Photogenerated carrier dynamics of Mn2+ doped CsPbBr3 assembled with TiO2 systems: Effect of Mn doping content.

In recent years, all-inorganic perovskite materials have become an ideal choice for new thin film solar cells due to their excellent photophysical properties and have become a research hotspot. Studying the ultrafast dynamics of photo-generated carriers is of great significance for further improving the performance of such devices. In this work, we focus on the transient dynamic process of CsPbBr3/TiO2 composite systems with different Mn2+ doping contents using femtosecond transient absorption spectroscopy technology. We used singular value decomposition and global fitting to analyze the transient absorption spectra and obtained three components, which are classified as hot carrier cooling, charge transfer, and charge recombination processes, respectively. We found that the doping concentration of Mn2+ has an impact on all three processes. We think that the following two factors are responsible: one is the density of defect states and the other is the bandgap width of perovskite. As the concentration of doped Mn2+ increases, the charge transfer time constant shows a trend of initially increasing, followed by a subsequent decrease, reaching a turning point. This indicates that an appropriate amount of Mn2+ doping can effectively improve the photoelectric performance of solar cell systems. We proposed a possible charge transfer mechanism model and further elucidated the microscopic mechanism of the effect of Mn2+ doping on the interface charge transfer process of the CsPbBr3/TiO2 solar cell system.

Recent Advances in Statistical Theory and Applications.

Complex data pose unique challenges for data processing [...].

Two-Sample Tests Based on Data Depth.

In this paper, we focus on the homogeneity test that evaluates whether two multivariate samples come from the same distribution. This problem arises naturally in various applications, and there are many methods available in the literature. Based on data depth, several tests have been proposed for this problem but they may not be very powerful. In light of the recent development of data depth as an important measure in quality assurance, we propose two new test statistics for the multivariate two-sample homogeneity test. The proposed test statistics have the same χ2(1) asymptotic null distribution. The generalization of the proposed tests into the multivariate multisample situation is discussed as well. Simulations studies demonstrate the superior performance of the proposed tests. The test procedure is illustrated through two real data examples.

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection.

This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

A case report of drug-induced liver injury after tigecycline administration: histopathological evidence and a probable causality grading as assessed by the updated RUCAM diagnostic scale.

BACKGROUND: There have been no reports of tigecycline-associated drug-related liver injury (DILI) identified by histopathological assistance and causal assessment method. We reported the histopathological manifestations for the first time and described tigecycline-associated liver injury's pattern, severity, duration, and outcome. CASE PRESENTATION: A 68-year-old male with post-liver transplantation was given high-dose tigecycline intravenously (loading dose 200 mg, followed by 100 mg every 12 h) combined with polymyxin B (50,000 units by aerosol inhalation every 12 h) for hospital-acquired pneumonia caused by carbapenem-resistant Klebsiella pneumoniae. At the same time, tacrolimus was discontinued. Liver function was initially normal but started to decline on day 4 of tigecycline. Reducing the dose of tigecycline and resuming tacrolimus could not reverse the deterioration. Therefore, a liver puncture biopsy was performed for further diagnosis, with histopathological findings being cytotoxic injury. The updated RUCAM scale was used to evaluate the causal relationship between tigecycline and liver injury, with the result of 7 points indicating a "probable" causality grading. Methylprednisolone was initiated to treat DILI that was determined to be Grade 1 cholestatic injury. Total bilirubin and transaminase levels returned to normal on day 4 and 11 after tigecycline withdrawal, respectively. Monthly outpatient follow-up showed that the patient's liver function stayed normal. CONCLUSIONS: This case possessed a significant reference value for differential diagnosis and treatment prognosis of tigecycline-associated DILI. With early diagnosis and timely management, the tigecycline-associated DILI of this patient was successfully reversed.

Cognitive processing features of elementary school children with mathematical anxiety: Attentional control theory-based explanation.

A growing body of research suggests that mathematical anxiety (MA) seriously affects an individual's math achievement. However, few studies have focused on the cognitive mechanisms of MA in elementary school children. Based on attention control theory (ACT), this research aimed to explore the cognitive mechanism of MA in elementary school children using two studies. In Study 1, the dual-task paradigm of number memory and computation span was used to investigate the difference in processing efficiency between the high-mathematical anxiety (HMA) group and the low-mathematical anxiety (LMA) group. In total, 59 students with HMA and 54 students with LMA participated in Study 1. The results showed that students with HMA had lower processing efficiency in dealing with high-load math tasks. To further investigate the underlying mechanism of low processing efficiency for students with HMA, Study 2 explored the attention bias toward math-related stimuli of students with HMA using the Posner paradigm. In total, 48 students with HMA and 49 students with LMA participated in Study 2. The results showed that math trials put children with HMA in a state of heightened vigilance in general, which might be related to the low processing efficiency in dealing with high-load math tasks. These findings support the ACT and further reveal the mechanism of MA in elementary school children from a cognitive perspective.

