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Searching for a stable and efficient photocatalyst still presents a variety of challenges, when photocatalytic technology is widely used today. In this paper, oxygen-vacancy BiOCl with different masses was loaded on Bi4O5Br2 nanosheets by a simple two-step method. The UV–Dis spectrum showed that the absorption range of the complex to visible light was larger than that of the two pure substances. In addition, the PL, i – t and EIS characterization prove that the formation of heterogeneous interface between the two materials accelerated the charge transfer in the semiconductor, eventually making photocatalytic efficiency significantly increased. The results showed that the 1 wt.% Ov-BOC@BOB has the best degradation performance, which was seven and four times than that of Ov-BOC and BOB within 120 min, respectively. Free radical capture experiment further confirmed that the charge transfer between oxygen-vacancy BiOCl and Bi4O5Br2 conforms to the Z-type transfer mechanism, such a charge-transfer mechanism would leave behind strongly reducing electrons and strongly oxidizing holes, respectively. The degradation rate of ciprofloxacin (CIP) was not significantly reduced after five cycles of experiments, indicating that the compound had good stability. This study provides a feasible idea for exploring stable and efficient photocatalysts. Z-type heterojunction of BiOCl@Bi4O5Br2 with oxygen vacancy was prepared. The construction of heterostructures facilitates the separation and transfer of photogenerated charges. Free radical capture experiment further confirmed that the charge transfer between oxygen-vacancy BiOCl and Bi4O5Br2 conforms to the Z-type transfer mechanism. [ABSTRACT FROM AUTHOR]

The rate reduction models have been widely used to model the recurrent failure data for their capabilities in quantifying the repair effects. Despite the widespread popularity, there have been limited studies on statistical inference of most failure rate reduction models. In view of this fact, this study proposes a semiparametric estimation framework for a general class of such models, called extended geometric failure rate reduction (EGFRR) models. Covariates are considered in our analysis and their effects are modeled as a log‐linear factor on the baseline failure rate. Unlike the existing inference methods for the EGFRR models that assume the failure data are censored at a fixed number of failures, our study considers covariates and time‐censoring, which are more common in practice. The semiparametric maximum likelihood (ML) estimators are obtained by carefully constructing the likelihood function. Asymptotic properties including consistency and weak convergence of the ML estimators are established by using the properties of the martingale process. In addition, we show that the semiparametric estimators are asymptotically efficient. A real example from the automobile industry illustrates the usefulness of the proposed framework and extensive simulations show its outstanding performance when comparing with the existing methods. [ABSTRACT FROM AUTHOR]

Dengue has been as an endemic with year‐round presence in Singapore. In the recent years 2013, 2014, and 2016, there were several severe dengue outbreaks, posing serious threat to the public health. To proactively control and mitigate the disease spread, early warnings of dengue outbreaks, at which there are rapid and large‐scale spread of dengue incidences, are extremely helpful. In this study, a two‐step framework is proposed to predict dengue outbreaks and it is evaluated based on the dengue incidences in Singapore during 2012 to 2017. First, a generalized additive model (GAM) is trained based on the weekly dengue incidence data during 2006 to 2011. The proposed GAM is a one‐week‐ahead forecasting model, and it inherently accounts for the possible correlation among the historical incidence data, making the residuals approximately normally distributed. Then, an exponentially weighted moving average (EWMA) control chart is proposed to sequentially monitor the weekly residuals during 2012 to 2017. Our investigation shows that the proposed two‐step framework is able to give persistent signals at the early stage of the outbreaks in 2013, 2014, and 2016, which provides early alerts of outbreaks and wins time for the early interventions and the preparation of necessary public health resources. In addition, extensive simulations show that the proposed method is comparable to other potential outbreak detection methods and it is robust to the underlying data‐generating mechanisms. [ABSTRACT FROM AUTHOR]

Many large organizations have developed ambitious programs to build reliability databases by collecting field failure data from a large variety of components. To make the database concise, only the number of component replacements in a component position during an operation time interval is reported in these databases. This leads to time-censoring in the aggregate failure data. Statistical inference for the time-censored aggregate data is challenging, because the likelihood function based on some common lifetime distributions can be intractable. In this study, we propose a general parametric estimation framework for the aggregate data. We first use the gamma distribution and the Inverse Gaussian (IG) distribution to model the aggregate data. Bayesian inference for the two models is discussed. Unlike the gamma/IG distribution, other lifetime distributions involve multiple integrals in the likelihood function, making the standard Bayesian inference difficult. To address the estimation problem, an approximate Bayesian computation algorithm that does not require evaluating the likelihood function is proposed, and its performance is assessed by simulation. As there are several candidate distributions, we further propose a model selection procedure to identify an appropriate distribution for the time-censored aggregate data. A real aggregate dataset extracted from a reliability database is used for illustration. [ABSTRACT FROM AUTHOR]

