Perovskite solar cells (PSCs) have achieved certified power conversion efficiency (PCE) over 25%. Though their high PCE can be achieved by optimizing absorber layer and device interfaces, the ...intrinsic instability of perovskite materials is still a key issue to be resolved. Mixed‐halide perovskites using multiple halogen constituents have been proved to improve robustness; however, the anion at the X site in the ABX3 formula is not limited to halogens. Other negative monovalent ions with similar properties to halogens, such as pseudo‐halogens, have the opportunity to form perovskites with ABX3 stoichiometry. Recently, thiocyanates and formates have been utilized to synthesize stable perovskite materials. This review presents the evolution of pseudo‐halide perovskite solar cells in the past few years. The intrinsic properties, their effects on crystal structure, and bandgap engineering of the pseudo‐halide perovskites are summarized. Various thiocyanate compounds applied in the fabrication of perovskite solar cells are discussed. The fabrication process, film formation mechanism, and crystallinity of pseudo‐halide perovskites are elucidated to understand their effects on the photovoltaic performance and device stability. Other applications of pseudo‐halide perovskites are summarized in the final section. Lastly, this review concludes with suggestions and outlooks for further research directions.
Monovalent pseudo‐halide anions share similar properties to halide anions. This review presents the evolution of pseudo‐halide perovskite solar cells in the past few years. The role of pseudo‐halides and their position and occupation in perovskite crystal, its impact on perovskite film quality, solar cell stability and photovoltaic performance, and pseudo‐halide optoelectronic devices beyond solar cells are compared comprehensively.
Two problems concerning detecting change-points in linear regression models are considered. One involves discontinuous jumps in a regression model and the other involves regression lines connected at ...unknown places. Significant literature has been developed for estimating piecewise regression models because of their broad range of applications. The segmented (SEG) regression method with an R package has been employed by many researchers since it is easy to use, converges fast, and produces sufficient estimates. The SEG method allows for multiple change-points but is restricted to continuous models. Such a restriction really limits the practical applications of SEG when it comes to discontinuous jumps encountered in real change-point problems very often. In this paper, we propose a piecewise regression model, allowing for discontinuous jumps, connected lines, or the occurrences of jumps and connected change-points in a single model. The proposed segmentation approach can derive the estimates of jump points, connected change-points, and regression parameters simultaneously, allowing for multiple change-points. The initializations of the proposed algorithm and the decision on the number of segments are discussed. Experimental results and comparisons demonstrate the effectiveness and superiority of the proposed method. Several real examples from diverse areas illustrate the practicability of the new method.
Change-point (CP) regression models have been widely applied in various fields, where detecting CPs is an important problem. Detecting the location of CPs in regression models could be equivalent to ...partitioning data points into clusters of similar individuals. In the literature, fuzzy clustering has been widely applied in various fields, but it is less used in locating CPs in CP regression models. In this paper, a new method, called fuzzy CP (FCP) algorithm, is proposed to detect the CPs and simultaneously estimate the parameters of regression models. The fuzzy c -partitions concept is first embedded into the CP regression models. Any possible collection of all CPs is considered as a partitioning of data with a fuzzy membership. We then transfer these memberships into the pseudomemberships of data points belonging to each individual cluster, and therefore, we can obtain the estimates for model parameters by the fuzzy c-regressions method. Subsequently, we use the fuzzy c -means clustering to obtain the new iterates of the CP collection memberships by minimizing an objective function concerning the deviations between the predicted response values and data values. We illustrate the new approach with several numerical examples and real datasets. Experimental results actually show that the proposed FCP is an effective and useful CP detection algorithm for CP regression models and can be applied to various fields, such as econometrics, medicine, quality control, and signal processing.
Regression models with change-points have been widely applied in various fields. Most methodologies for change-point regressions assume Gaussian errors. For many real data having longer-than-normal ...tails or atypical observations, the use of normal errors may unduly affect the fit of change-point regression models. This paper proposes two robust algorithms called EMT and FCT for change-point regressions by incorporating the t-distribution with the expectation and maximization algorithm and the fuzzy classification procedure, respectively. For better resistance to high leverage outliers, we introduce a modified version of the proposed method, which fits the t change-point regression model to the data after moderately pruning high leverage points. The selection of the degrees of freedom is discussed. The robustness properties of the proposed methods are also analyzed and validated. Simulation studies show the effectiveness and resistance of the proposed methods against outliers and heavy-tailed distributions. Extensive experiments demonstrate the preference of the t-based approach over normal-based methods for better robustness and computational efficiency. EMT and FCT generally work well, and FCT always performs better for less biased estimates, especially in cases of data contamination. Real examples show the need and the practicability of the proposed method.