A retrospective observational study of the treatment with polymyxin B for liver transplantation recipients infected by multidrug-resistant gram-negative bacteria.

WHAT IS KNOWN AND OBJECTIVE: Only a few studies about polymyxin B (PMB) against multidrug-resistant gram-negative bacteria (MDR GNB) infection were conducted in liver transplantation recipients (LTRs). The purpose of this study was to investigate the efficacy and safety of PMB in the treatment of MDR-GNB in liver transplant recipients and to determine the risk factors affecting clinical cure and 30-day all-cause mortality. METHODS: Data of LTRs receiving PMB from January 2016 to February 2020 were collected. Clinical cure and 30-day all-cause mortality were the main efficacy outcomes, while the incidence of nephrotoxicity, neurotoxicity, and hyperpigmentation of PMB was the main safety outcome. RESULTS AND DISCUSSION: Data of 42 LTRs were included. Clinical cure with PMB was observed in 27 recipients (64.3%), and the 30-day all-cause mortality rate was 31.0% (13/42). The incidence of acute kidney injury (AKI), neurotoxicity, and hyperpigmentation was 57.1% (16/28), 4.8% (2/42), and 16.7% (7/42), respectively. Logistic regression analysis showed that Acute Physiology and Chronic Health Evaluation (APACHE) II score (OR, 1.203; 95% CI, 1.016-1.423, p = 0.032) was an independent risk factor for 30-day all-cause mortality, whereas renal replacement therapy (OR, 0.128; 95% CI, 0.019-0.860, p = 0.034) was an independent risk factor for clinical cure with PMB. WHAT IS NEW AND CONCLUSIONS: This is the first study to evaluate the application of PMB in LTRs. If there were no better therapeutic options left for LTRs other than PMB, it can be used against MDR GNB infection in LTRs. We should closely observe adverse events or reactions, and adjust the dose based on the balance of efficacy and safety.

Alpha/Theta Ratio Neurofeedback Training for Attention Enhancement in Normal Developing Children: A Brief Report.

Attention plays an important role in children's development and learning, and neurofeedback training (NFT) has been proposed as a promising method to improve attention, mainly in population with attention problems such as attention deficit hyperactivity disorder. However, whether this approach has a positive effect on attention in normal developing children has been rarely investigated. This pilot study conducted ten sessions of alpha/theta ratio (ATR) NFT on eight primary students in school environment, with two to three sessions per week. The results showed inter-individual difference in NFT learning efficacy that was assessed by the slope of ATR over training sessions. In addition, the attention performance was significantly improved after NFT. Importantly, the improvement of attention performance was positively correlated with the NFT learning efficacy. It thus highlighted the need for optimizing ATR NFT protocol for the benefits on attention at the individual level. Future work can employ a double-blind placebo-controlled design with larger sample size to validate the benefits of ATR NFT for attention in normal developing children.

A New Class of Weighted CUSUM Statistics.

A change point is a location or time at which observations or data obey two different models: before and after. In real problems, we may know some prior information about the location of the change point, say at the right or left tail of the sequence. How does one incorporate the prior information into the current cumulative sum (CUSUM) statistics? We propose a new class of weighted CUSUM statistics with three different types of quadratic weights accounting for different prior positions of the change points. One interpretation of the weights is the mean duration in a random walk. Under the normal model with known variance, the exact distributions of these statistics are explicitly expressed in terms of eigenvalues. Theoretical results about the explicit difference of the distributions are valuable. The expansions of asymptotic distributions are compared with the expansion of the limit distributions of the Cramér-von Mises statistic and the Anderson and Darling statistic. We provide some extensions from independent normal responses to more interesting models, such as graphical models, the mixture of normals, Poisson, and weakly dependent models. Simulations suggest that the proposed test statistics have better power than the graph-based statistics. We illustrate their application to a detection problem with video data.

MCMCINLA Estimation of Missing Data and Its Application to Public Health Development in China in the Post-Epidemic Era.