Highlights • A Wiener process model based on random acceleration effect is proposed. • The IG distribution is used to model the heterogeneous degradation rate. • The model is extended to constant-stress ADT analysis. • EM algorithm is developed for model parameter estimation. Abstract Due to the variability of raw materials and the fluctuation in the manufacturing process, degradation of products may exhibit unit-to-unit variability in a population. The heterogeneous degradation rates can be viewed as random effects, which are often modeled by a normal distribution. Despite of its mathematical convenience, the normal distribution has certain limitations in modeling the random effects. In this study, we propose a novel random-effects Wiener process model based on ideas from accelerated failure time principle. An inverse Gaussian (IG) distribution can be used to characterize the unit-specific heterogeneity in degradation paths, which overcomes the disadvantages of the traditional models and provides more flexibility in the degradation modeling using Wiener processes. Properties of the model are investigated, and statistical inference based on the maximum likelihood estimation and the EM algorithm is established. An extension of the model to the constant-stress accelerated degradation test (ADT) is developed. The effectiveness and applicability of the proposed model are validated using a laser degradation dataset and an LED ADT dataset. [ABSTRACT FROM AUTHOR]

Ruthenium(II) polypyridyl complexes, [Ru(phen) 2 bppp]2+ (Ru 1), [Ru(phen) 2 dppp2]2+ (Ru 2) and [Ru(phen) 2 dppp3]2+ (Ru 3), have been synthesized and characterized in this work, and the binding of these complexes with double-stranded RNA poly(A)*poly(U) have been studied. [Display omitted] A series of ruthenium(II) complexes [Ru(phen) 2 dppp3]2+ (Ru 1 , phen = 1,10-phenanthrolline; dppp3 = pyrido[3′,4′:5,6]pyrazino[2,3- f ][1,10]phenanthroline), [Ru(phen) 2 dppp2]2+ (Ru 2 , dppp2 = pyrido[2′,3′:5,6]pyrazino[2,3- f ][1,10]phenanthroline) and [Ru(phen) 2 bppp]2+ (Ru 3 , bppp = 10-bromopyrido[2′,3′:5,6]pyrazino-[2,3- f ][1,10]phenanthroline) have been synthesized and characterized that helps to further investigate RNA-complex interactions. The viscosity measurement, UV–vis absorption spectrum study as well as EB (ethidium bromide) fluorescence competition have been explored to prove that the binding modes of Ru 1 , Ru 2 and Ru 3 with double-stranded RNA were intercalation, and these binding affinities of Ru 1 , Ru 2 and Ru 3 toward poly(A)*poly(U) (where "*" denotes the Watson-Crick base) have followed the order of Ru 1 > Ru 2 > Ru 3. The analysis of thermal denaturation results obtained the identical conclusion and revealed that three complexes have positive roles in the stabilization of RNA base pairs. To my knowledge, this study presents the first Ru(Ⅱ) polypyridine complexes with the most effective thermal stabilization of RNA poly(A)*poly(U). All results showed the structural effects of complex main ligands in RNA study, in which the RNA-affinities were impacted greatly by the positions of substituents, and the sizes of functional groups were also key factors controlling the interaction between the complexes and double-stranded RNA. [ABSTRACT FROM AUTHOR]

The generalized Pareto distribution (GPD) is widely used for extreme values over a threshold. Most existing methods for parameter estimation either perform unsatisfactorily when the shape parameterkis larger than 0.5, or they suffer from heavy computation as the sample size increases. In view of the fact thatk> 0.5 is occasionally seen in numerous applications, including two illustrative examples used in this study, we remedy the deficiencies of existing methods by proposing two new estimators for the GPD parameters. The new estimators are inspired by the minimum distance estimation and theM-estimation in the linear regression. Through comprehensive simulation, the estimators are shown to perform well for all values ofkunder small and moderate sample sizes. They are comparable to the existing methods fork< 0.5 while perform much better fork> 0.5. [ABSTRACT FROM AUTHOR]

Xiamen is the epitome of having steady economic growth and non-negligible environmental stress over decades. Several restoration programs have been applied to address the conflicts between heavy environmental pressures and human activities, but the response of current coastal protection policies to the marine environment remains to be assessed. Therefore, to assess the effectiveness and efficiency of marine conservation policies under regional economic growth in Xiamen, quantitative techniques including elasticity analysis and dummy variable regression models were applied. Here we show the potential relationship between seawater quality (pH, COD, DIN and DRP) and economic growth including Gross Domestic Product (GDP) and Gross Ocean Product (GOP), to evaluate the ongoing related policies by using over 10 years of data (2007–2018). According to our estimates, a GDP growth rate of 8.5% represents a stable economic climate that is favorable for the overall rehabilitation of the local coastal environment. The results of the quantitative research indicate a strong relationship between economic development and seawater quality, with marine protection regulations serving as the direct cause. As GDP growth and pH are significantly positively correlated (coef. = 0.8139, p = 0.012), ocean acidification has decreased over the last decade. With an inversely proportional correlation with GDP (coef. = 0.8456, p = 0.002) and GOP (coef. = 0.8046, p = 0.005), the trend in COD concentrations effectively meets the targets of current pollution control legislation. By using a dummy variable regression model, we found that legislation is the most effective way in seawater recovery in the GOP section, and positive externalities of marine protection frameworks are also estimated. Meanwhile, it is predicted that the negative effects from the non-GOP section will gradually affect the coastal environmental quality gradually. An overall framework for controlling marine pollutant discharges, giving equal attention to maritime and non-maritime anthropogenic activities should be promoted and updated. • The coastal seawater conditions with economic growth in Xiamen Bay were proposed. • A systematic evaluation of environmental protection policies was formed. • The relationship between seawater quality and economic development was analyzed. • Quantitative methods were adopted to evaluate the effectiveness of policy. • To provide policy-making suggestions for sustainable coastal management. [ABSTRACT FROM AUTHOR]

