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Aluminum‐ion batteries (AIBs) have attracted great attentions in recent years. Organic materials such as polythiophene (PT) are promising cathode for AIBs. However, the capacity and cyclic stability of conventional organic cathode such as PT are limited by the inadequate degree of reaction and the unstable nature of organic materials. To obtain high‐performance organic cathode, a new PT with the ability of self‐adaptive re‐organization was prepared. During cycling, its molecular chain can be re‐organized, and the polymerization mode will change from Cα−Cα (α‐PT) to Cβ−Cβ (β‐PT). This change leads to smaller steric hindrance and faster kinetics during ion insertion which can lower the reaction energy barrier and stabilize the molecular structure. Benefited by this, AIBs with this cathode can deliver a specific capacity of 180 mAh g−1 (@2 A g−1) and a superb stability of 100 000 cycles at 10 A g−1. High energy density and power density can also be achieved with this cathode. [ABSTRACT FROM AUTHOR]

Aluminum‐ion batteries (AIBs) have attracted great attentions in recent years. Organic materials such as polythiophene (PT) are promising cathode for AIBs. However, the capacity and cyclic stability of conventional organic cathode such as PT are limited by the inadequate degree of reaction and the unstable nature of organic materials. To obtain high‐performance organic cathode, a new PT with the ability of self‐adaptive re‐organization was prepared. During cycling, its molecular chain can be re‐organized, and the polymerization mode will change from Cα−Cα (α‐PT) to Cβ−Cβ (β‐PT). This change leads to smaller steric hindrance and faster kinetics during ion insertion which can lower the reaction energy barrier and stabilize the molecular structure. Benefited by this, AIBs with this cathode can deliver a specific capacity of 180 mAh g−1 (@2 A g−1) and a superb stability of 100 000 cycles at 10 A g−1. High energy density and power density can also be achieved with this cathode. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) technique has attracted more and more interest for specific identification of chemicals. However, the low selectivity and repeatability of the SERS-based technique limit its wide use in testing complex real-world samples containing various analytes. These shortcomings can be overcome by combining the SERS technique with molecularly imprinted polymers (MIPs), which can provide a highly selective SERS measure. This review firstly summarises the principles and preparation methods of MIPs and background knowledge of the SERS technique. Then, different MIPs-SERS preparation approaches and nanocomposites are highlighted. Finally, the main challenges and future efforts in developing MIPs-SERS sensors for detecting environmental contaminants are discussed. [ABSTRACT FROM AUTHOR]

Cr(VI) present in effluent discharged from tannery, electroplating, chemical industries, etc., has led to Cr(VI)-induced environmental contamination, which is threatening the ecosystem and human health. Conventional Cr(VI)-detecting techniques are inefficient with expensive instrumentation and additional chemical compounds. To address these issues, sensors can be employed for Cr(VI) detection due to their advantages of higher selectivity, sensitivity and responsibility. This review paper firstly summarises the properties, industrial use and toxicity of Cr(VI), as well as traditional detecting techniques. Then, we describe the principles and types of electrochemical sensors (potentiometric sensors, amperometric sensors and conductometric sensors) and non-electrochemical sensors (optical sensors and microcantilever sensors) in detail for detection of Cr(VI). Challenges and examples for each of the major sensor related to different application areas are reviewed. This review paper aims to act as a reference for researchers in developing new sensors for detection of Cr(VI). [ABSTRACT FROM AUTHOR]

The reinforced concrete (RC) infrastructure can be retrofitted by adhesively bonding fiber-reinforced polymers (FRPs) to the tension face. In the FRP-to-concrete bonding system, the debonding of the FRP plate from the member is the most common failure type. Predicting the bond strength of FRP-to-concrete joints using traditional predictive models is far from being satisfactory because of the highly nonlinear relationships between the bond strength and a large number of influencing variables. To address this issue, this study proposes a metaheuristic-optimized least-squares support vector regression (LSSVR) model to predict the bond strength of FRP-to-concrete joints. The hyperparameters of the LSSVR model are tuned using a recently proposed beetle antennae search (BAS) algorithm. In addition, the Levy flight is incorporated in the BAS algorithm to improve its searching efficiency. The proposed model is then trained on a dataset collected from internationally published literature. To understand the importance of each input variable on the bond strength, the variable importance is calculated using the random forest algorithm. The results show that the proposed LBAS-LSSVR model has comparatively high prediction accuracy, as indicated by a high correlation coefficient (0.983) and low root mean square error (1.99 MPa) on the test set. Width of FRP is the most sensitive variable to the bond strength. The proposed model can be extended to solve other regression problems in structural engineering. [ABSTRACT FROM AUTHOR]

Evaluation of earthquake-induced liquefaction potential is crucial in the design phase of construction projects. Although several machine learning models achieve good prediction accuracy on their particular datasets, they may not perform well in other liquefaction datasets. To address this issue, we proposed a novel hybrid classifier ensemble to improve generalizability by combining the predictions of seven base classifiers using the weighted voting method. The applied base classifiers include back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and naïve Bayes. The hyperparameters and weights of the base classifiers were tuned using the genetic algorithm. To verify the robustness of the classifier ensemble, its performance was tested on three datasets collected from previous published researches. The results show that the proposed classifier ensemble outperforms the base classifiers in terms of a variety of performance metrics including accuracy, Kappa, precision, recall, F1 score, AUC and ROC on the three datasets. In addition, the importance of influencing variables was achieved by the classifier ensemble on the three datasets to facilitate the future data collecting work. This robust ensemble method can be extended to solve other classification problems in civil engineering. [ABSTRACT FROM AUTHOR]

Superhigh‐water backfill mining can mitigate rockburst risks in rockburst‐prone deep coal mines and reduce pollution caused by traditional cemented paste backfill. In this mining method, the width of the rib pillar between two longwall panels is important to maintain roadway stability, prevent rockbursts and water seepage. To design the width of rib pillars, this study firstly established a T‐shaped overlying strata structure model and analyzed source of stress that caused deformation of the T‐shaped model. Based on this model, the abutment stress in the rib pillar was determined. Then, the criterion for overall burst instability of the rib pillar was proposed according to the derived abutment stress. The limit equilibrium theory was applied to obtain the pillar plasticity which can be used as the criterion for water‐seepage prevention. The proposed approach was used to design the width of a rib pillar in Yineng Coalmine located in Shandong Province, China. The analysis of the microseismic monitoring results and borehole drill cuttings show that the designed rib pillar with a width of 10 m was stable without water seepage during mining, indicating the width design method proposed in this study is effective. [ABSTRACT FROM AUTHOR]

The disposal of steel slag in landfills not only squanders precious landfill resources but also results in significant environmental contamination. Steel slag can be used to replace cement and natural aggregates in concrete. Nonetheless, utilizing untreated steel slag directly presents a substantial hazard to the structural characteristics and durability of concrete. To avoid this problem, study on accelerated carbonation of steel slag as construction materials has been extensively conducted. This review paper begins by introducing the classification and physicochemical properties of steel slag; it then describes the typical approaches of steel slag carbonation, including direct carbonation (dry carbonation, wet carbonation) and indirect carbonation (pH-swing, microbial carbonation), along with their processes and carbonation mechanisms. Furthermore, based on the reviewed literature, the influences of various reaction parameters such as reaction time, liquid-to-solid ratio, temperature, additives, CO 2 concentration, pressure, pH on carbonation effectiveness are analyzed. Also, the impact of carbonated steel slag on the micro-pore structure, stability, workability, and mechanical properties of concrete is discussed. Finally, the challenges and future work of using carbonated steel slag as construction materials are provided. • The current papers on steel slag classification, physicochemical properties, and methods for accelerated carbonation are reviewed. • The traditional carbonation methods, as well as recently proposed microbial carbonation methods are analyzed. • The factors influencing the efficiency of steel slag carbonation are summarized. • The impact of carbonated steel slag on concrete properties are concluded. • Insights into accelerated carbonation methods for steel slag and future work are provided. [ABSTRACT FROM AUTHOR]

