To survey the extent of complementary and alternative medicine (CAM) use among women for the alleviation of menopausal symptoms. A total of 1,296 eligible women aged 45 to 65 years were recruited from three Sydney menopause clinics, general practice clinics, and government agencies between July 2003 and July 2004. Volunteers were invited to complete a 19-item questionnaire covering basic demographics, general health status, use of CAM therapies and products, use of pharmaceuticals, and sources of CAM advice. Of respondents, 53.8% had visited a CAM practitioner and/or used a CAM product during the past year, with 34% using a product only and 5% consulting a practitioner only. The most commonly visited practitioners were naturopaths (7.2%) and acupuncturists (4.8%), whereas the most popular products were soy (25.4%) and evening primrose oil (18.4%). Massage, chiropractic, and nutrition were rated the most effective therapies, and phytoestrogen tablets, evening primrose oil, and black cohosh were deemed the most effective products. Of the 59.9% of respondents currently using prescription or over-the-counter pharmaceuticals, 62.5% reported using CAM products during the past 12 months. Of CAM users 71% had informed their doctor about CAM use, whereas 26.4% of respondents reported their doctor had inquired about CAM use. CAM use by women to alleviate menopausal symptoms is common, with several therapies perceived to be effective. Although a significant proportion of women may use CAM in conjunction with pharmaceuticals, relevant communication between medical practitioners and patients remains inadequate and may expose the patient to potential drug-herb interactions.

Air quality data sets are widely used in numerous analyses. Missing values are ubiquitous in air quality data sets as the data are collected through sensors. Recovery of missing data is a challenging task in the data preprocessing stage. This task becomes more challenging in time series data as time is an implicit variable that cannot be ignored. Even though existing methods to deal with missing data in time series perform well in situations where the percentage of missing values is relatively low and the gap size is small, their performances are reasonably lower when it comes to large gaps. This paper presents a novel algorithm based on seasonal decomposition and elastic net regression to impute large gaps of time series data when there exist correlated variables. This method outperforms several other existing univariate approaches namely Kalman smoothing on ARIMA models, Kalman smoothing on structural time series models, linear interpolation, and mean imputation in imputing large gaps. However, this is applicable only when there exists one or more correlated variables with the time series with large gaps.

It is a well-known fact that some criminals follow perpetual methods of operations known as modi operandi. Modus operandi is a commonly used term to describe the habits in committing crimes. These modi operandi are used in relating criminals to crimes for which the suspects have not yet been recognized. This paper presents the design, implementation and evaluation of a new method to find connections between crimes and criminals using modi operandi. The method involves generating a feature matrix for a particular criminal based on the flow of events of his/her previous convictions. Then, based on the feature matrix, two representative modi operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modi operandi are compared with the flow of events of the crime at hand, in order to generate two other outputs: completeness probability (CP) and deviation probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a score which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and ten other open data sets. In addition, comparison with nine other classification algorithms showed that the proposed method performs competitively with other related methods proving that the performance of the new method is at an acceptable level.