Field data provide important information about product quality and reliability. Many large organizations have developed ambitious reliability databases to trace field failure data of a variety of components on the systems they operate and maintain. Due to the exponential distribution assumption for the component lifetimes, the data in these databases are often aggregated. Specifically, individual lifetimes of the components are not available. Instead, each recorded data point is the cumulative operating time of one component position from system installation to the last component replacement, and the number of replacements in between. In the literature, the gamma distribution and the inverse Gaussian (IG) distribution have been used to fit the aggregate data, while the operating environment of different systems is often assumed the same. In order to capture possible heterogeneities among the systems, this study proposes the gamma random effects model and the IG random effects model. The expectation–maximization algorithm is used for point estimation of the parameters and an algorithm based on the generalized fiducial inference method is proposed for interval estimation. Simulation studies are conducted to assess the performance of the proposed inference methods. A real aggregate dataset is used for illustration. [ABSTRACT FROM AUTHOR]

Because of the exponential distribution assumption, many reliability databases recorded data in an aggregate way. Instead of individual failure times, each aggregate data point is a summation of a series of collective failures representing the cumulative operating time of one component position from system commencement to the last component replacement. The data format is different from traditional lifetime data and the statistical inference is challenging. We first model the individual component lifetime by a gamma distribution. Confidence intervals for the gamma shape parameter can be constructed using a scaled χ2approximation to a modified ratio of the geometric mean to the arithmetic mean, while confidence intervals for the gamma rate and mean parameters, as well as quantiles, are obtained using the generalized pivotal quantity method. We then fit the data using the inverse Gaussian (IG) distribution, a useful lifetime model for failures caused by degradation. Procedures for point estimation and interval estimation of parameters are developed. We also propose an interval estimation method for the quantiles of an IG distribution based on the generalized pivotal quantity method. An illustrative example demonstrates the proposed inference methods. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

In addition to conventional censoring schemes such as Type-I or Type-II censoring, progressively censoring is a useful method to reduce cost and obtain additional reliability information in accelerated life testing. Statistical inference for accelerated life testing (ALT) with Weibull distribution and progressively censoring is found to be difficult. This paper develops generalized fiducial inference techniques for the constant-stress ALT model with Weibull distribution and progressively Type-II censoring. Simulation studies reveal the good performance of the proposed inference methods. An example is given for illustration. [ABSTRACT FROM PUBLISHER]

Multifunctional sensing, a strategy to improve the efficiency of analysis and detection, has attracted much attention. In this study, a multifunction sensing platform was developed for temperature, pH, and proton pump inhibitors (PPI) with fluorescence carbon dots. Solvent-dependent carbon dots (PG-CDs) were synthesized through a one-step solvothermal method from phloroglucinol with the assistance of HCl. Base on the effect of functional groups on the surface, solvent-dependent behavior, temperature and pH responsive fluorescence can be observed. The PG-CDs display a reversible temperature-sensitive fluorescent behavior within 15–65 °C in N, N-dimethylacetamide. The fluorescence intensity of PG-CDs also responded to pH in a range of 3.0–7.0 with good reversibility and anti-interference. Therefore, the platform for temperature and pH sensing was proposed. In addition, since the synergistic effect of dynamic and static quenching, proton pump inhibitors (PPI) can be detected sensitively, including esomeprazole (EPZ), omeprazole (OPZ), and rabeprazole (RPZ). The linear range of detection for EPZ, OPZ, and RPZ is 2.0–80.0, 2.0–75.0, and 10.0–200.0 μM and the limits of detection is 0.29, 0.55, and 2.99 μM, separately. The recoveries for real samples were between 91.83 and 102.3%, which indicated that the platform for PPI sensing had good accuracy. [Display omitted] • Solvent-dependent carbon dots was prepared from phloroglucinol with the assistance of HCl. • A multifunction sensing platform was developed for temperature, pH, and proton pump inhibitors with fluorescence carbon dots. • Base on the effect of functional groups on the surface, solvent-dependent behavior, temperature and pH responsive fluorescence can be observed. • Since the synergistic effect of dynamic and static quenching, proton pump inhibitors can be detected sensitively. [ABSTRACT FROM AUTHOR]

Denitrifying phosphorus removal is very effective in retarding competition between denitrifying bacteria and phosphorus removing organisms for limited carbon in influent, and can enhance performances of biological nutrient removal processes (BNR) treating low strength wastewater. The aim of this study is to demonstrate the effects of influent split ratios on enrichment of denitrifying phosphorus removing bacteria (DPB) in a two-line BNR process. Performances of the process under different influent split ratios were investigated; profiles of intracellular compounds were characterized; mass flow were also described. The results showed that influent split ratio had no obvious effects on COD and total phosphorus distribution in bulk water when influent COD concentration was in the range of 172.0–183.3 mg/L, but affected intracellular content in activated sludge significantly. Better performances for ammonia removal were observed under influent split ratios (AN/AO, ratio of influent flow rates between the anaerobic–anoxic–anoxic line and the anaerobic–aerobic–aerobic line) of 10:10, 12:8 and 8:12 than under 15:5. An optimized balance between anoxic and aerobic phosphorus removal could be reached by adjusting the influent split ratio. Total nitrogen removal rate was improved by 16% through increasing the influent AN/AO split ratio from 8:12 to 12:8. This study also helped to understand role of split ratio in balancing pollutants removal within BNR processes. [ABSTRACT FROM AUTHOR]

