Evaluating a policy aimed at creating co-operatives in Kazakhstan

The government of Kazakhstan is currently developing policies to stimulate milk production at an industrial production level through creating co-operatives. Their main target members are rural households, who are currently responsible for producing most of the milk consumed in Kazakhstan. In order to analyse and identify the determinant factors behind rural households’ motivation to join/create co-operatives and likewise public support for the government policy and in order to estimate its monetary value, a survey was used to collect information from 181 randomly selected rural households in the Akmola region and 307 randomly selected Kazakh citizens. The bivariate probit model was used to jointly analyse rural households’ intentions to join/create a production co-operative, accounting for the impact of psychological factors and socio-demographic characteristics along with each household’s attitudes to risk, their production structure, level of information about the government support programme and co�operatives, and cultural aspects as well as the household’s proximity to the main market. In addition, the drivers associated with public support for such a policy were examined using a contingent valuation method. These include psychological factors, the individuals’ views on the country’s former regime, their awareness of the governmental policy, their socio�demographic characteristics, and their household location. Their willingness to pay (WTP) for the policy was analysed using an interval regression model. Additionally, we examined changes in individuals’ WTP before and during the COVID-19 pandemic. In addition to indicating the determinants behind rural households' intention to join/create a production co-operative and Kazakh citizens' willingness to support the policy on co-operatives, the results of the study revealed that a third of rural households were interested in the policy. Moreover, the social value of the policy was found to be equal to the cost of the whole program after 10 years, indicating public support for this policy amongst Kazakh citizens. Taking into account these results, guidance for policymakers was prepared

Attitudes of Kazakh rural households towards joining and creating cooperatives

The government of Kazakhstan is currently developing strategies and policies to stimulate milk production at an industrial production level to increase milk processing capacity. We use and expand the Reasoned Action Approach as a framework to study the factors underlying rural household’s motivation to participate in a governmental programme aimed at increasing rural cooperative production in Kazakhstan to increase milk production using primary data acquired from 181 randomly selected dairy households in the Akmola region of Kazakhstan. We account for rural household's psychological factors and socio-demographic characteristics along with household’s risk attitudes, production structure, level of information about the government support programme and cooperatives, cultural aspects as well as the household’s proximity to the main market. A Bivariate Probit model is used to jointly estimate the impact of these factors on rural household's intention to join and create a cooperative. The results show that rural households which hold positive views towards cooperatives, have a relatively high production capacity, are aware/know of cooperatives, and do not have a dairy business as a source of household income are relatively keen to participate in collective actions. Perceived social norms and household’ risk attitudes also play a significant role rural household's intention to participate in collective actions. Finally, gender and nationality are found to be positively associated with joining and creating a cooperative, while higher educated rural households are found to be less motivated to participate in the programme. In order to stimulate milk production at an industrial production level through a policy that encourages collective action we recommend a policy that a) supports rural households which have the capacity to produce and are in need; b) is attractive to rural households which consider dairy as a source of income and c) is well disseminated and well explained to the targeted rural households

Would Kazakh citizens support a milk co-operative system?

We estimate the monetary value of a policy aimed at increasing rural co-operative production in Kazakhstan to increase milk production. We analyse the drivers associated with public support for such policy using the contingent valuation method. The role of individuals’ psychological aspects, based on the reasoned action approach, along with individuals’ views on the country’s past regime (i.e., to the former Soviet Union), their awareness about the governmental policy, their sociodemographic characteristics, and household location on their willingness to pay (WTP) for the policy is analysed using an interval regression model. Additionally, we examine changes in individuals’ WTP before and during the COVID-19 pandemic. The estimated total economic value of the policy is KZT 1335 bn for the length of the program at KZT 267 bn per year, which is approximately half the total program budget, which includes other interventions beyond the creation of production co-operatives. The total economic value of the policy would equal the cost of the whole program after 10 years, indicating public support for this policy amongst Kazakh citizens. Psychological factors, i.e., attitude, perceived social pressure, and perceived behavioural control, and the respondents’ awareness of the policy and views on the Soviet Union regime are associated with their WTP. Sociodemographic factors, namely, age, income, and education, are also statistically significant. Finally, the effect of the shocks of COVID-19 is negatively associated with the respondents’ WTP

