A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

The most recent global wave of democratic reversal is marked by executive takeovers. Politically motivated interventions in domestic markets aimed at restructuring the underlying power dynamics in ...

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Building a Competitive Authoritarian Regime: State–Business Relations in the AKP’s Turkey

Within the European Union, risk-based funding requirements for life insurance companies are currently being revised as part of the Solvency II project. However, many insurers are struggling with the implementation, which is in part due to the ine‐cient methods underlying their numerical computations. We review these methods and propose a signiflcantly faster approach for the calculation of the required risk capital based on least-squares regression and Monte Carlo simulations akin to the well-known Least-Squares Monte Carlo method for pricing non-European derivatives introduced by Longstafi and Schwartz (2001, [20]).

/pdf/solvency-ii-and-nested-simulations-a-least-squares-monte-4byqnlk2o8.pdf

Solvency II and Nested Simulations - a Least-Squares Monte Carlo Approach

As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the analytical framework has vast potential for supporting support decision making by extracting value from big data.

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A study on decision-making of food supply chain based on big data

Putting Development First: The Importance of Policy Space in the WTO and International Financial Institutions edited by Kevin P. Gallagher

Is tatmini, yonetim psikolojisi dalinda en sik calisilan konulardan bir tanesidir. Ancak, ulkemizde universite ogretim elemanlarinin is tatmini duzeylerine yonelik calismalar oldukca sinirlidir. Bu calismanin amaci; Turkiye’deki bu boslugu bir derece olsun doldurabilmek ve is tatmini duzeyini etkileyen faktorleri belirtmektir. Bu amacla, anket calismasi uygulanmis ve anket formu, sekiz kamu universitesinden 138 ogretim elemaninca doldurulmustur. Uygulanan analiz sonucunda is tatmini duzeyinin; ozgurce karar verebilme, yeni programlar uygulamak icin sartlarin uygun olmasi, diger calisanlarla isbirligi ve ucret faktorlerinden etkilendigini soyleyebiliriz

/pdf/kamu-universiteleri-ogretim-elemanlarinin-is-tatmini-4iy31b88so.pdf

Kamu Üniversiteleri Öğretim Elemanlarının İş Tatmini Düzeyini Etkileyen Faktörler

Bayesian network (BN) is an efficient graphical method that uses directed acyclic graphs (DAG) to provide information about a set of data. BNs consist of nodes and arcs (or edges) where nodes represent variables and arcs represent relations and influences between nodes. Interest in organic food has been increasing in the world during the last decade. The same trend is also valid in Turkey. Although there are numerous studies that deal with customer perception of organic food and customer characteristics, none of them used BNs. Thus, this study, which shows a new application area of BNs, aims to reveal the perception and characteristics of organic food buyers. In this work, a survey is designed and applied in seven different organic bazaars in Turkey. Afterwards, BNs are constructed with the data gathered from 611 organic food consumers. The findings match with the previous studies as factors such as health, environmental factors, food availability, product price, consumers' income and trust to organizatio...

Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey

Turkish businessmen, politicians and most academicians tend to see foreign direct investment (FDI) as a remedy for the chronic lack of capital accumulation in Turkey. The meagre FDI inflows which followed the Customs Union Agreement with the European Union, in 1995, created a deep disappointment among these people. Efforts to attract foreign capital have intensified since 2005 and inflows have soared. However, the greater part of the increase is the result of the Turkish government's privatization programme of publicly owned companies, and the acquisition of private firms by large multinational companies, rather than greenfield investments. This research investigates FDI inflows to Turkey and tries to estimate the transfer of profits.

Foreign Direct Investment and Profit Transfers: The Turkish Case

The aim of this paper is to investigate the factors influencing the Beck Depression Inventory score, the Beck Hopelessness Scale score and the Rosenberg Self-Esteem score and the relationships among the psychiatric, demographic and socio-economic variables with Bayesian network modeling. The data of 823 university students consist of 21 continuous and discrete relevant psychiatric, demographic and socio-economic variables. After the discretization of the continuous variables by two approaches, two Bayesian networks models are constructed using the b n l e a r n package in R, and the results are presented via figures and probabilities. One of the most significant results is that in the first Bayesian network model, the gender of the students influences the level of depression, with female students being more depressive. In the second model, social activity directly influences the level of depression. In each model, depression influences both the level of hopelessness and self-esteem in students; additionally, as the level of depression increases, the level of hopelessness increases, but the level of self-esteem drops.

An Investigation into the Relationship among Psychiatric, Demographic and Socio-Economic Variables with Bayesian Network Modeling

In this article, we extend some variable selection criteria in regression analysis to heteroscedastic models. First, a sequential test procedure is proposed to identify potential heteroscedasticity of the error variances. Next, we develop a variance estimation method to estimate the variance–covariance matrix for data with unequal variances. We improve Mallows’ C p and AIC using the proposed variance estimation method. This work is motivated by the poor behavior of C p in highly heteroscedastic models and by the fact that C p can be written as a linear function of an F statistic for testing the fit of a regression model. The proposed method performs well for both homoscedastic and heteroscedastic data. Simulation results show that our method is superior to C p for data with significant heteroscedasticity and is comparable in accuracy for homoscedastic models. The new method is illustrated with real data.

Filiz Karaman

Papers

Kamu Üniversiteleri Öğretim Elemanlarının İş Tatmini Düzeyini Etkileyen Faktörler

Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey

Foreign Direct Investment and Profit Transfers: The Turkish Case

An Investigation into the Relationship among Psychiatric, Demographic and Socio-Economic Variables with Bayesian Network Modeling

Variable Selection for Heteroscedastic Data Through Variance Estimation