Summary: Rockbust is a violent expulsion of rock due to the extreme release of strain energy stored in surrounding rock mass, leading to considerable damages to underground strucures and equipment, and threatening workers' safety. As the operational depth of engineering projects increases, a larger number of factors influence the mechanism of rockburst. Therefore, accurate classification of rockburst intensity cannot be achieved based on conventional criteria. It is urgent to develop new models with high accuracy and ease to implement in practice. This study proposed an ensemble machine learning method by aggregating seven individual classifiers including back propagation neural network, support vector machine, decision tree, k‐nearest neighbours, logistic regression, multiple linear regression and Naïve Bayes. In addition, we proposed nine data imputation methods to replace the missing values in the compiled database including 188 rockburst instances. Five‐fold cross validation and the beetle antennae search algorithm are used to tune hyperparameters and voting weights of the individual classifiers. The results show that the rockburst classification accuracy obtained by the classifier ensemble has increased by 15.4% compared with the best individual classifier on the test set. The predictor importance obtained by the classifier ensemble shows that the elastic energy index is the most sensitive input variable for rockburst intensity classification. This robust ensemble method can be extended to solve other classification problems in underground engineering projects. [ABSTRACT FROM AUTHOR]

In deep coalmines, longwall panels are subject to high static initial geostress andhigh dynamic stress caused by mining and tunnelling activities. Under the action of high static and dynamic stress, rockburst hazards are very likely to occur. To reduce rockburst risks, protective panels are commonly applied in deep coalmines. However, stress concentration in the protective coal panel often causes rockburst hazards in the gateway of the next longwall panel pending mining. To reduce such type of rockburst, this study firstly proposes a mathematic model to analyse the overall static stress distribution in the protective panel based on the mining practice in Longyun coalmine, Shandong Province, China. To evaluate the stress concentration caused by geological defects in the protective panel, a new rockburst evaluation index is proposed based on the computed tomography (CT) method. Finally, the extent of dynamic stress evolution caused by different working face advancing velocities is determined by microseismic monitoring. Results show that the areas with higher rockburst evaluation indexes are highly associated with the areas with large-energy microseismic events, indicating that the static stress concentration can be accurately identified by the CT method. A medium advancing velocity (4.0 m/s) is recommend during mining the longwall panel, which can ensure mining safety and improve mining productivity simultaneously. The integrated microseismic and CT monitoring methods can be used in other underground projects to guarantee construction safety and productivity. [ABSTRACT FROM AUTHOR]

Tunnel squeezing occurs when time-dependent rock creep produces large tunnel convergence. The occurrence of tunnel squeezing may result in buget increase and time waste during tunnel construction. The aim of this study was to propose a robust classifier ensemble to predict squeezing conditions in rock tunnels. Seven individual machine learning classifiers were aggregated using weighted voting methods to establish the classifier ensemble. The weight and hyperparameters of each individual classifier were tuned using the firefly algorithm. The classifier ensemble was trained and tested on a dataset collected from published literature. Missing values in the database were replaced by various imputation methods. The results indicate that the proposed classifier ensemble achieved an accuracy of 96%, higher than that of the traditionally used individual classifiers. This robust ensemble method can be applied to other classification problems in civil engineering. [ABSTRACT FROM AUTHOR]

Highlights • The compressive strength of lightweight self-compacting concrete was modelled intelligently. • Beetle antennae search algorithm was firstly used to tune hyper-parameters of random forest. • The importance of different input variables was measured. Abstract Self-compacting concrete (SCC) can achieve compaction into every part of the formwork through its own weight without any segregation of the coarse aggregate. Lightweight concrete (LWC) can reduce the dead load of the structure by incorporating the lightweight aggregate (LWA). In recent years, more and more studies have focused on combining the advantages of SCC and LWC to produce lightweight self-compacting concrete (LWSCC). As one of the most important mechanical properties, uniaxial compressive strength (UCS) values need to be tested before field application of this new material. However, conducting UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, a beetle antennae search (BAS) algorithm based random forest (RF) model to accurately and effectively predict the UCS of LWSCC. This model was developed and verified using data from LWSCC laboratory formulation. Results show that BAS was efficient in searching the optimum hyper-parameters of RF. The proposed BAS-RF model achieved high predictive accuracy indicated by a high correlation coefficient (0.97). In addition, by measuring the variable importance, we conclude that temperature was the most sensitive to UCS development, followed by scoria content and water-to-binder (w/b) ratio, while UCS was less sensitive to fiber content. This pioneering work provides a simple and convenient method for evaluating UCS of LWSCC at varying temperatures. [ABSTRACT FROM AUTHOR]

• A novel method was proposed for predicting permeability and unconfined compressive strength of pervious concrete. • 270 samples were prepared for building the dataset. • Permeable and mechanical properties of pervious concrete were elucidated. • Beetle antennae search was firstly used to tune the hyper-parameters of support vector regression. • The support vector regression model tuned by beetle antennae search algorithm has high prediction accuracy. Pervious concrete is a widely used construction material thanks to its good drainage characteristics. Before application, its most important properties, i.e. the permeability coefficient (PC) and 28-day unconfined compressive strength (UCS) are required to be tested. However, conducting PC and UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, an evolved support vector regression (ESVR) tuned by beetle antennae search (BAS) to accurately and effectively predict the PC and UCS of pervious concrete. To prepare the dataset of the ESVR model, 270 specimens in total were prepared and casted in a controlled environment in the laboratory. The water-to-cement (w/c) ratio, aggregate-to-cement (a/c) ratio, and aggregate size were selected as the crucial influencing variables for the inputs, while PC and UCS were the outputs of this model. The results indicate that both the PC and UCS firstly increased and then decreased with increasing w/c ratio. As the a/c ratio increased, PC increased, while UCS decreased. Moreover, BAS is more reliable and efficient than random hyper-parameter selection for hyper-parameter tuning. A low root-mean-square error (RMSE) and high correlation coefficient (R) indicate a relatively high predictive capability of the proposed ESVR model. The sensitivity analysis (SA) suggests the a/c ratio and aggregate size were the most sensitive variables for UCS and PC, respectively. This pioneering work provides a simple and convenient method for evaluating PC and UCS of pervious concrete. [ABSTRACT FROM AUTHOR]

CRYAB is a member of the small heat shock protein family, first discovered in the lens of the eye, and involved in various diseases, such as eye and heart diseases and even cancers, for example, breast cancer, lung cancer, prostate cancer, and ovarian cancer. In addition, CRYAB proteins are involved in a variety of signaling pathways including apoptosis, inflammation, and oxidative stress. This review summarizes the recent progress concerning the role of CRYAB in signaling pathways and diseases. Therefore, the role of CRYAB in signaling pathways and cancers is urgently needed. This article reviews the regulation of CRYAB in the apoptotic inflammatory signaling pathway and its role in cancers progression and as a key role in anti-cancer therapy targeting CRYAB in an effort to improve outcomes for patients with metastatic disease. [ABSTRACT FROM AUTHOR]