[Display omitted] • The highly thermosensitive N-doped carbon dots (N-CDs) were prepared by NH 4 Cl as an inorganic dopant. • The N-CDs display marvelous thermosensitive fluorescence response with good linear relationship, reversibility, and restorability. • The mechanism of the thermosensitive fluorescence response of N-CDs was the activation of nonradiative channels. • The N-CDs can be applied to selective detection of 6-mercaptopurine based on inner filter effect and no additional complex nanomaterials are required. Carbon dots (CDs) have sparked immense interest because of their attractive optical properties in recent years, but the synthesis of multifunction CDs is still worth investigating. In this paper, a thermosensitive N-doped carbon dots (N-CDs) were prepared by facile hydrothermal method with sodium citrate and NH 4 Cl as raw materials. The N-CDs with a fluorescence quantum yield of 27.4% are excitation-independent, small size (1.34 nm), and good water-soluble. Importantly, the N-CDs display marvelous thermosensitive fluorescence response with good linear relationship, reversibility, and restorability in a range from 15 to 75 ℃, which can be applied to temperature sensing. Detailed characterization show that the thermosensitive fluorescence mechanism was the activation of nonradiative channels in higher temperatures. Besides, based on the inner filter effect (IFE), N-CDs could be served as a fluorescent probe for selective detection of 6-mercaptopurine (6-MP) without any modification or functionalization. The simple and efficient strategy for 6-MP sensing was quantified in a linear range of 1–90 µM with a detection limit of 0.86 µM and also can be applied to lake water and serum samples with satisfying recoveries. [ABSTRACT FROM AUTHOR]

The prediction of remaining useful life (RUL) is a critical component of prognostic and health management for industrial systems. In recent decades, there has been a surge of interest in RUL prediction based on degradation data of a well‐defined degradation index (DI). However, in many real‐world applications, the DI may not be readily available and must be constructed from complex source data, rendering many existing methods inapplicable. Motivated by multivariate sensor data from industrial induction motors, this paper proposes a novel prognostic framework that develops a nonlinear DI, serving as an ensemble of representative features, and employs a similarity‐based method for RUL prediction. The proposed framework enables online prediction of RUL by dynamically updating information from the in‐service unit. Simulation studies and a case study on three‐phase industrial induction motors demonstrate that the proposed framework can effectively extract reliability information from various channels and predict RUL with high accuracy. [ABSTRACT FROM AUTHOR]

• A Weibull proportional hazards model is adopted to model the hazard rate of the hard failure. • The degradation level is treated as a multiplicative time-varying covariate. • The closed-form of the RUL distribution is derived based on the Brownian bridge theory. • The optimal maintenance schedule is updated when new degradation signals are available. The remaining useful lifetime (RUL) estimated from the in-situ degradation data has shown to be useful for online predictive maintenance. In the literature, the RUL is often estimated by assuming a soft-failure threshold for the degradation data. In practice, however, systems may not be subject to the degradation-induced soft failures. Instead, the systems are deemed to be fail when they cannot perform the intended function, and such failures are known as hard failures. Because there are no fixed thresholds for hard failures, the corresponding RUL estimation is not an easy task, which causes difficulties in finding the optimal maintenance schedule. In this study, a Weibull proportional hazards model is proposed to jointly model the degradation data and the failure time data. The degradation data are treated as the time-varying covariates so that the degradation does not directly lead to system failures, but increases the hazard rate of hard failures. A random-effects Wiener process is proposed to model the degradation data by considering the system heterogeneities. Based on the developed proportional hazards model, closed-form distribution of the RUL is derived upon each inspection and the optimal maintenance schedule is then obtained by minimizing the system maintenance cost. The proposed maintenance strategy is successfully applied to predictive maintenance of lead-acid batteries. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer is the most common cancer type worldwide, with its survivors often experiencing physical and psychosocial health problems. Wearable device use is an innovative and effective way to promote physical activity and improve health‐related outcomes in breast cancer survivors; however, the current evidence is unclear. We aimed to determine the effects of wearable devices on physical activity and health‐related outcomes in breast cancer survivors. Methods: PubMed, Embase, Web of Science, and Cochrane Library databases were searched to identify eligible studies from inception to September 2022. Additional relevant studies were obtained from the reference lists of the identified studies. Two reviewers independently screened the eligible studies, appraised the risk of bias, and extracted the data. Meta‐analysis was conducted using Review Manager version 5.3. Findings: Sixteen randomized controlled trials were included. Physical activity tracking and pedometer‐based interventions improved moderate‐intensity physical activity (standardized mean difference [SMD] = 0.32, 95% confidence interval [CI]: 0.17–0.46, p < 0.0001), moderate‐to‐vigorous physical activity (SMD = 0.85, 95%CI: 0.38–1.32, p = 0.0004), total physical activity (SMD = 0.51, 95%CI: 0.12–0.90, p = 0.01), quality of life (SMD = 0.17, 95%CI: 0.03–0.31, p = 0.01), physical function (SMD = 0.21, 95%CI: 0.04–0.38, p = 0.02), and mood state profiles (SMD = −0.58, 95%CI: −1.13 to 0.02, p = 0.04) in breast cancer survivors. However, the effects of low‐intensity physical activity, vigorous‐intensity physical activity, fatigue, anxiety, depression, and sleep quality could not be ascertained. Conclusions: Physical activity tracking and pedometer‐based interventions were effective in increasing physical activity and improving health‐related outcomes in breast cancer survivors. Implications for nursing practice: This review offers availability of credible evidence supporting the potential usefulness and effectiveness of wearable physical activity trackers on physical activity and health‐related outcomes in breast cancer survivors. [ABSTRACT FROM AUTHOR]

