Most microcapsule preparation methods produce a population of microcapsules in a bulk solution. To control the microcapsule preparation or obtain an optimal preparation condition, the mechanism of the microcapsule preparation should be investigated. The mechanism is estimated via structure reformation during the preparation process because diameter and wall thickness are drastically altered in the solution. Considering microcapsule applications, some important properties, such as the mechanical properties of microcapsules and release rate of the encapsulated product, depend on the microcapsule structure. In this study, polystyrene microcapsules containing saline water droplets were prepared via the solvent evaporation method from a solid-in-oil-in-water (S/O/W) emulsion system. The microcapsules exhibited a speci c structural distribution, which comprised monocore, multicore, and solidcore structures. The structural distribution was altered by the preparation condition. The monocore structure was absolutely dominant owing to the increase in the amount of calcium chloride added in the organic phase. The salt concentration is not the sole controlling factor of the microcapsule structure, as the surfactant and dispersion exerted a signi cant impact on the microcapsule structure. The structural distribution was automatically analyzed by a machine learning algorithm (MLA). The decision-making time for the microcapsules preparation was shortened by the accelerated structure determination, and the accuracy was improved by increasing the number of counting particles.

For the microcapsules preparation process, the mechanism is estimated by structure reformation during the preparation process since diameter and wall thickness drastically changed. Microstructures are recently studied by machine learning techniques. The Hough transformation algorithm is used by other researchers for the preparation of the microcapsules but it is difficult to determine the mechanism by using only a diameter change of the microcapsules. Therefore, one additional way to establish the mechanism is the analysis of the formation of the microcapsule structure. In this study, The Hough transformation algorithm was used for the image segmentation, the simple feature extractions were checked and the support vector machine and the k-nearest neighbors algorithm were used as classifiers in order to analyze the structure of the microcapsules which were prepared by solvent evaporation method from a solid in oil in water, S/O/W, emulsion system. The structural distribution was analyzed by the developed detection method. The microcapsules had a specific structural distribution which are monocore, multicore, and other aggregated structures. The structural distribution was changed by the preparation condition. The monocore structure was dominant by increasing in the amount of water soluble solid particles added in the organic phase.

No abstract

Tests are frequently used in any field of science education to assess students’ knowledge and skills. In this paper, we briefly introduce the results of the analysis on the test data of twelve grade students in Mongolia. These analyses are based on two approaches- classical test theories and cluster analysis. It is less important to measure learners’ attitudes through only one subject. We believe that students’ attitudes towards learning academic subjects and acquiring scientific education can be defined through their achievement data on their knowledge and skills on multiple subjects. According to learning attitudes, most researchers analyze the data using a survey that includes “Likert” scale statements and questions. In this paper, we have categorized students' learning attitudes based on their results of academic performances that assess only students’ knowledge and skills. Students are categorized into five groups according to their learning attitudes based on the two-step clustering components. Кластер анализ ашиглан суралцагчдыг сурах хандлагаар ангилсан нь ХураангуйСуралцагчдын мэдлэг, чадварыг үнэлэхэд ихэвчлэн тестийг ашигладаг. Уг өгүүлэлд Сүхбаатар аймгийн II сургуулийн 12-р ангийн суралцагчдын улсын шалгалтын үр дүнгийн өгөгдөлд классик тестийн онол, кластер анализ зэрэг аргыг хэрэглэн анализ хийсэн үр дүнг толилуулж байна. Ангийн суралцагчдын сурах хандлагын хэв маягийг зөвхөн нэг хичээлийн эцсийн дүнгээр хэмжих нь ач холбогдол багатай. Ихэнх судлаачид суралцагчдыг сурах хандлагаар ангилахдаа лайкертын хэмжээс бүхий өгүүлбэрүүд болон асуултуудыг агуулсан судалгааны асуулгаар цуглуулсан өгөгдөлд анализ хийдэг. Харин, бид бүхэн зөвхөн суралцагчдын мэдлэг, чадварыг үнэлэх академик гүйцэтгэлийн үр дүнгээр суралцагчдыг сурах хандлагаар ангилсан нь онцлогтой. Шинжлэх ухааны буюу академик боловсролтой холбоотой сурах хандлагыг олон хичээлийн мэдлэг, чадварын цогц байдлаар авч үзэх нь зүйтэй гэдгийг баталж, кластерчлалын хоёр алхамт аргад тулгуурлан суралцагчдыг сурах хандлагаар 5 бүлэгт ангиллаа. Түлхүүр үг: Классик тестийн онол, кластер анализ, сурах хандлага, үнэлгээ, даалгаврын хариултын онол