Abstract The coalcrete, a new supporting material produced by jet grouting (JG) technique was firstly studied for improving soft coal mass to support roadways in Guobei coal mine, China. Young's modulus is an essential indicator to evaluate the deformation-resisting ability of coalcretes. In this study, for determining Young's modulus of coalcretes efficiently, an intelligent technique was proposed using the support vector machine (SVM) and beetle antennae search (BAS). The hyper-parameters of SVM were firstly tuned by BAS, and then the SVM-BAS model with optimum hyper-parameters was employed to model the non-linear relationship between the inputs (coal content, water content, cement content, and curing time) and output (Young's modulus). By combining these variables, 360 coalcrete samples in total were prepared and tested for establishing the dataset. The results show that BAS is more reliable and efficient than the trial–and–error tuning method. Moreover, by comparison with other baseline models such as back-propagation neural network (BPNN), logistic regression (LR) and multiple linear regression (MLR), the optimized SVM-BAS model is more reliable, accurate and less time consuming for predicting Young's modulus of coalcretes. Besides, by conducting sensitivity analysis (SA), the importance of different input variables was determined. This pioneering work provides guidelines for predicting Young's modulus of coalcretes and designing proper JG parameters in engineering applications. Highlights • The jet grouting technique was firstly utilized in underground coal mine for generating coalcrete to reinforce the roadway stability • A total of 360 specimens were tested to determine the deformation-resisting ability of coalcrete. • A support vector machine model was proposed for predicting Young's modulus of coalcrete. • The hyperparameters of support vector machine were tuned by beetle antennae search algorithm. [ABSTRACT FROM AUTHOR]

Summary: This investigation studied the coalcrete, a new supporting material produced by jet grouting (JG) for supporting surrounding coal seams. For support design, the unconfined compressive strength (UCS) of the coalcrete is an essential parameter to evaluate the jet grouting effect in coal mines. In this study, an intelligent technique was proposed for predicting the UCS of the coalcrete by combining back propagation neural network (BPNN) and beetle antennae search (BAS). The architecture of BPNN was first tuned by BAS, and then, the optimized BPNN‐BAS model was subsequently used for nonlinear relationship modeling. Several crucial influencing variables including water‐cement ratio, coal‐grout ratio, and curing time were selected as the inputs. By combining these variables, 360 coalcrete samples were prepared in a controlled laboratory environment and tested for establishing the dataset. The results demonstrate that BAS can tune the BPNN architecture more efficiently compared with random selection. Moreover, in comparison with multiple regression (MLR) and logistic regression (LR), and support vector machine (SVM), the optimized BPNN‐BAS model is more reliable and accurate for predicting coalcrete strength. Sensitivity analysis (SA) was used to obtain the variable importance, and the results demonstrate that curing time affects the UCS of the coalcrete most strongly, followed by water‐cement ratio and coal‐grout ratio. The success of this study provides an accurate and brief approach to coalcrete strength prediction. [ABSTRACT FROM AUTHOR]

The addition of a red-emitting phosphor to YAG:Ce3+-based white light-emitting diodes (WLEDs) greatly facilitates their applications in the field of high-color-rendering-index warm solid-state lighting. It is highly desirable to develop a red phosphor with satisfactory spectral features and low synthesis cost. In this study, a novel non-rare-earth and nonequivalent doping type of Cs2NbOF5:Mn4+ oxyfluoride red-emitting phosphor with high luminous efficiency was obtained via a facile room-temperature co-precipitation method, and its morphology and luminescent properties were investigated in detail. The Cs2NbOF5:Mn4+ phosphor with micro-rod-like morphology exhibited broad band absorption at blue light region (∼474 nm) and narrow bandwidth emissions at red region (∼633 nm). The color purity of the Cs2NbOF5:Mn4+ phosphor was calculated to be about 99%, and the internal quantum yield (QY) under 474 nm excitation was 63.4%. The concentration quenching of Mn4+ in Cs2NbOF5 matrix was mainly due to dipole–dipole interactions, and the activation energy of temperature quenching was calculated to be ∼0.2610 eV. The demonstration of a blue InGaN LED chip in combination with a blend of newly developed Cs2NbOF5:Mn4+ red phosphor and YAG:Ce3+ yellow phosphor greatly decreased the correlated color temperature (CCT) from 6255 to 3517 K while significantly improving the color rendering index (CRI) from 72.5 to 87.5. It deserves to be mentioned that the brand-new matrix to phosphor in the present study can be extended to various niobium/tantalum oxyfluoride series, which is very helpful for developing and designing new red phosphors. [ABSTRACT FROM AUTHOR]

Abstract Lack of red-light of current w-LEDs restricts their applications in the field of high color rendering index warm solid-state lighting. Fluoride phosphors doped with Mn4+ provide an efficient way to resolve the problem. Unfortunately, these excellent Mn4+ activated fluoride red phosphors are generally synthesized in highly toxic HF solution. In this paper, we report a novel green synthetic route to prepare a non-rare-earth and nonequivalent doping type of K 2 NbF 7 :Mn4+ red phosphor with micro-rod-like morphology. The results of XRD and its Rietveld refinement reveal that all the samples possessed only a single phase with monoclinic P21/c structure and the Mn4+ ions were successfully incorporated into the crystal lattice. The Mn4+ ions in K 2 NbF 7 matrix have a highly distorted octahedral environment, which strengthen the intensity of zero phonon line (ZPL). The strongest absorption and excitation bands appear in blue region of diffuse refection spectra (DRS) and the photoluminescence excitation (PLE) of K 2 NbF 7 :Mn4+ sample, which demonstrates the product perfectly match with the most popular GaN-based blue chip. Under blue light illumination, the as-prepared K 2 NbF 7 :Mn4+ sample exhibits intense sharp-line red fluorescence (∼628 nm) with internal quantum yield (QY) of 71.3%, an extreme narrow full width at half-maximum (FWHM) of ∼2.8 nm, and an ultra-high-color purity of 99.2%. Impressively, by incorporating K 2 NbF 7 :Mn4+ as a red component, a warm w-LED with high CRI of 83.8, low color temperature of 3010 K and high luminous efficiency of 115.91 lm/W is obtained, demonstrating great validity of K 2 NbF 7 :Mn4+ red phosphor for white LED devices. Highlights • The K 2 NbF 7 :Mn4+ red phosphor with high luminous efficiency was obtained successfully via a novel HF-free approach. • Reaction mechanism of K 2 NbF 7 :Mn4+ red phosphor, including incorporation mode of Mn4+ into K 2 NbF 7 host, were investigated carefully. • Optical properties of K 2 NbF 7 :Mn4+ red phosphor, especially the intense ZPL emission, were investigated carefully. • Addition of K 2 NbF 7 :Mn4+ red phosphor, a warm w-LED with high CRIand low color temperature is obtained. [ABSTRACT FROM AUTHOR]

In this paper, we will consider the existence of a strong solution for stochastic differential equations with discontinuous drift coefficients. More precisely, we study a class of stochastic differential equations when the drift coefficients are an increasing function instead of Lipschitz continuous or continuous. The main tools of this paper are the lower solutions and upper solutions of stochastic differential equations. [ABSTRACT FROM AUTHOR]

The investigation on single phase multi-color phosphors is highly meaningful for near-ultraviolet chip based white light emitting diodes. In this work, a series of Eu 2+ and Tb 3+ singly doped and Eu 2+ /Tb 3+ codoped Sr 5 (PO 4 ) 3 Cl phosphors were synthesized via a high-temperature solid state reaction method. The luminescence spectra and decay curves of Eu 2+ and Tb 3+ singly doped samples were discussed, the optimal doping concentrations were determined. Thanks to the spectra overlap between Eu 2+ and Tb 3+ , nonradiative energy transfer from Eu 2+ to Tb 3+ was investigated. It is found electric dipole-dipole interaction played the main role for the energy transfer in codoped samples, the highest energy transfer efficiency was calculated to be 60.98%. Tunable emissions are observed for codoped samples by adjusting doping concentration. The thermal quenching properties were discussed and the activation energy (ΔE) was estimated in the present work. [ABSTRACT FROM AUTHOR]