V‐doped WO3 microspheres composed of nanorods radially grown from the center were fabricated via a simple hydrothermal method. The as‐obtained products were characterized by several techniques such as X‐ray powder diffraction (XRD), energy dispersive spectrometer (EDS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), Raman spectrum, and Brunauer–Emmett–Teller (BET). It was found that V atoms were incorporated inside the hexagonal WO3 lattice. On the basis of time‐dependent experiments, the formation mechanism for the as‐prepared V‐doped WO3 micro‐nanostructures was discussed. In addition, the catalytic properties of the synthesized material on the thermal decomposition of ammonium perchlorate (AP) were evaluated by thermal gravimetric and differential thermal analysis (TG/DTA), which showed the thermal decomposition temperatures of AP in the presence of 1, 2, and 4 wt% of V‐doped WO3 micro‐nanostructures were reduced to 399, 393, and 380°C, respectively. [ABSTRACT FROM AUTHOR]

Semi-supervised dimensionality reduction has attracted an increasing amount of attention in this big-data era. Many algorithms have been developed with a small number of pairwise constraints to achieve performances comparable to those of fully su pervised methods. However, one challenging problem with semi-supervised approaches is the appropriate choice of the constraint set, including the cardinality and the composition of the constraint set which, to a large extent, affects the performance of the resulting algorithm. In this work, we address the problem by incorporating ensemble subspaces and active learning into dimensionality reduction and propose a new global and local scatter based semi-supervised dimensionality reduction method with active constraints selection. Unlike traditional methods that select the supervised information in one subspace, we pick up pairwise constraints in ensemble subspaces, where a novel active learning algorithm is designed with both exploration and filtering to generate informative pairwise constraints. The automatic constraint selection approach proposed in this paper can be generalized to be used with all constraint-based semi-supervised learning algorithms. Comparative experiments are conducted on four face database and the results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We fabricated internal dual-emission carbon dots (CDs) using a facile hydrothermal treatment of eosin Y and ethylenediamine (EDA). Y-CDs exhibited distinct double peaks at 384 and 520 nm upon 320 nm excitation. The fluorescence (FL) intensities of double emissions gradually decreased along with the increase in the concentration of procainamide (PA). Since the fluorescence alteration could be monitored, Y-CDs were employed as an efficient platform for the FL sensing of PA over the linear ranges for double emissions from 4 to 18 μM and 20 to 400 μM, with a detection limit of 0.9 μM. The satisfactory accuracy and reliability in practical applications were investigated for the sensitive detection of PA in unqualified cosmetics, which took advantage of the inner filter effect (IFE) between Y-CDs and PA. [ABSTRACT FROM AUTHOR]

[Display omitted] Bisphenol A (BPA), a chemical present in many hard plastics and widely applied in human daily life, is linked by experts to higher doses of BPA causing side effects on health. To this end, BiO 2-x /Bi 2 MoO 6 composites were constructed by a solvothermal method and utilized as a photocatalyst to degrade BPA under simulated sunlight irradiation. It manifested that the increased oxygen vacancies and the optimized electron structure are the key factors responsible for the enhanced photodegradation ability of BiO 2-x /Bi 2 MoO 6 when compared with the pure Bi-based samples. The mechanism analysis suggested that the ·O 2 –, h+, and ·OH are the main active species involved in the reaction, and ·O 2 – plays a key role in the degradation of BPA. In addition, DFT computation manifests that the improved photodegradation performance of the constructed BiO 2-x /Bi 2 MoO 6 is also related to the enhanced adsorption ability of BPA over BiO 2-x /Bi 2 MoO 6. [ABSTRACT FROM AUTHOR]

Nanoparticle-built FeVO 4 microspheres were successfully prepared by a hydrothermal approach. The prepared microspheres exhibit well electron transport greatly enhancing the low temperature catalytic activity in ammoxidation of DCT to DCBN. [Display omitted] • Nanoparticle-built FeVO 4 microspheres were synthesized via one-step hydrothermal approach. • The FeVO 4 microspheres exhibit unprecedented low temperature catalytic activity. • The conversion of ammoxidation of DCT to DCBN catalyzed by FeVO 4 microspheres reaches up to 87% at 320 °C. FeVO 4 microspheres were successfully fabricated by a hydrothermal approach. Characterization results revealed that the FeVO 4 microspheres are composed of numerous nanoparticles. In the ammoxidation of 2,6-dichlorotoluene (DCT) to 2,6-dichlorobenzonitrile (DCBN) catalyzed by FeVO 4 microspheres, the conversion of DCT reaches up to 87% at a much lower temperature of 320 °C in comparison to previously reported catalysts (380–420 °C). The reason for this is that irons greatly improve the electron transport, leading to well reduction redox properties of vanadium species which are important to enhance the initial activation efficiency of C-H bond in methyl group. [ABSTRACT FROM AUTHOR]

