Zika viruses. Li et al. produce a dengue virus (DENV)-derived defective interfering particle (DIP) production cell line that can stably produce infectious virusfree DIPs. The DENV DIPs produced from this system can potently inhibit replication of all DENV serotypes in cells and have the potential to be repurposed to make antiviral DIPs for other RNA viruses. [ABSTRACT FROM AUTHOR]

Virtual trial assembly (VTA) simulates the preassembly by verifying the correspondence of as‐built and as‐designed prefabricated component (PC) models in virtual reality. Notably, obtaining accurate as‐built PC models ensures the success of VTA. To accomplish it, current methods require matching the scanned PC data with the as‐designed model and manually modifying the model to approximate the actual dimensions, which is inefficient and leads to subjective errors. Therefore, it is desirable to develop an automatic modeling approach to obtain reliable as‐built PC models for further improving the efficiency of VTA. However, to the best of our knowledge, few studies have been done on the automatic modeling of as‐built PC models in VTA procedure. To this end, based on the prior knowledge of as‐designed model, this study proposes an approach to automatically reconstruct the primary structure via plane fitting and structural feature point calculation for geometric modeling. Then, two adaptive methods for inner boundary detection and round‐hole estimation are developed for the reconstruction of detailed structure. Finally, as‐built model visualization is realized based on the data extraction and modeling development of a building information modeling software. To verify the feasibility of the proposed approach, a validation experiment was conducted on a full‐scale precast concrete stair using two scanning resolutions (1/5 and 1/8). Experiment results show that the estimation of the dimensions consists of multifaceted corners that are stable and not affected by scanning resolution. Low scan resolution (1/8) may increase estimation errors of the edge, which has a single‐sided turning point. The experiment results also confirm the effectiveness of the proposed approach for the automatic model reconstruction of as‐built PCs. [ABSTRACT FROM AUTHOR]

Single atom catalysts (SACs) are considered as efficient catalysts for hydrogen‐based energy conversion and storage because of their excellent catalytic performance for oxygen evolution reactions (OER) and oxygen reduction reactions (ORR). In the present work, a new concept of SACs is proposed with the capacity to form d‐p orbital hybridization. These computationally designed SACs contain a metal and non‐metal pair embedded in the N‐doped graphene framework (MX@N6). Based on the overpotential evaluation by the first principle theory calculations, CoS@N6 containing Co and S atom pair possessed a low overpotential of 0.37 V/0.29 V when used as a bifunctional ORR/OER catalyst. These overpotentials are much lower than Co@N6 without S atom. The electronic structure analysis revealed that non‐metal atoms of the catalyst can regulate the electronic structure of active metal sites and facilitate the adsorption and charge transfer between intermediates and the catalyst resulting in enhanced catalytic performance. This work demonstrates an alternative way to further improve the catalytic activity of SACs by introducing a non‐metal atom that may shed light on the rational design of advanced SACs for ORR/OER with high efficiency and stability. [ABSTRACT FROM AUTHOR]

A tremendous amount of research is currently focused on two-dimensional (2D) magnetic semiconductors because of their remarkable physical properties and diverse applications. However, their applications are highly limited by the low Curie temperature (TC). Based on first-principles calculations and Monte Carlo simulations, we demonstrate that 2D InCrTe3 monolayers are ideal candidates for next-generation spintronics applications. We show that 2D InCrTe3 monolayers have thermodynamical and mechanical stability. The magnetic couplings between Cr atoms are strong ferromagnetic (FM) interactions. 2D InCrTe3 monolayers have robust FM semiconducting behavior with a bandgap of ∼0.67 eV, adequate TC (higher than ∼300 K), and a giant out-of-plane magnetic anisotropy energy (MAE) of ∼1.4 meV/Cr. Moreover, the low symmetry C3v point group leads to large in-plane piezoelectric coefficients d11 (larger than 4.8 pm/V) and out-of-plane piezoelectric coefficients d31 (larger than 0.39 pm/V), which are higher than many Janus transition metal dichalcogenides. The theoretical predictions of high TC, large MAE, and piezoelectricity in 2D InCrTe3 monolayers suggest that they have great potential for applications in spintronics, nano-sized sensors, and electromechanics. [ABSTRACT FROM AUTHOR]

Background: Mesenchymal stem cells (MSCs) have generated a great amount of interest in recent years as a novel therapeutic application for improving the quality of pet life and helping them free from painful conditions and diseases. It has now become critical to address the challenges related to the safety and efficacy of MSCs expanded in vitro. In this study, we establish a standardized process for manufacture of canine adipose-derived MSCs (AD-MSCs), including tissue sourcing, cell isolation and culture, cryopreservation, thawing and expansion, quality control and testing, and evaluate the safety and efficacy of those cells for clinical applications. Results: After expansion, the viability of AD-MSCs manufactured under our standardized process was above 90 %. Expression of surface markers and differentiation potential was consistent with ISCT standards. Sterility, mycoplasma, and endotoxin tests were consistently negative. AD-MSCs presented normal karyotype, and did not form in vivo tumors. No adverse events were noted in the case treated with intravenously AD-MSCs. Conclusions: Herein we demonstrated the establishment of a feasible bioprocess for manufacturing and banking canine AD-MSCs for veterinary clinical use. [ABSTRACT FROM AUTHOR]