This paper first analyzes the concept and features of microlecture, mobile learning, and ubiquitous learning, then presents the combination of microlecture and mobile learning, to propose a novel way of micro-learning through mobile terminals. Details are presented of a microlecture mobile learning system (MMLS) that can support multiplatforms, including PC terminals and smartphones. The system combines intelligent push, speech recognition, video annotation, Lucene full-text search, clustering analysis, Android development, and other technologies. The platform allows learners to access microlecture videos and other high-quality microlecture resources wherever and whenever they like, in whatever time intervals they have available. Teachers can obtain statistical analysis results of the microlecture in MMLS to provide teaching/learning feedback and an effective communication platform. MMLS promotes the development of microlecture and mobile learning. A statistical analysis of the implementation of the system shows that students using MMLS to assist their learning had improved results on their final exams and gave a higher evaluation of the curriculum than those who did not. The advantages and disadvantages of MMLS are also analyzed. [ABSTRACT FROM AUTHOR]

Lightweight concrete (LWC) is widely used in the construction industry due to a variety of advantages. However, compared with traditional normal-weight concrete, more influencing variables (e.g. types of lightweight aggregates) must be considered to optimize multiple properties including uniaxial compressive strength (UCS), density and cost. This makes the mixture design of LWC more difficult or sometimes impossible using laboratory experiments. To address this issue, this study proposes a multi-objective optimization (MOO) method using machine learning and metaheuristic approaches for LWC mixture design through a two-step approach. In the first step, a least squares support vector regression (LSSVR) model is constructed to predict multiple properties of LWC. The hyper-parameters of the LSSVR model are tuned using the firefly algorithm (FA). A dataset containing a large number of different mixtures of LWC is compiled from published literature. High prediction accuracy (0.97 for UCS and 0.90 for density) is achieved on the test dataset (including 30% of all the instances). In the second step, a newly developed multi-objective FA (MOFA) model is used to optimize the LWC mixture, while satisfying the constraints. The Pareto fronts of the triple objectives (UCS, cost and density) are successfully obtained. The proposed MOO method is powerful and efficient in finding optimal LWC mixtures with conflicting objectives and therefore decision making can be facilitated in early phases of construction. [ABSTRACT FROM AUTHOR]

• The strength of SFRC are predicted using FA-SVR; • Multi-objective firefly algorithm is proposed based on the basic firefly algorithm; • The mixture proportion of SFRC is automatically designed using a multi-objective optimization method. Steel fibre reinforced concrete (SFRC) is widely used in the construction concrete industry as it partakes an important role of evolving concrete technology. It consists of steel fibres of various shapes, sizes and geometries that influence the concrete mix composition and mechanical properties. However, compared to traditional concrete, it is difficult to design the mix proportions because more influencing variables need to be considered to optimise multiple properties including ultimate compressive strength, tensile or flexural strength and cost. Therefore, the present study proposes an artificial intelligence based multi-objective optimization model to enable an efficient method of finding the optimum mix design for SFRC. A large dataset including 299 instances for uniaxial compressive strength (UCS) test and 269 instances for flexural strength (FS) test were collected from previous literature. Support vector regression (SVR) model was applied to predict UCS and FS for SFRC. The hyper parameters of SVR models were tuned using a firefly algorithm (FA) and a sensitivity study was conducted to understand the importance of the inputs on the output variables for the algorithms. High correlation coefficients (0.91 for UCS and 0.85 for FS) were achieved on the test dataset. The FA-SVR model was then applied as the objective function for a developed multi-objective FA to search for the optimal SFRC mixture proportion. Pareto optimal solutions were obtained and served as a design guide to determine the optimal SFRC mixtures. [ABSTRACT FROM AUTHOR]

The replacement of natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) in concrete mixtures offers various advantages, including conservation of natural resources, reduction of CO 2 emissions, and cost reduction. However, multiple related variables and objectives (e.g., mechanical, economic, and environmental objectives) need to be considered when optimizing mixtures of recycled aggregate concrete (RAC). This cannot be achieved through traditional laboratory- or statistics-based methods. This study proposes a hybrid intelligent system based on artificial intelligence (AI) and metaheuristic algorithms for designing optimal mixtures of RAC. To verify the proposed model, a data set containing 344 different RAC mixtures was collected from previous literature. A semi-supervised cotraining algorithm using two k -nearest neighbor (k NN) regressors with different distance metrics is developed to label the unlabeled data in the collected dataset. Different AI models are incorporated into the system for modeling the relationship between RAC strength and its influencing variables. A multi-objective optimization (MOO) model based on AI algorithms and on a multi-objective firefly algorithm is used to search for optimal mixtures of RAC. The results show that k NN-based semi-supervised cotraining can effectively exploit unlabeled data to improve the regression estimates. In the test set, A Random Forest and Backpropagation Neural Network achieve the best prediction accuracy for predicting, respectively, uniaxial compressive strength and splitting tensile strength of RAC, indicated by the highest correlation coefficients (0.9064 and 0.8387, respectively) and lowest root-mean-square errors (6.639 MPa and 0.5119 MPa, respectively). The Pareto fronts of the multi-objective mixture optimization problem are successfully obtained by the MOO model. The proposed system can also be used to optimize mixture proportions of other cementitious materials in civil engineering. • RF and BPNN achieve highest accuracy for predicting UCS (R = 0.91) and STS (0.84) of RAC, respectively. • The kNN-based semisupervised cotraining can effectively exploit unlabeled data to improve the regression estimates. • Pareto fronts of multi-objective mixture optimization problem for RAC are obtained by using AI models and MOFA. [ABSTRACT FROM AUTHOR]

• BPNN has good prediction accuracy for UCS, while RF performs better in predicting slump. • PSO is efficient in tuning hyperparameters of machine learning models. • The Pareto front of the mixture optimization problem is obtained by MOPSO. For the optimization of concrete mixture proportions, multiple objectives (e.g., strength, cost, slump) with many variables (e.g., concrete components) under highly nonlinear constraints need to be optimized simultaneously. The current single-objective optimization models are not applicable to multi-objective optimization (MOO). This study proposes an MOO method based on machine learning (ML) and metaheuristic algorithms to optimize concrete mixture proportions. First, the performances of different ML models in the prediction of concrete objectives are compared on data sets collected from the published literature. The winner is selected as the objective function for the optimization procedure. In the optimization step, a multi-objective particle swarm optimization algorithm is used to optimize mixture proportions to achieve optimal objectives. The results show that the backpropagation neural network has better performance on continuous data (e.g., strength), whereas the random forest algorithm has higher prediction accuracy on more discrete data (e.g., slump). The Pareto fronts of a bi-objective mixture optimization problem for high-performance concrete and a tri-objective mixture optimization problem for plastic concrete are successfully obtained by the MOO model. The MOO model can serve as a design guide to facilitate decision-making before the construction phase. [ABSTRACT FROM AUTHOR]

• A multi-output model was firstly applied for predicting pervious concrete properties. • A bio-inspired algorithm was modified to tune hyperparameters of the multi-output model. • A number of pervious concrete samples were prepared in laboratory to test the model. Pervious concrete can purify water, mitigate storm water runoff and reduce the urban heat island effect due to its larger porosity. However, its highly porous inner structure causes a lower compressive strength in comparison with normal concrete. Therefore it is vital to accurately predict the two basic parameters: permeability coefficient (PC) and uniaxial compressive strength (UCS) before field application to reduce time and cost of a construction project. As traditional mathematical models cannot model the highly nonlinear relationships between PC (or UCS) and its constituents, this study addresses this problem by applying a hybrid artificial intelligence model: multi-output least squares support vector regression (MOLSSVR). This model can also improve the prediction accuracy by utilizing the relationship between the two outputs: PC and UCS. In addition, the hyperparameters of MOLSSVR are tuned by a beetle-antennae search (BAS) algorithm which is modified by incorporating self-adaptive inertia weight and Levy flight. To train the proposed model, a large number of pervious concrete samples with different mixture proportions were prepared in laboratory. The results show that the searching efficiency of the modified BAS is significantly higher than that of BAS. The proposed hybrid model achieves better prediction accuracy than other models in the literature. This method can be used to address other multi-output problems in civil engineering. [ABSTRACT FROM AUTHOR]