The performance of units in the same batch can exhibit considerable heterogeneity due to the variation in the raw materials and fluctuation in the manufacturing process. For products suffering performance degradation in their use, such heterogeneity often results in an increase in the dispersion of the degradation paths of units in a population. The degradation rate of products can be unit-specific and often treated as random effects. This paper develops a novel random-effects Wiener process model to account for the unit-to-unit heterogeneity in the degradation, where the generalized inverse Gaussian (GIG) distribution is used to model the unit-specific degradation rate. The GIG distribution is a very general distribution with broad applications, which includes the inverse Gaussian (IG) distribution and the Gamma distribution as special cases. We investigate the model properties and develop an expectation maximization (EM) algorithm for parameter estimation. By comparing the proposed model with existing models on two real degradation datasets of the infrared LEDs and the GaAs lasers, we show that the proposed model is quite effective for degradation modeling with heterogeneous rates. [ABSTRACT FROM AUTHOR]

• Vanadium-based mixed mental oxides were investigated for the ammoxidation of DCT to DCBN. • High vanadic reducibility of catalyst is beneficial to enhance the conversion of DCT. • Lewis acid sites (containing amount and strength) are helpful to increase the selectivity to DCBN. Vanadium composite different metal oxides such as VAlO, VTiO, VFeO and VWO were synthesized via a one-pot hydrothermal approach and subsequent annealing treatment, and used as catalysts for the ammoxidation of 2, 6-dichlorotoluene (DCT) to 2, 6-dichlorobenzonitrile (DCBN). XRD, SEM, TEM, XPS, FTIR, TPD, TPR and BET technologies were taken on to investigate the correlations between the physical-chemical characteristics and reactivities. The results have revealed that high vanadic reducibility of catalyst is beneficial to enhance the activity resulting in high conversion of DCT, but tends to total oxidation leading to low selectivity. The surface Lewis acid sites (containing strong and medium acidity) are helpful to increase the selectivity to DCBN. VFeO displays the best reducibility resulting in the highest conversion, but the least Lewis acid sites leading to the lowest selectivity, which is well contrary to VWO catalyst. VTiO showed the best catalytic properties in terms of yield, mainly attributed to its much larger specific surface area contributing to forming more moderate active sites with monolayer vanadia in tetracoordinated as well as abundant acid sites. This work provides valuable information for designing highly effective catalysts for the ammoxidation of DCT to DCBN. Vanadium-based mixed metal oxides including VAlO, VTiO, VFeO and VWO were synthesized and the correlations between surface Lewis acidity, reducibility and reactivity were investigated. High vanadic reducibility of catalyst is beneficial to enhance the activity and Lewis acid sites (containing amount and strength) are helpful to increase selectivity. [Display omitted] [ABSTRACT FROM AUTHOR]

• HPLC-RRS was first proposed for the detection of proton pump inhibitors (PPIs). • The established method has a satisfactory LOD of 5.6 to 10.1 ng mL−1. • The work was applied to the detection of PPIs in human serum samples with satisfactory results. • This work confirmed the potential of HPLC-RRS for detection. In the present study, a convenient, selective and sensitive method of high performance liquid chromatography combined with resonance Rayleigh scattering (HPLC-RRS) for the detection of proton pump inhibitors (PPIs) was proposed based on enhancing the volume and hydrophobicity of PPIs by introducing Eosin Y (EY) and Pd(II). In order to investigate the interaction and the mechanism of signal enhancement, the complexes were characterized by ultraviolet–visible spectrum (UV), dynamic light scattering (DLS), RRS, scanning electron microscope (SEM) and the calculation of the density functional theory (DFT). The results indicated that PPIs, Pd(II) and EY formed ionic associations at 1:1:2 due to electrostatic attraction, which increased the volume and hydrophobicity of the system, resulting in the enhancement of RRS signals. Under the optimized experimental conditions, a linear relationship was found between RRS intensity and the concentration of PPIs within 0.02–25.00 μg mL−1. The limits of detection were 10.1, 5.6, 7.5 and 8.3 ng mL−1 for rabeprazole, omeprazole, pantoprazole, and lansoprazole, respectively. In addition, the established simple, reliable and cheap method was validated and applied for the human serum samples, which indicated broad application prospects in drugs. [ABSTRACT FROM AUTHOR]