Fire detection using deep learning methods

Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires

Classification of scientific manuscripts using text processing methods

Günümüzdeki teknolojik gelişmeler ile, kağıt üzerindeki metinlerin sayısal ortamlara aktarılması kolaylaşmıştır. Bu metinlere daha kolay erişilebilmesi için metin sınıflandırma yapılması gerekmektedir. Çok sayıdaki doğal dil metinlerini sınıflandırmadan önce metin işleme tekniklerinin uygulanması gereklidir. Metin işleme; dokümanlarda bulunan ham verileri sınıflandırmak için çeşitli teknikler ile analiz etme işlemidir. Bu çalışmada Türkçe bilimsel makalelerden bir veri kütüphanesi oluşturulmuştur ve değişik metin işleme ve sınıflandırma yöntemleri ile en yüksek başarı elde edilmeye çalışılmıştır. Bu amaçla sıra ile metin sınıflandırma süreçleri (ön işleme, indeksleme, öznitelik seçme, sınıflandırma ve performans değerlendirme) uygulanmıştır. Bu çalışmada metinleri ifade etmek için kelimeler doğrudan alınarak kelime kökleri ile birlikte karakter 2-gram ve 3-gram yöntemi kullanılmıştır. Bahsettiğimiz yöntemlerden elde ettiğimiz verileri sayısallaştırmak için vektör uzayı modelinin TF, ikili ve en yaygın olarak kullanılan TF-IDF ağırlıklandırma yöntemleri uygulanmıştır. Nitelikli özniteliklerin seçilip gereksiz olanlarının atılabilmesi için bilgi kazancı ve korelasyon tabanlı öznitelik seçme yöntemleri kullanılmıştır. En bilinen sınıflandırma yöntemleri olan K-NN, Naive Bayes, Multinominal Naive Bayes ve DVM Weka programının yardımı ile çalışmada önerilen yöntemin performansını karşılaştırmak üzere kullanılmıştır. Ayrıca diğer bir veri kümesi (internet üzerindeki Türkçe haberlerden oluşturulan 1150 haber) kullanılarak karşılaştırma yapılmıştır. Sonuç olarak kelime kökleri ile elde ettiğimiz öznitelik vektörleri için en iyi sonucu ikili ağırlıklandırma yöntemi vermiştir. Karakter 2-gram ve 3-gram yönteminde ise TF ağırlıklandırma yöntemi en yüksek başarı göstermiştir. Korelasyon tabanlı öznitelik seçme yöntemine göre bilgi kazancı yöntemi iyi sonuçlar vermiştir. Öznitelikler düzeyinde birleştirme işleminin performansı daha da arttığı ve iyi etkilediği belirlenmiştir. Tekil olarak en iyi sonucu %99,44 başarı ile ?kelime kökleri+bilgi kazancı+ikili+TF+TF-IDF? öznitelik vektörü vermiştir. Bu çalışmada açıklanan metin işleme yöntemlerini uygulayarak önceki çalışmadan daha başarılı sonuçlar elde edilmiştir.Transferring of paper-based texts to digital media has become easier with today?s technological advances. Classification of texts should be made in order to access information more easily. Before classification, text processing techniques must be applied many natural language texts. Text processing is the process of analyzing with variety of techniques in order to classify raw data in documents. In this study, a data set of scientific articles published in Turkish was built and it is aimed to obtain high success by applying different text processing and classification methods. With this aim text classification procedures (preprocessing, indexing, feature selection, classification and performance evaluation) were performed step by step. We used character 2-gram and 3-gram methods to choose the word stem in order to express the texts used in this study. To quantintify the data obtained from abovementioned methods, we applied TF, binary and most commonly used TF-IDF weighting methods of the vector space model. We used information gain and correlation based feature selection methods in order to choose the relevant features and remove the unnecessary ones. We used the most famous classifications methods, namely K-NN, Naive Bayes, Multinominal Naive Bayes and SVM, on the Weka software to benchmark the performance of the proposed method. In advance, data set was compared to an other one (1150 news published in Turkish in Internet). In conclusion, the best results regarding the feature vectors obtained using word stems were obtained from the double weighting method. For the character 2-gram and 3-gram methods, the best results were obtained from TF weighting method. The information gain method returned better results compared to the correlation based feature selection method. It yielded better performance on the fusion at feature level. The best result (99,44%) was obtained from the word stems+information gain+binary+TF+TF-IDF feature vector. By applying the text processing methods explained in this study, we obtained better results compared to the previous study