Depletion of river sand due to large-scale concrete production has caused many environmental problems. To address this issue, river sand can be replaced with sand manufactured from waste deposits. To facilitate manufactured-sand concrete production, this study proposes three tree-based models: one individual model (regression tree (RT)), and two ensemble models (random forest (RF) and gradient boosted regression tree (GBRT)) to predict its mechanical properties, such as uniaxial compressive strength (UCS), and splitting tensile strength (STS). These tree-based models were trained and tested on a dataset collected from previous literature. In addition, to understand the importance of each input variable on the mechanical properties of manufactured-sand concrete, the variable importance is calculated using the RF algorithm. The results show that the highest correlation coefficients are achieved by GBRT in predicting UCS (0.9887) and STS (0.9666), which respectively increase by 3.0%–10.8% and 16.0%–21.6% in comparison with the models in previous literature. The mechanical properties UCS and STS are highly sensitive to the curing age with relative importance of 36.8% and 40.3%, respectively. To facilitate the application of the tree-based models in predicting mechanical properties of manufactured-sand concrete, a graphical user interface has been designed in this study. • The mechanical properties of manufactured-sand concrete were predicted by tree-based models. • Firefly algorithm was used to tune the hyperparameters of the tree-based models. • Importance of input variables was calculated using random forest algorithm. • A GUI was developed to facilitate the application of the proposed models. [ABSTRACT FROM AUTHOR]

Prediction of rockburst in underground engineering is of significance as it is close to the safety of support structures, personnel, and working environments. To improve the accuracy of rockburst classification, this paper proposed an ensemble classifier RF-FA model in which the random forest classifier (RF) and firefly algorithm (FA) were combined to achieve the optimum performance on rockburst prediction. Five key parameters of surrounding rock, i.e., the depth H, the maximum tangential stress σθ, the uniaxial compressive strength σc, the tensile strength σt, and the elastic energy index Wet, are selected as input variables while the rockburst intensity including none, light, moderate, and strong classes was chosen as output. A total of 279 cases worldwide were collected and used for train and test the proposed RF-FA model. The results showed that the FA can optimize the hyperparameters of RF efficiently and the optimum model exhibited a high performance on rockburst data from the independent test set and new engineering projects. The feature importance obtained by the ensemble RF-FA model indicated that the elastic energy index plays the most important role in rockburst. Besides, the proposed model showed much better accuracy on rockburst classification compared with previously existing RF models and empirical criteria, which means it is a useful and robust tool for rockburst prediction. [ABSTRACT FROM AUTHOR]

Improving roadway stability in deep underground mines is quite challenging, as the conventional support structures easily fail. Roadway collapse and large deformation occur just several months after tunnel excavation. In this study, a relatively new prereinforcement technique, the jet grouting (JG), is introduced to improve roadway stability. A field test was performed for evaluating the practicability and applicability of JG in soft coal mass. A series of laboratory tests were conducted to assess the properties of coalcrete (coal-grout after JG treatment). A two-dimensional numerical model was established for validating the input parameters. Based on the verified model, three JG support cases for roadway were modeled and compared with a conventional support case, namely, the currently used support in this mine "rock bolts + U-shaped steel set + shotcrete." The results show that the proposed prereinforced JG support structures can considerably control the deformation and failure zone of the roadway and improve the bearing capacity of coal mass. The mechanism of maintaining roadway stability using JG techniques is further revealed. Some suggestions are further presented to improve the stability of the jet-grouted roadway. [ABSTRACT FROM AUTHOR]

The efficiency of coal mining is seriously affected by roadway stability, as large time‐dependent deformation of roadway frequently occurs and needs to be maintained several times during its service life. Such rheological deformation was common in soft coal mass at Huaibei coalfield in China. To address this issue, in this study, the time‐dependent deformation of the soft coal roadway was analyzed and a new Jet Grouting (JG) technique was presented for controlling deformation. The time‐dependent deformation of the soft coal roadway was numerically simulated and validated. Based on the field test results and the verified model, a JG support model was established to examine its effect on roadway deformation. The JG support system can reduce the horizontal and vertical displacement of the roadway effectively and constrain the time‐dependent deformation of coal mass. The deformation rate and stabilization time of roadway decreased significantly by comparison with conventional support. This work presented a promising JG support scheme for controlling the time‐dependent deformation in the roadway in deep underground mine, which can greatly promote the JG design and application. [ABSTRACT FROM AUTHOR]

Roadway stability in extremely soft coal mass is a typical challenge in deep underground mines, as the most widely adopted support structure such as rock bolting, compound support system normally fails. This paper proposed a novel prereinforcement method for coal roadway, that is, jet grouting (JG) technique, to improve the quality of surrounding soft coal mass and form a stable support structure namely jet grouted coalcrete. The field and laboratory tests were conducted for evaluating the applicability of JG and the mechanical parameters of coalcrete. A numerical model was verified by comparing the calculated results with field measurements. According to the confirmed model, three typical JG support schemes were performed to assess the roadway stability after JG. The results showed that the proposed JG schemes can reduce the deformation and failure zone of the roadway and optimize the stress states around the roadway. The mechanism related to these advantages was investigated. This study provides a promising idea for supporting soft coal roadway in deep underground coal mines, which can promote the JG application in the field. [ABSTRACT FROM AUTHOR]

• High curing temperature increases early concrete strength, while reduces later strength. • The concrete subjected to thermal curing until the CAFS age exhibits superior 28-day UCS in comparison to concrete subjected to thermal curing for only 3 days. • The strength-maturity model corrected using an empirical formula incorporating the "crossover effect", improved the prediction accuracy of concrete strength. The maturity method is widely used to estimate early-age concrete strength. However, the traditional maturity models exhibit limited predictive capability for late concrete strength under thermal curing conditions due to the influence of the "crossover effect". This study developed a curing scheme for Standard Portland cement concrete in the absence of supplementary cementitious materials at temperatures ranging from 5 °C to 50 °C and analyzed the temperature variations inside thermally cured concrete specimens. The findings reveal that an increase in curing temperature and time between 30 °C and 50 °C and 8 and 72 h respectively led to an increase in the early strength and a decrease in the late strength of concrete, due to the "crossover effect". Additionally, a linear relationship was found between curing temperature and the late strength reduction coefficient. Utilizing this relationship, a modified maturity model that considers the "crossover effect" was proposed, improving the accuracy of predicting concrete strength under thermal curing conditions (with a prediction error of less than 10%). The research outcomes are of significant guiding significance for winter construction by ensuring the quality of concrete, reducing construction accidents, and improving construction efficiency. [ABSTRACT FROM AUTHOR]