This study investigates the distribution, structural properties, and potential impacts of oceanic processes on microplastics (MPs) in the Taiwan Strait (TWS) and surrounding seas. With an average of 174 particles/m3, the MP abundance in surface seawater ranged from 84 to 389 particles/m3. MP abundance ranged from 16 to 382 particles/kg in sediments, with a median of 121 particles/kg. Fragment and fiber were the two most frequently detected shapes. These MPs were found to be composed primarily of carbon and oxygen elements at 70–90% levels using energy-dispersive X-ray spectroscopy. Additionally, several examples had trace levels of metallic components. Black was the color that MPs saw the most often out of all the hues. The two main types of polymers are polyester and rayon, and their production is influenced by home sewage discharge and synthetic fiber production. The main routes of MP transport were land source input, riverine input, and oceanic currents. This study showed that salinity affects the distribution of MPs, with high-salinity seawater serving to saturate their presence. On the other hand, upwelling raises MP concentrations by bringing nutrients from the deep to the surface. Furthermore, it has been discovered that the dilution of the Pearl River plume increases the MP prevalence in the region. The South China Sea Warm Current had the highest lateral MPs transport flux (2.1 × 1014 particles/y), which was followed by the Taiwan Strait Current area (1.0 × 1014 particles/y) and the Guangdong coastal areas (8.6 × 1013 particles/y). In sediments, the MP prevalence was inversely correlated with particle size. Flocculation processes probably made it easier for MPs to travel down the water column and deposit themselves on the aquatic substrate. Although the relationship between MPs, total organic carbon, and total organic nitrogen was not correlated, a favorable trend showed that MPs may discreetly contribute to carbon storage in coastal sediment. • Microplastics (MPs) from the Taiwan Strait to adjacent sea were investigated. • Riverine input and oceanic currents were the principal pathways for MP transport. • Upwelling leads to higher MP concentrations along with nutrient levels. • The net flux of lateral transport of MPs in the Taiwan Strait was estimated (2.0 × 1014 particles/y). [ABSTRACT FROM AUTHOR]

[Display omitted] • An automatic strategy for NMR data analysis was developed. • Artifacts like baseline drift and NMR peak position shifts can be corrected. • The developed strategy is comparable with current advanced tools. • The developed strategy can benefit geographical discrimination of Goji honey samples. 1H nuclear magnetic resonance (NMR)-based untargeted metabolomics has been extensively used for the geographical discrimination of food samples. In analyzing complex samples, this technique faces the challenges of baseline drift and NMR peak position shifts, resulting in inaccurate geographical discrimination by current chemometric models, like partial least squares. To address this problem, we provide a novel automatic untargeted chemometrics strategy named AntDAS-NMR in this work. AntDAS-NMR utilizes the originally acquired NMR spectra as inputs to automatically perform baseline drift correction, NMR peak detection, NMR peak-based spectrum alignment, and geographical discrimination analysis. The developed strategy is used for the geographical discrimination of Goji honey samples from the northwest zones of China. Results indicate that with the aid of AntDAS-NMR, the artifacts of baseline drifts and NMR peak shift that influence the geographical discrimination model can be reasonably resolved and that Goji honey samples from various provinces of China can be satisfactorily classified. Additionally, the performance of AntDAS-NMR is comparable with classic NMR data analysis tools, like COW, icoshift , PAFFT, and RAFFT. In conclusion, the AntDAS-NMR may be used as a candidate for 1H NMR-based untargeted metabolomics. [ABSTRACT FROM AUTHOR]

• Stable isotope and multi-element compositions of muskmelon were analyzed. • Geographical differences of muskmelon in Xinjiang were compared by ANOVA. • LDA modeling of muskmelon origin was achieved using multi-variate data. • LDA accuracies were 91.9 % for Hami PGI muskmelon products. • This strategy can be as an alternative tool for combating origin mislabeling. Xinjiang muskmelon is a popular fruit in China that is exported worldwide. In particular, Hami muskmelon is classified as a protected geographical indication (PGI) product, and consequently it is more desirable and sells at a higher market price than products from other regions. Origin mislabeling and fraudulent substitution of Hami PGI muskmelon products by non-PGI products are frequently reported, which damages its reputation and market advantage. In this study, the geographical origin of muskmelon from Xinjiang was investigated using stable isotopes and multi-element analyses with chemometrics. Four stable isotopes (δ 13C, δ 15N, δ 2H, δ 18O) and seventeen elemental contents (Na, Al, P, K, Ca, Cr, Mn, Co, Ni, Cu, As, Sr, Mo, Cd, Sb, Ba, Pb) of 239 batches of muskmelon were collected from 13 administrative regions distributed across Xinjiang Province. One-way analysis of variance (ANOVA) was used to compare these regional differences using stable isotopes and multi-elements, and showed that a single variable could not fully classify all sample origins. A multivariate model was developed using linear discriminant analysis (LDA) based on stable isotope and multi-element data. The discriminant accuracies of 13 localities across Xinjiang Province was unsatisfactory with values as low as 46.2 % for Changji. Muskmelon from Kashi were misclassified as Aksu (20 %) and Bazhou (16.7 %) respectively. Further LDA modeling was undertaken after the 13 administrative regions were reduced to 6 geographical production regions from Xinjiang. Classification accuracies improved significantly; 100 % for Altay, 91.9 % for Hami and more than 79.1 % for other regions. Therefore, this strategy may be a useful and complementary tool to combat origin mislabeling of Hami PGI muskmelons from other Xinjiang regions, effectively ensuring food integrity and promoting origin certainty of PGI Hami muskmelon from Xinjiang. [ABSTRACT FROM AUTHOR]