This paper presents a robust method for dealing with switching regression problems. Regression models with switch-points are broadly employed in diverse areas. Many traditional methods for switching ...regressions can falter in the presence of outliers or heavy-tailed distributions because of the modeling assumptions of Gaussian errors. The outlier corruption of datasets is often unavoidable. When misapplied, the Gaussian assumption can lead to incorrect inference making. The Laplace distribution is known as a longer-tailed alternative to the normal distributions and connected with the robust least absolute deviation regression criterion. We propose a robust switching regression model of Laplace distributed errors. To advance robustness, we extend the Laplace switching model to a fuzzy class model and create a robust algorithm named FCL through the fuzzy classification maximum likelihood procedure. The robustness properties and the advance of resistance against high-leverage outliers are discussed. Simulations and sensitivity analyses illustrate the effectiveness and superiority of the proposed algorithm. The experimental results indicate that FCL is much more robust than the EM-based algorithm. Furthermore, the Laplace-based algorithm is more time-saving than the t-based procedure. Diverse real-world applications demonstrate the practicality of the proposed approach.
Process data provide important information for monitoring product quality. A common problem of process readings is their deviation from in-control values due to systematic errors or instrument ...biases. Timely detection and modification of instrument fault is crucial for process manipulation. Monitoring changes in process mean and variance simultaneously is important because special causes can evoke changes in both at once. Many existing methods identify either the mean or variance only; some even inaccurately assumes that the in-control parameters are known. We propose a new method called fuzzy maximum likelihood change-point (FMLCP) algorithm that allows detection of the time of shifts in mean and variance simultaneously without knowing the in-control parameters. The fuzzy
c
-partition concept is embedded into change-point formulation to deal with the vagueness of boundaries between adjacent segments. An FMLCP procedure is constructed and suitable for processes following any distribution. The FMLCP algorithm can be applied to both phase I and II processes in quality control without any information of in-control parameters; multiple change points are allowed and the shifts in each individual segment can be estimated simultaneously. Our experimental results demonstrate the preferred utility of FMLCP over traditional statistical maximum likelihood approaches. We used real datasets to demonstrate the effectiveness of FMLCP. Using FMLCP to detect small shifts is particularly beneficial for identifying root causes quickly and correctly in phase II applications where small changes occur more often and the average run length tends to be long.
To investigate whether the O'Leary-Sant Interstitial Cystitis Symptom Index (ICSI) and Interstitial Cystitis Problem Index (ICPI) is efficacy measure tool for interstitial Cystitis (IC) treatment ...with hydrodistention (HD) and bladder training (BT).
From January 2003 to March 2006, 108 consecutive IC patients were treated by HD and BT after HD. This study evaluated the efficacy of treatment with the specific questionnaire for IC, the ICSI and ICPI. Each patient filled out the questionnaire before HD and three months after HD and BT. The efficacy of the treatment was evaluated using the average scores of ICSI and ICPI.
The mean ± margin of error, (95% confidence interval) of total scores of ICSI and ICPI were 13.89 ± 2.95, (13.33–14.45) and 12.51 ± 2.50, (12.04–12.98) before HD, respectively, and were 2.70 ± 1.16, (2.44–2.95) and 1.99 ± 1.27, (1.71–2.26) (all p < 0.005) three months after HD and BT, respectively.
O'Leary-Sant ICSI and ICPI is not only a screening tool for IC but also a useful assessment tool for IC treatment outcomes.
•Robust algorithms are proposed for estimating multiphase regression models.•M-estimation with a resistant criterion helps relieve the effects of outliers.•The proposed algorithms are resistant to ...outliers and heavy-tailed distributions.•A modified procedure present better tolerance to high leverage outliers.•Numerous examples show the effectiveness and usefulness of the proposed method.
This paper proposes a robust procedure for solving multiphase regression problems that is efficient enough to deal with data contaminated by atypical observations due to measurement errors or those drawn from heavy-tailed distributions. Incorporating the expectation and maximization algorithm with the M-estimation technique, we simultaneously derive robust estimates of the change-points and regression parameters, yet as the proposed method is still not resistant to high leverage outliers we further suggest a modified version by first moderately trimming those outliers and then implementing the new procedure for the trimmed data. This study sets up two robust algorithms using the Huber loss function and Tukey's biweight function to respectively replace the least squares criterion in the normality-based expectation and maximization algorithm, illustrating the effectiveness and superiority of the proposed algorithms through extensive simulations and sensitivity analyses. Experimental results show the ability of the proposed method to withstand outliers and heavy-tailed distributions. Moreover, as resistance to high leverage outliers is particularly important due to their devastating effect on fitting a regression model to data, various real-world applications show the practicability of this approach.
PDF
