Derivation of Monotone Decision Models from Non-Monotone Data

The objective of data mining is the extraction of knowledge from databases. In practice, one often encounters difficulties with models that are constructed purely by search, without incorporation of knowledge about the domain of application.In economic decision making such as credit loan approval or risk analysis, one often requires models that are monotone with respect to the decision variables involved.If the model is obtained by a blind search through the data, it does mostly not have this property even if the underlying database is monotone.In this paper, we present methods to enforce monotonicity of decision models.We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone.In addition, it is shown that monotone decision trees derived from cleaned data perform better compared to trees derived from raw data.decision models;knowledge;decision theory;operational research;data mining

Antonymy in the Terminological System of Logistics in English and Bulgarian

This paper reflects on logistics terminology and the relationships defining “oppositeness of meaning”. It briefly reviews theoretical assumptions in the sphere of antonymy and offers a classification of antonyms from a morphological and semantic point of view. The examples discussed are excerpted from documented materials standardized and consistent within the area of logistics. The findings are a good starting point for further research in this relatively new complex discipline whose terminological system is still in the process of establishment, evolution and modification. The topic is worth discussing in order to meet our syllabus goals and needs of learners who are going to function in a multinational environment and be faced with varieties of English as the primary language for international communication

Understanding the Impacts of Altering Safe Harbor Plans within Small Business Organizations

In the past three decades, many companies have adopted less expensive, relatively easier to establish, modify, amend, and terminate safe harbor 401 (k) retirement plans. By adopting the safe harbor, 401(k) plans, smaller private employers have enacted retirement incentives, shared the investment and managing responsibilities, and assisted their employees with building their retirement savings (Ali & Frank, 2019; Clark et al., 2019a). Furthermore, many small companies have reflected the impacts of the economic fluctuations and the retirement legislation changes, amended their safe harbor 401(k) plans, and lowered their retirement investments (Card & Ransom, 2011). The high number of plan amendments and the economic uncertainties have limited the employees’ abilities to accumulate retirement savings (Benartzi & Thaler, 2013; Libson, 2017). As a result, the increasing number of safe harbor amendments and the lack of financial education among employers and employees have amplified the retirement crisis in the United States (Acevedo, 2016; Clark et al., 2019b; Smith, 2016). Enhancing an understanding of the employees’ perceptions of engaging in retirement planning was essential for this research. The study focused on exploring the adverse impacts of amendments in the safe harbor 401(k) retirement plans as perceived and described by the retirement plan participants. It revealed the importance of pursuing proactive participation in retirement through employment. Finally, the research explored the impacts of the employees’ understanding and awareness of their opportunities to maximize their retirement benefits

Novel particles based on strong intermolecular interactions between polymers and low molecular weight molecules

Strong molecular interactions play an important role in many biological processes and in the formation of new materials. In particular, non-covalent interactions between polymers and low molecular weight molecules can lead to self-assembly and formation of insoluble complexes, which can be unitized as building blocks for new materials or for stabilization of more complex soft matter systems. In this talk several examples of the use of intermolecular attraction for design of novel biobased particles will be presented. Strong H-bond formation and hydrophobic interactions will be used to design various colloidal particles, microcapsules for delivery of bioactive ingredients, and for stabilization of foams and emulsions. We will discuss the preparation, characterization, stability and application of these particles. Please click Additional Files below to see the full abstract

Risk Management in Higher Education Institutions in Bulgaria - State and Prospects

The paper examines the main challenges facing universities in Bulgaria. It presents organisational issues of risk management in the higher education system and focuses on practices that need to be changed in order to implement risk management. Based on comparative content analysis, it identifies key areas that should be the subject of intervention in educational and scientific organisations in order to achieve efficiency, effectiveness and appropriateness of the process. The paper proposes solutions to improve risk management in the sector. It outlines guidelines for introducing a systematic approach to risk management in order to improve the organisational environment and operational management

Factors influencing on the decision for abortion and not using contraception

The problems of the reproductive health and sexual behavior provoke an increasing scholarly interest and study in Bulgaria. Research made on various aspects of sexual behavior say, that abortion is still practiced as a means of regulating number of children in family and contraception are not known enough. The larger choice of contraceptive methods is associated with lower fertility and a smaller percentage of abortions

Patients report improvements in continuity of care when quality of life assessments are used routinely in oncology practice: Secondary outcomes of a randomised controlled trial.

INTRODUCTION AND AIM: In a randomised trial investigating the effects of regular use of health-related quality of life (HRQOL) in oncology practice, we previously reported an improvement in communication (objective analysis of recorded encounters) and patient well-being. The secondary aims of the trial were to measure any impact on patient satisfaction and patients' perspectives on continuity and coordination of their care. METHODS: In a prospective trial involving 28 oncologists, 286 cancer patients were randomised to: (1) intervention arm: regular touch-screen completion of HRQOL with feedback to physicians; (2) attention-control arm: completion of HRQOL without feedback; and (3) control arm: no HRQOL assessment. Secondary outcomes were patients' experience of continuity of care (Medical Care Questionnaire, MCQ) including 'Communication', 'Coordination' and 'Preferences to see usual doctor' subscales, patients' satisfaction, and patients' and physicians' evaluation of the intervention. Analysis employed mixed-effects modelling, multiple regression and descriptive statistics. RESULTS: Patients in the intervention arm rated their continuity of care as better than the control group for 'Communication' subscale (p=0.03). No significant effects were found for 'Coordination' or 'Preferences to see usual doctor'. Patients' evaluation of the intervention was positive. More patients in the intervention group rated the HRQOL assessment as useful compared to the attention-control group (86% versus 29%), and reported their doctors considered daily activities, emotions and quality of life. CONCLUSION: Regular use of HRQOL measures in oncology practice brought changes to doctor-patient communication of sufficient magnitude and importance to be reported by patients. HRQOL data may improve care through facilitating rapport and building inter-personal relationships

Monotone Models for Prediction in Data Mining.

This dissertation studies the incorporation of monotonicity constraints as a type of domain knowledge into a data mining process. Monotonicity constraints are enforced at two stages¿data preparation and data modeling. The main contributions of the research are a novel procedure to test the degree of monotonicity of a real data set, a greedy algorithm to transform non-monotone into monotone data, and extended and novel approaches for building monotone decision models. The results from simulation and real case studies show that enforcing monotonicity can considerably improve knowledge discovery and facilitate the decision-making process for end-users by deriving more accurate, stable and plausible decision models.