Covalent organic polymers (COPs) are a promising class of cross-linked polymeric networks that attracted extensive attention in separation and analysis fields. Exploring facile and convenient strategy to prepare COPs-based mixed-mode stationary phases for high performance liquid chromatography (HPLC) has seriously lagged and has never been reported. Herein, we describe a facile in-situ grow strategy for fabrication of silica@COPs core-shell composites (SiO 2 @TpBD-(OH) 2) as a novel mixed-mode stationary phase for HPLC. Owing to the co-existing of abundant hydroxyl, carbonyl, imine, cyclohexyl groups, and benzene rings in the skeleton of COPs shell, the developed mixed-mode stationary phase exhibits hydrophilic interaction liquid chromatography (HILIC)/reversed-phase liquid chromatography (RPLC)/ion-exchange chromatography (IEX) retention mechanisms. The content of acetonitrile, pH value, and salt concentration in the mobile phase were investigated on SiO 2 @TpBD-(OH) 2 packed column. In comparison to conventional single-mode columns, the SiO 2 @TpBD-(OH) 2 column showed flexible selectivity, enhanced separation performance, and superior resolution for benzene homologues, polycyclic aromatic hydrocarbons, nucleosides and bases, and acidic organic compounds. The column efficiency of p -nitrobenzoic acid was up to 54440 plates per meter. The packed column also possessed outstanding chromatographic repeatability for six nucleosides and bases with the RSDs of 0.07–0.23%, 0.58–1.77%, and 0.31–1.23% for retention time, peak area, and peak height, respectively. Besides, the SiO 2 @TpBD-(OH) 2 column offered baseline separation of multiple organic pollutants in lake water, which verified its great potential in real sample analysis. Overall, the silica@COPs core-shell composites not only provide a new candidate of mixed-mode stationary phases, but also extend the potential application of COPs in separation science. [Display omitted] • A novel silica@COPs core-shell composites were conveniently fabricated. • The COPs-based stationary phase exhibited HILIC/RPLC/IEX mixed-mode retention mechanism. • The developed column possessed excellent separation performance and flexible selectivity for various compounds. • Successful separation of complex sample containing polar, nonpolar, and ionic components was realized on the new column. [ABSTRACT FROM AUTHOR]

Coronavirus disease-2019 (COVID-19) poses a significant threat to the population and urban sustainability worldwide. The surge mitigation is complicated and associates many factors, including the pandemic status, policy, socioeconomics and resident behaviours. Modelling and analytics with spatial-temporal big urban data are required to assist the mitigation of the pandemic. This study proposes a novel perspective to analyse the spatial-temporal potential exposure risk of residents by capturing human behaviours based on spatial-temporal car park availability data. Near real-time data from 1,904 residential car parks in Singapore, a classical megacity, are collected to analyse car mobility and its spatial-temporal heat map. The implementation of the circuit breaker, a COVID-19 measure, in Singapore has reduced the mobility and heat (daily frequency of mobility) significantly at about 30.0%. It contributes to a 44.3%–55.4% reduction in the transportation-related air emissions under two scenarios of travelling distance reductions. Urban sustainability impacts in both environment and economy are discussed. The spatial-temporal potential exposure risk mapping with space-time interactions is further investigated via an extended Bayesian spatial-temporal regression model. The maximal reduction rate of the defined potential exposure risk lowers to 37.6% by comparison with its peak value. The big data analytics of changes in car mobility behaviour and the resultant potential exposure risks can provide insights to assist in (a) designing a flexible circuit breaker exit strategy, (b) precise management via identifying and tracing hotspots on the mobility heat map, and (c) making timely decisions by fitting curves dynamically in different phases of COVID-19 mitigation. The proposed method has the potential to be used by decision-makers worldwide with available data to make flexible regulations and planning. Image 1 • Spatiotemporal potential exposure risks are analysed by capturing human behaviours. • Big urban data in Singapore (SG) are used to demonstrate the method's applicability. • Air emissions were reduced significantly after implementing the circuit breaker in SG. • Precise management via identifying and tracing hotspots on the mobility heat map. • Spatial potential exposure risks can guide a flexible local lockdown exit strategy. [ABSTRACT FROM AUTHOR]

Artificial redox mediators can be employed to improve the electron transfer efficiency during sludge methanogenesis, whereas these artificial redox mediators have possible deficiencies, such as high cost and non-biodegradability. For large-scale commercial applications, more cost-effective and environmentally friendly alternatives should be developed. Herein, the potential of extracellular polymeric substances (EPS) as natural redox mediators to improve methanogenesis was investigated. Compared to the control test without EPS addition, the methane (CH 4) production yield was increased by 83.5 ± 2.4% with an EPS dosage of 0.50 g/L and the lag phase duration was shortened by 45.6 ± 7.0%, along with the enhanced sludge dewaterability. Spectroelectrochemical measurements implied that EPS addition notably changed the intensities of different redox-active groups, which decreased the charge transfer resistance and enhanced the extracellular electron transfer efficiency. These redox-active groups were mainly from the solubilization and hydrolysis of sludge protein due to increased protease activities, thereby leading to a higher acetate concentration during the acidification step. Further investigation showed that EPS addition also improved the activities of both acetotrophic and hydrogenotrophic methanogens, as indicated by a higher abundance of alpha subunit of methyl coenzyme M reductase (mcrA) genes, enhancing CH 4 production. This work provides an innovative strategy for improving sludge anaerobic digestion with efficient additives. • EPS were chosen as natural redox mediators to enhance methanogenesis. • Addition of 0.5 g/L EPS resulted in an 83.5% increase in CH 4 accumulation. • EPS addition altered the species and intensities of redox-active groups. • The solubilization of sludge protein was significantly improved with EPS addition. [ABSTRACT FROM AUTHOR]