Medical data are often missing during epidemiological surveys and clinical trials. In this paper, we propose the MCMCINLA estimation method to account for missing data. We introduce a new latent class into the spatial lag model (SLM) and use a conditional autoregressive specification (CAR) spatial model-based approach to impute missing values, making the model fit into the integrated nested Laplace approximation (INLA) framework. Combining the advantages of both the Markov chain Monte Carlo (MCMC) and INLA frameworks, the MCMCINLA algorithm is used to implement imputation of the missing data and fit the model to derive estimates of the parameters from the posterior margins. Finally, the economic data and the hemorrhagic fever with renal syndrome (HFRS) disease data of mainland China from 2016-2018 are used as examples to explore the development of public health in China in the post-epidemic era. The results show that compared with expectation maximization (EM) and full information maximum likelihood estimation (FIML), the predicted values of the missing data obtained using our method are closer to the true values, and the spatial distribution of HFRS in China can be inferred from the imputation results with a southern-heavy and northern-light distribution. It can provide some references for the development of public health in China in the post-epidemic era.

Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data.

The change-point detection has been carried out in terms of the Euclidean minimum spanning tree (MST) and shortest Hamiltonian path (SHP), with successful applications in the determination of authorship of a classic novel, the detection of change in a network over time, the detection of cell divisions, etc. However, these Euclidean graph-based tests may fail if a dataset contains random interferences. To solve this problem, we present a powerful non-Euclidean SHP-based test, which is consistent and distribution-free. The simulation shows that the test is more powerful than both Euclidean MST- and SHP-based tests and the non-Euclidean MST-based test. Its applicability in detecting both landing and departure times in video data of bees' flower visits is illustrated.

Consistent and powerful graph-based change-point test for high-dimensional data.

A change-point detection is proposed by using a Bayesian-type statistic based on the shortest Hamiltonian path, and the change-point is estimated by using ratio cut. A permutation procedure is applied to approximate the significance of Bayesian-type statistics. The change-point test is proven to be consistent, and an error probability in change-point estimation is provided. The test is very powerful against alternatives with a shift in variance and is accurate in change-point estimation, as shown in simulation studies. Its applicability in tracking cell division is illustrated.

3,5-Dimethylorsellinic Acid Derived Meroterpenoids from Penicillium chrysogenum MT-12, an Endophytic Fungus Isolated from Huperzia serrata.

Eight new chrysogenolides (A-H (1-8)) and seven known (9-15) 3,5-dimethylorsellinic acid derived meroterpenoids were isolated from the solid substrate fermentation cultures of a Huperzia serrata endophytic fungus, Penicillium chrysogenum MT-12. The structures of the new compounds were elucidated by interpretation of spectroscopic and spectrometric data (1D and 2D NMR, IR, and HRESIMS). The absolute configurations of 1-4 were determined by single-crystal X-ray crystallographic analysis, and those of 5-8 were assigned on the basis of experimental and calculated electronic circular dichroism spectra. Compounds 3, 4, 6, 11, and 12 showed inhibition of nitric oxide production in lipopolysaccharide-activated RAW 264.7 macrophage cells with IC50 values in the range of 4.3-78.2 µM (positive control, indomethacin, IC50 = 33.6 ± 1.4 µM).

Approximation of the expected value of the harmonic mean and some applications.

Although the harmonic mean (HM) is mentioned in textbooks along with the arithmetic mean (AM) and the geometric mean (GM) as three possible ways of summarizing the information in a set of observations, its appropriateness in some statistical applications is not mentioned in textbooks. During the last 10 y a number of papers were published giving some statistical applications where HM is appropriate and provides a better performance than AM. In the present paper some additional applications of HM are considered. The key result is to find a good approximation to [Formula: see text], the expectation of the harmonic mean of n observations from a probability distribution. In this paper a second-order approximation to [Formula: see text] is derived and applied to a number of problems.

A multi-objective optimization framework with rule-based initialization for multi-stage missile target allocation.

This paper investigates a novel multi-objective optimization framework for the multi-stage missile target allocation (M-MTA) problem, which also widely exists in other real-world complex systems. Specifically, a constrained model of M-MTA is built with the trade-off between minimizing the survivability of targets and minimizing the cost consumption of missiles. Moreover, a multi-objective optimization algorithm (NSGA-MTA) is proposed for M-MTA, where the hybrid encoding mechanism establishes the expression of the model and algorithm. Furthermore, rule-based initialization is developed to enhance the quality and searchability of feasible solutions. An efficient non-dominated sorting method is introduced into the framework as an effective search strategy. Besides, the genetic operators with the greedy mechanism and random repair strategy are involved in handling the constraints with maintaining diversity. The results of numerical experiments demonstrate that NSGA-MTA performs better in diversity and convergence than the excellent current algorithms in metrics and Pareto front obtained in 15 scenarios. Taguchi method is also adopted to verify the contribution of proposed strategies, and the results show that these strategies are practical and promotive to performance improvement.

Analysis of air pollution in Fenwei Plain in China based on functional spatial autoregressive combined model.