Background. Malaria is a global public health concern, mainly occurring in sub-Saharan Africa. Children infected with malaria are more likely to develop severe disease, which can be fatal. During COVID-19 in 2020, diagnosing and treating malaria became difficult. We analyzed the clinical characteristics and laboratory indicators of children with severe malaria in Benin to provide important information for designing effective prevention and treatment strategies to manage pediatric cases. Methods. Clinical characteristics of pediatric patients with severe malaria admitted to two hospitals in Benin (Central Hospital of Lokossa and Regional Hospital of Natitingou, located ∼650 kilometers apart) were collected from January to December 2020. Patients were grouped according to age (group A: 4–12 months old, group B: 13–36 months old, and group C: 37–60 months old), and clinical and laboratory indicators were compared. The incidences of severe pediatric malaria in both hospitals in 2020 were calculated. Inclusion, exclusion, and blood transfusion criteria were identified. Results. We analyzed 236 pediatric cases. The main clinical symptoms among all patients were severe anemia, vomiting, prostration, poor appetite, dysphoria, and dyspnea. Over 50% of patients in group A experienced vomiting and severe anemia. Most patients in group B had severe anemia and prostration. Delirium affected significantly more patients in group C than in groups A and B. In group C, the hemoglobin and hematocrit levels were significantly higher p < 0.05 , and the leukocyte count was significantly lower p < 0.01 than in groups A and B. Parasitemia was significantly higher in group C than in group A p < 0.01 . Twelve deaths occurred. Conclusions. Severe pediatric malaria is seasonal in Benin. The situation in children under 5 years old is poor. The main problems are severe disease conditions and high fatality rates. Effective approaches such as prevention and early and appropriate treatment are necessary to reduce the malaria burden in pediatric patients. [ABSTRACT FROM AUTHOR]

Efflorescence is always a major problem that prohibits the wide application of geopolymer. It reduces mechanical strength, increases surface degradation and lowers the durability of geopolymer. This study investigates the efflorescence and mitigation techniques of the geopolymer synthesized with fly ash (FA), granulated blast furnace slag (GBFS), steel slag (SS) and flue gas desulfurization gypsum (FGD). It was found that pre-curing in sealed condition for 3 and 7 days could improve both the compressive strength and the efflorescence resistance of the geopolymers in both ambient and accelerated conditions. Adding SS improved the compressive strength of multi-componential geopolymers by forming more C-(A)-S-H and N(C)-A-S-H gels, while was not effective in reducing efflorescence. The efflorescing problem became more serious with the addition of FGD due to the formation of new efflorescence product of Na 2 SO 4. It was also found that the w (OH−), w (CO 3 2−) and w (Na+) of the leachate obtained by soaking geopolymer in deionized water can describe the efflorescence more quantitively than visual characterization. The correlation between the porosity, w (Na+), efflorescence and compressive strength was also established, which shed light on better understanding and mitigating the efflorescence of multi-componential geopolymers. • Pre-curing in sealed condition effectively mitigated efflorescence of geopolymer. • SS at proper content decreased efflorescence and increased strength of geopolymer. • Connected pore volume and w (Na+) can reflect the efflorescence of geopolymer. [ABSTRACT FROM AUTHOR]

• The effect of high-volume fly ash on the microstructure and mechanical properties of CCGPB was studied. • The importance of the influencing variables on the strength development was analyzed by an RF model. • The maximum UCS was achieved at the mass concentration of 0.81 and the fly ash-cement replacement ratio of 0.42. • The curing age had the highest relative importance (47.7%) on the UCS gain. The utilization of coal gangue and fly ash to produce cemented paste backfill is an effective strategy to mitigate the environmental impact of such bulk solid wastes. In this study, we aimed to investigate the hardening properties of cemented coal gangue paste backfill (CCGPB) containing high-volume fly ash using macroscopic and microscopic characterization techniques. Specifically, uniaxial compressive strength (UCS) and slump were measured to evaluate the hardening properties of CCGPB, while Scanning Electron Microscopy (SEM) and Mercury Intrusion Porosimetry (MIP) were used to characterize the internal microstructure and internal pores. A total of 216 mixture proportions were designed through variations in the contents of cement, fly ash, water, and curing time. The resulting experimental data were used to develop a random forest model. This model was subsequently employed to evaluate the impact of different variables on the development of UCS in CCGPB. The results revealed that the slump increased with increasing fly ash contents and mass concentration. The maximum UCS was achieved at the mass concentration of 0.81 and the fly ash-cement replacement ratio of 0.42, which was also verified by the SEM showing a more compact interfacial transition zone with hydration products of calcium silicate hydrates, calcium aluminosilicate hydrates, and ettringites. The MIP results showed that the addition of fly ash had slight effect on the pores less than 1 μm, while pores larger than 1 μm increased with increasing fly ash content. The porosity ratio decreased by only 4% when fly ash was increased from 30% to 50%. The simulation results by the developed RF model indicated that the curing age had the highest relative importance (47.7%), while the UCS gain was less sensitive to the fly ash content compared with the cement. Overall, this study provides valuable insights into the application of fly ash and coal gangue to cemented paste backfill. [ABSTRACT FROM AUTHOR]

• A GA-RF model was developed for predicting UCS and FST of AAMs; • The curing time and water content significantly influenced the UCS, while Na/Al and water content were more important to FST; • The recommended Ca/Si varied from 1 to 2; Na/Al was slightly lower than 1 and Si/Al ratios changed between 2.5 and 3.5. The final setting time (FST) and uniaxial compressive strength (UCS) are critical parameters for designing the mixture proportions of alkali-activated materials (AAMs). To understand the influence of the mixture composition on FST and UCS of AAMs, two datasets containing 616 samples for UCS and 278 samples for FST were compiled from published literature. A random forest (RF) model was developed on these datasets to predict FST and UCS of AAMs. The hyperparameters of the RF model were optimized using the Genetic Algorithm (GA). Results show that the hybrid GA-RF model achieved the highest prediction accuracy on the test set of UCS (0.932) and FST (0.997), compared to other machine learning models. The developed model was then used to interpret the influence of mixture composition on FST and UCS. The curing time and water content significantly influenced the UCS, while Na/Al and water contents were more important to FST. The microstructure development of the AAMs was affected by Ca/Si, Na/Al and Si/Al ratios. To achieve better UCS, the recommended Ca/Si varied from 1 to 2; Na/Al was slightly lower than 1 and Si/Al ratios changed between 2.5 and 3.5. This study can facilitate the mixture optimization for FA-slag based AAMs. [ABSTRACT FROM AUTHOR]

• Using PVA and steel fiber enhanced mechanical performance of ternary geopolymer. • Higher PVA fiber ratio reduced setting time and fluidity of HFRG. • The best performance was achieved at a PVA: steel fiber volume ratio of 1:1. This study employed fly ash, blast furnace slag and steel slag to synthesize ternary-blended alkali-activated materials (AAMs) in order to make full use of the industrial solid wastes and improve the mechanical performance by synergistic effects of the precursors. However, peudo-brittle nature is still the main problem of ternary-blended AAMs. To solve this problem, the effect of hybrid fibers consisting of high-modulus steel (ST) fiber and low-modulus Polyvinyl Alcohol (PVA) fiber on the mechanical properties of the ternary-blended AAMs was evaluated by testing their setting time, flowability, uniaxial compressive strength (UCS), indirect tensile strength (IDT), uniaxial tensile strength (UTS), and three-point bending strength (3PBS). Also, the microstructure of the matrix and fibers were analyzed using a scanning electron microscope. The results show that the flowability and setting time increased with the increasing replacement of PVA fiber by ST fiber. At the PVA/ST fiber volume ratio of 1:1, the AAMs cured for 28 days achieved the highest UCS, IDT, UTS, and 3PBS, which were 32 %, 91 %, 80 % and 114.7 % higher than the AAMs without fiber reinforcement, respectively. The fracture pattern and microstructure illustrated that the best synergistic effect of hybrid fibers was achieved at the PVA:ST fiber ratio of 1:1, at which the small-sized PVA fibers and large-sized ST fibers can inhibit the propagation of micro- and macro-cracks at early and late deformation processes. Furthermore, the ultimate elongation of the composite was improved from 2.62 % to 4.21 % and 5.3 % by modifying the PVA fiber with acrylic polyurethane copolymer and replacing the PVA fiber with polyethylene fiber, respectively, implying the significance of surface hydrophilicity of low-modulus fiber on the ductility of AAMs. This study provided a guide for synthesizing and tailoring the mix design of hybrid-fiber-reinforced ternary AAMs with better strength and ductility. [ABSTRACT FROM AUTHOR]

