Insecticidal and repellant activities of Southeast Asia plants towards insect pests: a review

Crops are being damaged by several plant pests. Several strategies have been developed to restrict the damage of cultivated plants by using synthetic pesticides and repellants. However, the use to control these insects is highly discouraged because of their risks on humans. Therefore, several alternatives have been developed from plant extracts to protect crops from plant pests. Accordingly, this review focuses on outlining the insecticidal and repellant activities of Southeast Asia plants towards insect pests. Several extracts of plants from Southeast Asia were investigated to explore their insecticidal and repellant activities. Azadiracha indica (neem) and Piper species were highly considered for their insecticidal and repellant activities compared to other plants. This review also addressed the investigation on extracts of other plant species that were reported to exert insecticidal and repellant activities. Most of the conducted studies have been still in the primarily stage of investigation, lacking a focus on the insecticidal and repellant spectrum and the identification of the active constituents which are responsible for the insecticidal and repellant activity

Regresyon kontrol kartı ve bir çalışma

İstatistiksel kalite kontrolünde, ölçülebilen ve ölçülemeyen özelliklere ait kalite kontrol diyagramları geniş ölçüde kullanılmaktadır. Birçok üretim sürecinde uygulanan işlemler nedeniyle bir süre sonra süreçten elde edilen ürünün ölçülebilen veya ölçülemeyen özeliklerinin değerlerinde değişmelerin meydana geldiği görülür. Bu karakteristikler zamana bağlı olarak artabilir veya azalabilir. Bu gibi durumlarda geleneksel kontrol diyagramlarını kullanmak mümkün olmayabilir. Bu sebepten dolayı ölçülen özelliğin değişimine uygun kontrol grafiği kullanmak gerekir. Bu çalışmada amaca uygun olarak Isparta’da bir yonga levha sektöründe, regresyon kalite kontrol kartı kullanılarak bir uygulama yapılmış ve bağımlı değişken sürekli izlenerek performansı iyileştirmeye çalışılmıştır. Anahtar kelimeler: Regresyon, Regresyon kontrol kartı, İstatistiksel kalite kontrol, Yonga levha endüstris

A Theoretical Development of Distance Measure for Intuitionistic Fuzzy Numbers

The objective of this paper is to introduce a distance measure for intuitionistic fuzzy numbers. Firstly the existing distance measures for intuitionistic fuzzy sets are analyzed and compared with the help of some examples. Then the new distance measure for intuitionistic fuzzy numbers is proposed based on interval difference. Also in particular the type of distance measure for triangle intuitionistic fuzzy numbers is described. The metric properties of the proposed measure are also studied. Some numerical examples are considered for applying the proposed measure and finally the result is compared with the existing ones

Outlier detection algorithms over fuzzy data with weighted least squares

In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an observation and then construct a model on the remaining data. If the difference between predicted and observed value is high we declare this value an outlier. As a rule, those procedures utilize single comparison testing. The problem becomes much harder when the observations can be associated with a given degree of membership to an underlying population, and the outlier detection should be generalized to operate over fuzzy data. We present a new approach for outlier detection that operates over fuzzy data using two inter-related algorithms. Due to the way outliers enter the observation sample, they may be of various order of magnitude. To account for this, we divided the outlier detection procedure into cycles. Furthermore, each cycle consists of two phases. In Phase 1, we apply a leave-one-out procedure for each non-outlier in the dataset. In Phase 2, all previously declared outliers are subjected to Benjamini–Hochberg step-up multiple testing procedure controlling the false-discovery rate, and the non-confirmed outliers can return to the dataset. Finally, we construct a regression model over the resulting set of non-outliers. In that way, we ensure that a reliable and high-quality regression model is obtained in Phase 1 because the leave-one-out procedure comparatively easily purges the dubious observations due to the single comparison testing. In the same time, the confirmation of the outlier status in relation to the newly obtained high-quality regression model is much harder due to the multiple testing procedure applied hence only the true outliers remain outside the data sample. The two phases in each cycle are a good trade-off between the desire to construct a high-quality model (i.e., over informative data points) and the desire to use as much data points as possible (thus leaving as much observations as possible in the data sample). The number of cycles is user defined, but the procedures can finalize the analysis in case a cycle with no new outliers is detected. We offer one illustrative example and two other practical case studies (from real-life thrombosis studies) that demonstrate the application and strengths of our algorithms. In the concluding section, we discuss several limitations of our approach and also offer directions for future research

Outlier detection algorithms over fuzzy data with weighted least squares

In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an observation and then construct a model on the remaining data. If the difference between predicted and observed value is high we declare this value an outlier. As a rule, those procedures utilize single comparison testing. The problem becomes much harder when the observations can be associated with a given degree of membership to an underlying population, and the outlier detection should be generalized to operate over fuzzy data. We present a new approach for outlier detection that operates over fuzzy data using two inter-related algorithms. Due to the way outliers enter the observation sample, they may be of various order of magnitude. To account for this, we divided the outlier detection procedure into cycles. Furthermore, each cycle consists of two phases. In Phase 1, we apply a leave-one-out procedure for each non-outlier in the dataset. In Phase 2, all previously declared outliers are subjected to Benjamini–Hochberg step-up multiple testing procedure controlling the false-discovery rate, and the non-confirmed outliers can return to the dataset. Finally, we construct a regression model over the resulting set of non-outliers. In that way, we ensure that a reliable and high-quality regression model is obtained in Phase 1 because the leave-one-out procedure comparatively easily purges the dubious observations due to the single comparison testing. In the same time, the confirmation of the outlier status in relation to the newly obtained high-quality regression model is much harder due to the multiple testing procedure applied hence only the true outliers remain outside the data sample. The two phases in each cycle are a good trade-off between the desire to construct a high-quality model (i.e., over informative data points) and the desire to use as much data points as possible (thus leaving as much observations as possible in the data sample). The number of cycles is user defined, but the procedures can finalize the analysis in case a cycle with no new outliers is detected. We offer one illustrative example and two other practical case studies (from real-life thrombosis studies) that demonstrate the application and strengths of our algorithms. In the concluding section, we discuss several limitations of our approach and also offer directions for future research

Математичні методи і моделі в управлінні економічними процесами

Викладено теоретичні та практичні підходи до розроблення та обчислення економіко-математичних моделей і вдосконалення математичних методів в управлінні економічними процесами, а саме нечітких моделей конкурентоспроможності банків, статистичних інструментів моніторингу фінансової діяльності підприємства, інструментів динамічного програмування до задачі оптимізації фінансових потоків підприємства, стійкості в моделях інвестиційних стратегійИзложены теоретические и практические подходы к разработке и вычислениям экономико-математических моделей, использование усовершенствованных математических методов в управлении экономическими процессами, а именно нечетких моделей конкурентоспособности банков, статистических инструментов мониторинга финансовой деятельности предприятия, инструментов динамического программирования к задаче оптимизации финансовых потоков предприятия, устойчивости в моделях инвестиционных стратегийTheoretical and practical approaches to development and calculation are stated economic and mathematical models and improvement of mathematical methods in the management of economic processes, namely fuzzy models of competitiveness banks, statistical tools for monitoring the financial performance of the enterprise, dynamic programming tools for the task of optimizing financial flows enterprises, stability in models of investment strategie