Photoinduced FeCl3-Catalyzed Chlorination of Aromatic Sulfonyl Chloride via Extrusion of SO2 at Room Temperature.

We report, herein, a photoinduced iron-catalyzed direct chlorination of aromatic sulfonyl chloride at room temperature. In this protocol, FeCl3-catalyzed direct chlorination has been realized at room temperature under the irradiation of light (400-410 nm). During the process, many commercially or readily available substituted aromatic sulfonyl chlorides could produce the corresponding aromatic chlorides in moderate to good yields.

Data-driven Q-matrix learning based on Boolean matrix factorization in cognitive diagnostic assessment.

Attributes and the Q-matrix are the central components for cognitive diagnostic assessment, and are usually defined by domain experts. However, it is challenging and time consuming for experts to specify the attributes and Q-matrix manually. Thus, there is an urgent need for an automatic and intelligent means to address this concern. This paper presents a new data-driven approach for learning the Q-matrix from response data. By constructing a statistical index and a heuristic algorithm based on Boolean matrix factorization, the response matrix is decomposed into the Boolean product of the Q-matrix and the attribute mastery patterns. The feasibility of the proposed approach is evaluated using simulated data generated under various conditions. A real data example is also presented to demonstrate the usefulness of the proposed approach.

A Law of Comparative Preference: Distinctions Between Models of Personal Preference and Impersonal Judgment in Pair Comparison Designs.

The pair comparison design for distinguishing between stimuli located on the same natural or hypothesized linear continuum is used both when the response is a personal preference and when it is an impersonal judgment. Appropriate models which complement the different responses have been proposed. However, the models most appropriate for impersonal judgments have also been described as modeling choice, which may imply personal preference. This leads to potential confusion in interpretation of scale estimates of the stimuli, in particular whether they reflect a substantive order on the variable or reflect a characteristic of the sample which is different from the substantive order on the variable. Using Thurstone's concept of a discriminal response when a person engages with each stimulus, this article explains the overlapping and distinctive relationships between models for pair comparison designs when used for preference and judgment. In doing so, it exploits the properties of the relatively new hyperbolic cosine model which is not only appropriate for modeling personal preferences but has an explicit mathematical relationship with models for impersonal judgments. The hyperbolic cosine model is shown to be a special case of a more general form, referred to in parallel with Thurstone's Law of Comparative Judgment, as a specific law of comparative preference. Analyses of two real data sets illustrate the differences between the models most appropriate for personal preferences and impersonal judgments in a pair comparison design.

Development and application of a high-throughput sample cleanup process based on 96-well plate for simultaneous determination of 16 steroids in biological matrices using liquid chromatography-triple quadrupole mass spectrometry.

A novel high-throughput sample pretreatment system was developed by the integration of protein precipitation (PP), phospholipid removal (PPR), and hollow fiber liquid-phase microextraction (HF-LPME) into two simple 96-well plates and a matching 96-grid lid. With this system, 16 steroids were separated from biological matrices of plasma, milk, and urine and analyzed by liquid chromatography-triple quadrupole mass spectrometry. In the tandem sample cleanup process, the prepositive PP and PPR step preliminarily removed some of the interferences from the biological matrices. The following HF-LPME step kept the residual interference out of the hollow fiber and enriched the steroids in the hollow fiber to achieve high sensitivity. By a series of method optimizations, acetonitrile was chosen as the crash solvent for PP and PPR. A mixture of octanol and toluene (1:1 v/v) was used as the acceptor phase for HF-LPME. The extraction was conducted at 80 rpm for 50 min in a donor phase containing 1 mL 20% sodium chloride at 25 °C. Under these conditions, the limits of detection for the 16 steroids were 3.6-300.0 pg(.)mL(-1) in plasma, 3.0-270.0 pg·mL(-1) in milk, and 2.2-210.0 pg(.)mL(-1) in urine. The recoveries of the 16 steroids were 81.9-97.9% in plasma (relative standard deviation 1.0-8.0%), 80.6-97.7% in milk (relative standard deviation 0.8-5.4%), and 87.3-98.7% in urine (relative standard deviation 1.0-4.9%). Further, the integrated 96-well platform of PP, PPR, and HF-LPME enabled us to run this assay in an automatic and high-throughput fashion. The reliability of the method was further corroborated by evaluation of its applicability in plasma and urine samples from volunteers and fresh bovine milk from local dairy enterprises.