Attitudes of Kazakh Rural Households towards Joining and Creating Cooperatives

The government of Kazakhstan is currently developing strategies and policies to stimulate milk production at an industrial production level to increase milk processing capacity. We use and expand the reasoned action approach as a framework to study the factors underlying the rural household’s motivation to participate in a governmental programme aimed at increasing rural cooperative production in Kazakhstan to increase milk production using primary data acquired from 181 randomly selected dairy households in the Akmola region of Kazakhstan. We account for the rural household’s psychological factors and socio-demographic characteristics along with the household’s risk attitudes, production structure, level of information about the government support programme and cooperatives, cultural aspects as well as the household’s proximity to the main market. A bivariate probit model is used to jointly estimate the impact of these factors on the rural household’s intention to join and create a cooperative. The results show that rural households which hold positive views towards cooperatives, have a relatively high production capacity, are aware/know of cooperatives, and do not have a dairy business as a source of household income are relatively keen to participate in collective actions. Perceived social norms and household risk attitudes also play a significant role in the rural household’s intention to participate in collective actions. Finally, gender and nationality are found to be positively associated with joining and creating a cooperative, while higher educated rural households are found to be less motivated to participate in the programme. In order to stimulate milk production at an industrial production level through a policy that encourages collective action, we recommend a policy that (a) supports rural households which have the capacity to produce and are in need; (b) is attractive to rural households which consider dairy as a source of income; and (c) is well disseminated and well explained to the targeted rural households

Підхід до синтезу аперіодичної робастної системи автоматичного керування на основі градієнтно-швидкісного методу вектор-функцій Ляпунова

One of the actual problems for the theory and practice of control of dynamic objects is the development of methods for research and synthesis of control systems of multidimensional objects. The paper proposes a universal approach to construct Lyapunov vector functions directly from the equation of state of control system and a new gradient-speed method of Lyapunov vector functions to study aperiodic robust stability of linear control system with m inputs and n outputs. The study of aperiodic robust stability of automatic control systems is based on the construction of Lyapunov vector functions and gradient-speed dynamic control systems. The basic statements of Lyapunov's theorem about asymptotic stability and notions of stability of dynamic systems are used. The representation of control systems as gradient systems and Lyapunov functions as potential functions of gradient systems from the catastrophe theory allow to construct the full-time derivative of Lyapunov vector functions always as a sign-negative function equal to the scalar product of the velocity vector on the gradient vector. The conditions of aperiodic robust stability are obtained as a system of inequalities on the uncertain parameters of the automatic control system, which are a condition for the existence of the Lyapunov vector-function.  A numerical example of synthesis of aperiodic robustness of a multidimensional control object is given. The example shows the main stages of the developed synthesis method, the study of the system stability at different values of the coefficients k, confirming the consistency of the proposed method. Transients in the system satisfy all requirementsОднією з актуальних проблем теорії та практики керування динамічними об’єктами є розробка методів дослідження та синтезу систем керування багатовимірними об’єктами. У статті запропоновано універсальний підхід до побудови вектор-функцій Ляпунова безпосередньо з рівняння стану системи керування та новий градієнтно-швидкісний метод вектор-функцій Ляпунова для дослідження аперіодичної робастної стійкості лінійної системи керування з m входами та n виходами. Дослідження аперіодичної робастної стійкості систем автоматичного керування базується на побудові вектор-функцій Ляпунова та градієнтно-швидкісних систем динамічного керування. Використано основні положення теореми Ляпунова про асимптотичну стійкість та поняття стійкості динамічних систем. Представлення систем керування як градієнтних систем і функцій Ляпунова, як потенційних функцій градієнтних систем з теорії катастроф, дозволяє побудувати повну похідну векторних функцій Ляпунова завжди як знаконегативну функцію, що дорівнює скалярному добутку вектора швидкості на вектор градієнта. Отримано умови аперіодичної робастної стійкості як систему нерівностей щодо невизначених параметрів системи автоматичного керування, які є умовою існування вектор-функції Ляпунова. Наведено числовий приклад синтезу аперіодичної стійкості багатовимірного об'єкта керування. На прикладі показано основні етапи розробленого методу синтезу, дослідження стійкості системи при різних значеннях коефіцієнтів k, що підтверджує постійність запропонованого методу. Перехідні процеси в системі задовольняють усім вимога