The Fenwei Plain is listed as one of the most serious air pollution regions in China, along with Beijing-Tianjin-Hebei and Yangtze River Delta regions. This paper proposed a functional data analysis method to study the environmental pollution problem in the Fenwei Plain of China. Functional spatial autoregressive combined (FSAC) model with spatial autocorrelation of both the response variable and error term is developed. The model takes the SO2 concentration of Fenwei Plain as the dependent variable and the dew point temperature as the independent variable and realizes the maximum likelihood estimation using functional principal component analysis to obtain the asymptotic properties of parameter estimation and the confidence interval of the slope function. According to the findings of the empirical analysis of the Fenwei Plain, the SO2 concentration has significant seasonal characteristics and has decreased year over year for three years in a row. Winter is the season with the highest concentration on the Fenwei Plain, followed by spring and autumn, while summer is the season with the lowest concentration. Winter also has a high spatial autocorrelation. The FSAC model is more effective at fitting the concentration and dew point temperature of the Fenwei Plain in China because its mean square error (MSE) is significantly lower than that of the other models. As a result, this paper can more thoroughly study the pollution problem on the Fenwei Plain and offer guidance for prevention and control.

Global analysis of the COVID-19 policy activity levels and evolution patterns: A cross-sectional study.

Background and Aims: Since the beginning of the coronavirus disease 2019 (COVID-19), a large number of government policies have been implemented worldwide in response to the global spread of COVID-19. This paper aims at developing a data-driven analysis to answer the three research questions: (a) Compared to the pandemic development, are the global government COVID-19 policies sufficiently active? (b) What are the differences and characteristics in the policy activity levels at the country level? (c) What types of COVID-19 policy patterns are forming? Methods: Using the Oxford COVID-19 Government Response Tracker data set, we present a global analysis of the COVID-19 policy activity levels and evolution patterns from January 1, 2020 to June 30, 2022, based on the differential expression-sliding window analysis (DE-SWAN) algorithm and the clustering ensemble algorithm. Results: Within the period under study, the results indicate that (a) the global government policy responses to COVID-19 are very active, and the policy activity levels are significantly higher than those of global pandemic developments; (b) a high activity of policy is positively correlated to pandemic prevention at the country level; and (c) a high human development index (HDI) score is negatively correlated to the country policy activity level. Furthermore, we propose to categorize the global policy evolution patterns into three categories: (i) Mainstream (152 countries); (ii) China; and (iii) Others (34 countries). Conclusion: This work is one of the few studies that quantitatively explores the evolutionary characteristics of global government policies on COVID-19, and our results provide some new perspectives on global policy activity levels and evolution patterns.

Degradation of Methyl Orange by ozone microbubble process with packing in the bubble column reactor.

The performance of the ozone microbubble(MB) process for the degradation of Methyl Orange (MO) in a bubble column reactor with added packing was investigated. The highest decolorization efficiency of 96.04% was achieved by the ozone MB process with packing, which was 10.17% and 62.02% higher than that of the ozone MB process without packing and the ozone millimeter bubble(MLB) process, respectively while keeping other operating parameters the same. In addition, the saturation gas holdup, ozone mass transfer coefficient, and decolorization rate constant of the ozone MB process with packing were 15.32%, 0.260 min-1, and 0.027 min-1, respectively, which were much better than those of the ozone MB process without packing and the ozone MLB process. The study also suggested that within a certain porosity range, the types of packings did not affect the performance of the ozone MB process in the degradation of MO. Moreover, the optimum operating conditions were initial concentration of MO of 30 mg/L, initial pH of 3, circulating liquid flow of 75 L/h, and ozone dosage of 0.56 mg/L. The decolorization efficiency was 99.28% within 120 min.

Effect of Fiber Fraction on Ballistic Impact Behavior of 3D Woven Composites.

This paper studies the ballistic impact performance of 3D woven composites (3DWCs) with hexagonal binding patterns. Para-aramid/polyurethane (PU) 3DWCs with three kinds of fiber volume fraction (Vf) were prepared by compression resin transfer molding (CRTM). The effect of Vf on the ballistic impact behavior of the 3DWCs was analyzed by characterizing the ballistic limit velocity (V50), the specific energy absorption (SEA), the energy absorption per thickness (Eh), the damage morphology and the damage area. 1.1 g fragment-simulating projectiles (FSPs) were used in the V50 tests. Based on the results, when the Vf increases from 63.4% to 76.2%, the V50, the SEA and the Eh increase by 3.5%, 18.5% and 28.8%, respectively. There are significant differences in damage morphology and damage area between partial penetration (PP) cases and complete penetration (CP) cases. In the PP cases, the back-face resin damage areas of the sample III composites were significantly increased to 213.4% of the sample I counterparts. The findings provide valuable information for the design of ballistic protection 3DWCs.