The relationship between the Rating Scale and Partial Credit Models and the implication of disordered thresholds of the Rasch models for polytomous responses.

There is a perception in the literature that the Rating Scale Model (RSM) and Partial Credit Model (PCM) are two different types of Rasch models. This paper clarifies the relationship between the RSM and PCM from the perspectives of literature history and mathematical logic. It is shown that not only are the RSM and the PCM identical, but the two approaches used to introduce them are statistically equivalent. Then the implication of disordered thresholds is discussed. In addition, the difference between the structural thresholds and the Thurstone thresholds are clarified.

Estimating parameters in the Rasch model in the presence of null categories.

A category with a frequency of zero is called a null category. When null categories are present in polytomous responses, then in the Rasch model for such responses, the thresholds that define the categories are inestimable with the commonly used joint maximum likelihood, marginal maximum likelihood, or standard conditional maximum likelihood estimation algorithms. The reason for this situation is that in principle, these estimation algorithms involve frequencies of each category. Andrich and Luo (2003) describe an algorithm in which the thresholds are reparameterized into their principal components and in which the estimate of any threshold is based on a function of the frequencies of all categories of the item rather than the frequency of a particular category. This algorithm works in the presence of null categories. However, in situations where the null categories are at the extremes of a set of categories, the estimates themselves can become too extreme. This paper describes a procedure in which the solution algorithm described by Andrich and Luo is further adapted in the presence of null categories by using their expected frequencies. The procedure is demonstrated with simulated and real data.

Conditional pairwise estimation in the Rasch model for ordered response categories using principal components.

In the Rasch model for items with more than two ordered response categories, the thresholds that define the successive categories are an integral part of the structure of each item in that the probability of the response in any category is a function of all thresholds, not just the thresholds between any two categories. This paper describes a method of estimation for the Rasch model that takes advantage of this structure. In particular, instead of estimating the thresholds directly, it estimates the principal components of the thresholds, from which threshold estimates are then recovered. The principal components are estimated using a pairwise maximum likelihood algorithm which specialises to the well known algorithm for dichotomous items. The method of estimation has three advantageous properties. First, by considering items in all possible pairs, sufficiency in the Rasch model is exploited with the person parameter conditioned out in estimating the item parameters, and by analogy to the pairwise algorithm for dichotomous items, the estimates appear to be consistent, though unlike for the dichotomous case, no formal proof has yet been provided. Second, the estimates of each item parameter is a function of frequencies in all categories of the item rather than just a function of frequencies of two adjacent categories. This stabilizes estimates in the presence of low frequency data. Third, the procedure accounts readily for missing data. All of these properties are important when the model is used for constructing variables from large scale data sets which must account for structurally missing data. A simulation study shows that the quality of the estimates is excellent.

A Class of Probabilistic Unfolding Models for Polytomous Responses.

By revisiting the approaches used to present the Rasch model for polytomous response, this paper uses the principle of the rating formulation (Andrich, 1978) to construct a class of unfolding models for polytomous responses in terms of a set of latent dichotomous unfolding variables. By anchoring the dichotomous unfolding variables involved at the same location, this paper presents a formulation of a very general class of unfolding models for ordered polytomous responses, of which the unfolding models for ordered polytomous responses proposed hitherto are special cases. Within this class, the analytic and measurement properties of the probabilistic functions are well interpreted in terms of the latitudes of acceptance parameters of the dichotomous unfolding models. Based on the general form of this class of unfolding models, some new models are readily specified. Copyright 2001 Academic Press.