In order to study the hardening characteristics and formation mechanism of concrete prepared with highly mineralized mine water (which is called CMW for short), four mineralized mine water mixtures with different dosages (25%, 50%, 75%, and 100%) were prepared, and concrete specimens were made using coal-based solid waste (gangue and fly ash) as the aggregate and aged for a 70 d long-age test. Strength tests, scanning electron microscopy (SEM), and X-ray diffraction (XRD) measurements were performed to determine the relationship between the hardening strength and aging time. The hardening mechanism was studied based on the changes in the characteristic composition and microstructure. The results showed that, compared with the two-stage hardening in σC seen in conventional concrete prepared with ground purified water, drinking water, or surface water (which is called CN-MW for short), σC in our experiments had three-stages. The stages included a growth period (0~28 d), in which σC of the 28 d concrete samples prepared with mine water dosages of 25%, 50%, 75%, and 100% increased by 18.0%, 36.4%, 57.2%, and 72.7%, respectively, compared with that of CN-MW; a rapid decline period (28~56 d), in which σC at 56 d decreased by 47.7%, 43.2%, 36.0%, and 30.5%, respectively, and finally, the stable period (56~70 d~long-age), in which the strength σC remained stable. The mechanisms of the hardening characteristics were different from those of CN-MW in the three stages. In the first stage (0~28 d), Friedel's salt and more ettringite were generated by the secondary hydration reaction, which filled the internal pores of the specimens and thus improved the compactness and σC. In the second stage (28~56 d), the amount of Friedel's salt and ettringite further increased, the crystals inside the specimens expanded, and macroscopic cracks appeared on the specimen surface, thus leading to the decrease in σC. In the third stage (56~70 d~long-age), the amount of Friedel's salt and ettringite plateaued, and σC entered a stable stage, decreasing by 52.5%, 47.8%, 40.4%, and 36.8%, respectively, compared with that of the specimens prepared without mine water. The hardening time of CMW was 42 d longer than that of conventional CN-MW, the hardening strength decreased significantly, and the σC at the final setting time was much lower than that of CN-MW. Our research results provide a reference for the filling strength design of coal mine rock stratum control. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method to quantitatively evaluate the quality of the carrier phase observation signals of the BeiDou Navigation Satellite System (BDS) during weak and moderate geomagnetic storms. We take a moderate geomagnetic storm that occurred on 12 May 2021 during the 25th solar cycle as an example. The results show that the newly defined PAS (Percentage of Affected Satellites) index shows significant anomaly changes during the moderate geomagnetic storm. Its variation trend has good correlations with the geomagnetic storm Kp index and Dst index. The anomaly stations are mainly distributed in the equatorial region and auroral region in the northern and southern hemispheres. The proposed PAS index has a good indication for both BDS2 and BDS3 satellites. We further validated this index by calculating the Precise Point Position (PPP) positioning error. We found that the anomaly period of PAS has strong consistency with the abnormal period of PPP positioning accuracy. This study could provide methodological support for the evaluation of the signal quality and analysis of positioning accuracy for the BeiDou satellite navigation system under different space weather conditions. [ABSTRACT FROM AUTHOR]

The prevalence of moving object data (MOD) brings new opportunities for behavior related research. Periodic behavior is one of the most important behaviors of moving objects. However, the existing methods of detecting periodicities assume a moving object either does not have any periodic behavior at all or just has a single periodic behavior in one place. Thus they are incapable of dealing with many real world situations whereby a moving object may have multiple periodic behaviors mixed together. Aiming at addressing this problem, this paper proposes a probabilistic periodicity detection method called MPDA. MPDA first identifies high dense regions by the kernel density method, then generates revisit time sequences based on the dense regions, and at last adopts a filter-refine paradigm to detect mixed periodicities. At the filter stage, candidate periods are identified by comparing the observed and reference distribution of revisit time intervals using the chi-square test, and at the refine stage, a periodic degree measure is defined to examine the significance of candidate periods to identify accurate periods existing in MOD. Synthetic datasets with various characteristics and two real world tracking datasets validate the effectiveness of MPDA under various scenarios. MPDA has the potential to play an important role in analyzing complicated behaviors of moving objects. [ABSTRACT FROM AUTHOR]

Background: Diclofenac diethylamine (DDEA) gel has demonstrated efficacy for treatment of ankle sprains in both the 1.16% four-times-daily (QID) and 2.32% twice-daily (BID) formulations. The objective of this study was to compare, for the first time, the efficacy of DDEA 2.32% gel BID and DDEA 1.16% gel QID. Methods: This was a phase 3, randomized, double-blind, multicenter, active-controlled, parallel-group study conducted in China from October 2019 to November 2020, designed to determine the noninferiority of DDEA 2.32% gel BID relative to DDEA 1.16% gel QID for treatment of grade I–II ankle sprain. At study entry, patients must have had pain on movement (POM) ≥50 mm on a 100-mm visual analogue scale (VAS), and not received any pain medication. The primary efficacy endpoint was the noninferiority of DDEA 2.32% gel BID vs DDEA 1.16% gel QID for POM as assessed by the patient using the 100-mm VAS, conducted on day 5. Secondary endpoints included measures of ankle tenderness, joint function, swelling, and patient-reported pain intensity and pain relief. Results: A total of 302 patients were randomized and 95.4% completed the study. The mean (SD) change in POM from baseline to day 5 using the 100-mm VAS was − 42.8 mm (19.7 mm) with DDEA 2.32% gel BID and − 43.1 mm (18.1 mm) with DDEA 1.16% gel QID for the per-protocol population. The least squares mean difference (DDEA gel 2.32% – DDEA gel 1.16%) at this timepoint was 1.11 mm (95% CI − 3.00, 5.22; P = 0.595), and the upper limit (5.22 mm) of the 95% CI was less than the noninferiority margin of 13 mm, demonstrating that DDEA 2.32% gel BID was noninferior to DDEA 1.16% gel QID. Similar trends were seen for the secondary efficacy endpoints. There was no significant difference in the incidence of treatment-emergent adverse events or adverse events adjudicated as being treatment related. All treatment-related adverse events were dermatological; one patient discontinued from the DDEA 2.32% gel BID arm due to application-site inflammation. Conclusions: DDEA 2.32% gel BID offers a convenient alternative to DDEA 1.16% gel QID, with similar pain reduction and relief, anti-inflammatory effects, and tolerability. Trial registration: NCT04052620. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a common malignant tumor with a high frequency of recurrence and metastasis, which are the major causes of death in patients. The prerequisite for the invasion and metastasis is the strong mobility of CRC cells to transport far away from the original site to the distant organs and tissues, where they settle down and proliferate. It was reported that the epithelial-mesenchymal transition (EMT) is involved in the occurrence and development of various tumors in the entire process of tumor invasion and metastasis. Therefore, as a vital factor for the biological characteristics of tumor cells, EMT markers may serve as prognostic predictors and potential therapeutic targets in CRC. This article mainly reviews the current status of CRC with metastasis, the studies of EMT, the possible relationship of EMT with CRC, as well as the potential targeted therapy. [ABSTRACT FROM AUTHOR]