The maximum biosurfactant concentration of 5.98 g L−1 was achieved from kitchen waste oil by Pseudomonas aeruginosa. Then, biosurfactant was extracted from the broth and applied to wash oil-based drilling cuttings. Effect rules of six parameters on oil recovery were investigated separately, and the main influencing factors of concentration, pH, and temperature were screened out by Plackett-Burman design. And then the oil washing conditions were optimized by response surface method. The maximum recovery rate was predicted to be 70.48% with the relative error less than 0.006%. The results indicating promising prospect of biosurfactant application in industrial oil recovery. [ABSTRACT FROM AUTHOR]

[Display omitted] • The pulse electromagnetic EOF of Jeffrey fluid in microchannels under a constant pressure gradient is investigated. • Analytical solutions for velocity and volumetric flow rate are derived using Laplace transform and residue theorem. • An analysis is conducted on both the near-center and near-wall velocities. • The velocities are evaluated during the first and second half-cycles, respectively. The purpose of this work is to investigate the pulse electromagnetic electroosmotic flow (EOF) behavior of a Jeffrey fluid in parallel plate microchannels under a constant pressure gradient. Analytical solutions for the velocity and volumetric flow rate are derived by employing the Laplace transform method and the residue theorem. In particular, the electric double layer (EDL) effect is considered, and an analysis is conducted on both the near-center and near-wall velocities. The flow periodicity is also noted, and these velocities are evaluated during the first and second half-cycles, respectively. Moreover, both 3D and 2D graphics are simultaneously utilized to visualize the impact of relevant parameters on velocity and volumetric flow rate. Our analysis yielded the following key findings: The effects of relevant parameters, such as pulse width a ¯ , pressure gradient Ω , Hartmann number Ha , electric field strength β , electrokinetic width K , relaxation time λ ¯ 1 and retardation time λ ¯ 2 , on the near-center velocity during the first half-cycle align with the results of previous studies. During the second half-cycle, higher values of variables Ha and β lead to reduced velocities. Interestingly, during any half-cycle, the near-wall velocity demonstrates similar characteristics to the near-center velocity in response to variations in certain parameters, including Hartmann number Ha , electric field strength β , relaxation time λ ¯ 1 and retardation time λ ¯ 2. It is worth mentioning that the influence of relaxation time λ ¯ 1 and retardation time λ ¯ 2 on the near-wall velocity is largely independent of the Hartmann number, especially for the retardation time. Regardless of the values of the parameters λ ¯ 1 and λ ¯ 2 , a higher volumetric flow rate is observed with an increased electrokinetic width K. Additionally, the volumetric flow rate profile of the Jeffrey fluid exhibits a comparatively smoother trend over time compared to that of the Maxwell fluid. The remarkable similarity between Jeffrey fluid and blood suggests that the results presented in this paper hold immense potential as a crucial reference for studying blood transport within the field of microfluidics. [ABSTRACT FROM AUTHOR]

• •A review of grout defect detection techniques for grouted splice sleeves (GSSs) is presented. • •The causes and types of grout defects are summarized. • •The principles, detection mechanisms, and advantages and disadvantages of various detection techniques are analyzed and compared. • •A concept of full life cycle monitoring of grout defects for GSSs is introduced. • •The future prospects of grout defect detection for GSSs are discussed. At present, grouted splice sleeves are widely used in assembly-type building connections due to their exceptional structural performance. However, various grout defects may arise, including grout leakage, interfacial debonding, internal voids, reinforcement eccentricity, and poor grout, as a result of the combined impact of human, environmental, technical, and equipment risks. These defects can lead to a decrease in the load-bearing capacity and durability of the structure. Therefore, it is crucial to develop efficient detection methods for grouted splice sleeves to ensure grout quality. This study provides a comprehensive and up-to-date review of the defect detection techniques for grouted splice sleeves. First, the typical applications and types of grout defects in grouted splice sleeves are reviewed. Various detection methods are categorized into direct and indirect detection (mechanical waves and electromagnetic waves) methods, and their current application status and detection principles are systematically summarized. Direct detection methods employ tools that can directly contact the grout defects themselves, while the equipment used in mechanical wave- and electromagnetic wave-based detection methods cannot directly touch the grout but rather is placed outside the steel sleeve or concrete protective layer to indirectly measure grout defects. Furthermore, a systematic comparison and in-depth analysis of the advantages and disadvantages, detection targets, and presentation modes of various detection techniques are conducted. The findings of this study can help guide researchers and engineers in the development and application of grout defect detection techniques for grouted splice sleeves. [ABSTRACT FROM AUTHOR]