Does severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection during ovarian stimulation affect assisted reproductive technology outcomes? This retrospective cohort study conducted at the Reproductive Medicine Centre of The First Affiliated Hospital of Anhui Medical University aimed to assess the effects of acute SARS-CoV-2 infection during IVF on treatment outcomes and the reproductive system. The study included 151 treatment cycles involving couples with coronavirus disease 2019 (COVID-19) during ovarian stimulation, along with 224 cycles of non-infected couples as a control group. Clinical characteristics and laboratory parameters were analysed, including total gonadotrophin dosage, duration of ovarian stimulation, number of oocytes retrieved, fertilization method, fertilization rate, and number of blastocyst embryos available. Forty-six follicular fluid samples, 38 semen samples and 78 embryo culture medium samples from patients with COVID-19 were tested for SARS-CoV-2 RNA using reverse transcription polymerase chain reaction assay. The treatment and control groups showed similar cycle characteristics, including fertilization method, total gonadotrophin dosage and duration of ovarian stimulation. The mean number of oocytes retrieved per cycle and rate of mature oocytes in intracytoplasmic sperm injection cycles were comparable. No significant difference was observed in the total number of blastocyst embryos available between the groups. Furthermore, no SARS-CoV-2 RNA was detected in any of the samples of patients with COVID-19. In conclusion, acute SARS-CoV-2 infection during ovarian stimulation does not have a significant impact on IVF treatment outcomes. Additionally, no risk to the reproductive system was observed in patients infected with SARS-CoV-2. Therefore, individuals with asymptomatic or mild COVID-19 can safely continue IVF treatment. Future research is needed to investigate the long-term effects of COVID-19 on fertility and reproductive outcomes. [ABSTRACT FROM AUTHOR]

Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective: To explore the utility of Internet of Things in power transformer fault diagnosis system. Methods: A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results: The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system. [ABSTRACT FROM AUTHOR]

Objective A positive result of SARS-CoV-2 nucleic acid detection provides critical laboratory evidence for clinical confirmed diagnosis, pandemic status evaluation, a pandemic prevention plan, treatment of infected people with symptoms, and protection of uninfected people. This study aims to provide a practical reference for SARS-CoV-2 nucleic acid–related research and detection. Methods Our laboratory has established policies combining personnel management and quality control practices for SARS-CoV-2 nucleic acid detection during the pandemic. Results In this article, we describe cross-department personnel management and key points of personal protection and quality control in the testing process. We also report on the differences in detection and the compatibility between different brand kits. Conclusion It is critical to maintain a standard and accurate laboratory operation for nucleic acid testing. [ABSTRACT FROM AUTHOR]

Fluoride phosphors as red components for warm white LEDs have attracted a tremendous amount of research attention. But these phosphors are extremely sensitive to moisture, which seriously limits their practical industrial applications. To tackle this problem, unlike all the straightforward preventive strategies, a reverse strategy "Good comes from bad" was successfully developed to treat the degraded K2SiF6 : Mn4+ (D‐KSFM) phosphor in the present study, which not only completely restores the luminescence properties, but also significantly enhances the moisture resistance at the same time. After treatment with an oxalic acid solution as restoration modifier, the emission intensity of the D‐KSFM phosphor can be restored to 103.7% of the original K2SiF6 : Mn4+ red phosphor (O‐KSFM), and the moisture resistance is remarkably improved. The restored K2SiF6 : Mn4+ (R‐KSFM) maintains approximately 62.3% of its initial relative emission intensity after immersing in deionized water for 300 min, while the reference commercial K2SiF6 : Mn4+ with a protective coating (C‐KSFM) is only 33.2%. As a proof of general applicability, this strategy was also conducted to K2TiF6 : Mn4+ phosphor, which is less moisture‐stable than K2SiF6 : Mn4+. The luminescence intensity of the degraded K2TiF6 : Mn4+ (D‐KTFM) phosphor can be restored to 162.6% of original level of the K2TiF6 : Mn4+ synthesized through a cation exchange approach without any treatment (O‐KTFM). The emission intensity of the restored K2TiF6 : Mn4+ (R‐KTFM) phosphor retains 62.8% of its initial emission intensity after soaking in deionized water for 300 min. Finally, the R‐KSFM phosphors were packaged into white light‐emitting diodes with blue InGaN chips and Y3Al5O12 : Ce3+ yellow phosphors. The WLEDs display excellent color rendition with higher color rendering index, lower color temperature (WLED‐II: Ra=83.6, R9=57.3, 3743 K, ηl=199.68 lm/W; WLED‐III: Ra=90.4, R9=94.2, 2892 K, ηl=183.3 1 m/W). The above results show that the reverse strategy can be applied in those phosphor materials with poor moisture resistance to restore luminescence properties and improve moisture resistance without excessively care about the deterioration during the production, storage and transportation. [ABSTRACT FROM AUTHOR]

The dispersion of recycled glass fiber reinforced plastic (rGFRP) fibers in cement mortars has a significant impact on both the workability and mechanical properties of the composites. In this study, the dispersity of three types of rGFRP fibers was improved in two methods: Method 1: the dispersant (Sodium Hexametaphosphate, SHMP) was directly added into the mortar, and Method 2: the rGFRP fibers were pre-dispersed by the SHMP solution and then added into the mortar. The effect of the two fiber-dispersion methods on workability, mechanical properties, and microstructure of the rGFRP fiber reinforced mortars were investigated by Zeta potential test, mechanical tests, Mercury intrusion porosimetry and X-ray micro-CT analysis. The results showed that Method 2 had more significant improvement in mechanical strength than Method 1 for all types of rGFRP fiber reinforced mortar. The flexural strength of the mortars increased by 58.3%, 43.2% and 105.4% with 7.5 wt% GF1, GF2 and GF3 treated by Method 2, respectively, compared to their untreated counterparts. The pre-dispersion of the fibers with SHMP facilitated better dispersion of the large rGFRP fiber clusters in the mortar by increasing the surface charge of the fibers and enhancing repulsion between them, thus improving the mechanical performance of the mortar. The results of this study will promote the utilization of rGFRP fiber in construction materials. • The SHMP improved the dispersity of rGFRP fiber and the mechanical strength of mortar. • Pre-dispersion can better improve the dispersity of rGFRP fiber. • The Fe 2 O 3 –Fe 3 O 4 coating increased the visualization of fiber in X-ray CT scanning. • The porosity of pre-treated fiber mortar decreased as fiber clusters were separated. [ABSTRACT FROM AUTHOR]

Abstract: Background and Aim: The aim of our study was to investigate the immunomodulatory effect and short‐term efficacy and long‐term prognosis of decompensated liver cirrhosis patients caused by hepatitis B after a double transplantation with human umbilical cord mesenchymal stem cells (hUCMSCs). Methods: Fifty inpatients were recruited and given the same medical treatments, receiving hUCMSCs injection intravenously. Fifty‐three patients (Group B) matched for age, sex, and baseline alanine aminotransferase, aspartate aminotransferase, albumin, total bilirubin, prothrombin time, and model for end‐stage liver disease score and Child–Pugh classification, acted as the control group. Results: Interleukin‐6 and tumor necrosis factor alpha levels markedly decreased, and interleukin‐10 level apparently increased in Group A at 2 and 4 weeks after treatment. Transforming growth factor beta in Group A increased more remarkably at 2 weeks after treatment. T4 cells and Treg cells in Group A were apparently higher than those in Group B at 2 and 4 weeks after treatment, and T8 cells and B cells were significantly lower than those in Group B. Aspartate aminotransferase levels in Group A were dramatically declining at 8 and 12 weeks after treatment. Levels of albumin, total bilirubin, and prothrombin time in Group A were apparently improved from 4 to 12 weeks after treatment. The improvements in model for end‐stage liver disease and Child–Pugh scores in Group A were notably superior to those in Group B from 4 to 36 weeks after treatment. There were no remarkable differences in the incidence of developing liver failure throughout the follow‐up period, but the mortality rate of Group A was lower than that of Group B. Conclusion: This therapeutic method may be an appropriate choice for patients with decompensated liver cirrhosis. [ABSTRACT FROM AUTHOR]