At the Intergenerational Transfer Elderly Population Based Shelter in Medan - Indonesia

Editorial Decision: Removed

Alien Registration- Decision, Jewell (Wade, Aroostook County)

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Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant

The energy efficiency of industrial plants is an important issue in any type of business but particularly in the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring better product quality, and generally increasing profits. This article describes a decision system developed for optimizing the energy efficiency of a petrochemical plant. The system has been developed after a data mining process of the parameters registered in the past. The designed system carries out an optimization process of the energy efficiency of the plant based on a combined algorithm that uses the following for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in the past and, on the other hand, a module of two neural networks to obtain new interpolated operation points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to select the parameters most important in the plant and to study the behavior of the energy efficiency index. This study also helped ensure an optimal training of the neural networks. The robustness of the system as well as its satisfactory results in the testing process (an average rise in the energy efficiency of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to implement and to integrate the decision system as a pilot software in an SCADA

Group Decision Making with Uncertain Outcomes: Unpacking Child-Parent Choices of High School Tracks

Predicting group decisions with uncertain outcomes involves the empirically difficult task of disentangling individual decision makers' beliefs and preferences over outcomes' states from the group's decision rule. This paper addresses the problem within the context of a consequential family decision concerning the high school track of adolescent children in presence of curricular strati cation. The paper combines novel data on children's and parents' probabilistic beliefs, their stated choice preferences, and families' decision rules with standard data on actual choices to estimate a simple model of curriculum choice featuring both uncertainty and heterogeneous cooperative-type decisions. The model's estimates are used to quantify the impact on curriculum enrollment of policies affecting family members' expectations via awareness campaigns, publication of education statistics, and changes in curricular specialization and standards. The latter exercise reveals that identity of policy recipients--whether children, parents, or both--matters for enrollment response, and underlines the importance of incorporating information on decision makers' beliefs and decision rules when evaluating policies.Choice under Uncertainty, Multilateral Choice, Heterogeneous Decision Rules, Curricular Tracking, Curriculum Choice, Child-Parent Decision Making, Subjective Probabilities, Stated and Revealed Preferences, Choice-Based Sampling

Decision Making Towards Maternal Health Services in Central Java, Indonesia

Background: Indonesia has always been struggling with maternal health issue even after the Millennium Development Goals (MDGs) programs were done. Prior research findings identified many factors which influenced maternal health status in developing countries such Indonesia and even though various efforts had been made, the impact of the transformation of maternal health behavior was minimal.Purpose: This study aimed to seek an understanding of the factors influencing decisions towards maternal health services.Methods: A case study with a single case embedded design was employed. Interviews and Focus Group Discussions (FGDs) were held to collect data from 3 health workers and 40 maternal women in a sub-district in Central Java, Indonesia.Results: Interviews with the village midwives as the main health providers in the Getasan sub-district concluded that there were several factors influencing the women\u27s decisions towards maternal services. The factors were options to have services with other health workers outside the area, and shaman services as alternative care and family influencing maternal health behaviors. The analysis of the FGDs also supported the village midwives\u27 statements that in spite of their awareness towards the available maternal health services, the existence of shamans and traditional beliefs strongly affected their decision.Conclusion: The findings in this study showed that cultural issues prevented the maximum maternal health status in Getasan sub-district. This study recommends Puskesmas (Primary Health Care) as the first level of health institutions in Indonesia to support the village midwives\u27 roles within their target area

Treatment decision-making capacity in children and adolescents hospitalized for an acute mental disorder: The role of cognitive functioning and psychiatric symptoms

OBJECTIVE: This study was conducted to assess treatment decision-making capacity (TDMC) in a child and adolescent psychiatric sample and to verify possible associations between TDMC, psychiatric symptom severity, and cognitive functioning. METHODS: Twenty-two consecutively recruited patients hospitalized for an acute mental disorder, aged 11-18 years, underwent measurement of TDMC by the MacArthur Competence Assessment Tool for Treatment (MacCAT-T). The MacCAT-T interview focused on patients' current treatment, which comprised second-generation antipsychotics (45.5%), first-generation antipsychotics (13.6%), antiepileptic drugs used as mood stabilizers or lithium carbonate (45.5%), selective serotonin reuptake inhibitors (32%), and benzodiazepines (18%). We moreover measured cognitive functioning (Wechsler Intelligence Scale for Children III) and psychiatric symptom severity (Brief Psychiatric Rating Scale v 4.0). RESULTS: Patients' TDMC varied within the sample, but MacCAT-T scores were good in the sample overall, suggesting that children and adolescents with severe mental disorders could be competent to consent to treatment. The TDMC proved independent of psychiatric diagnosis while being positively associated with cognitive functioning and negatively with excitement. CONCLUSION: The MacCAT-T proved feasible for measuring TDMC in a child and adolescent psychiatric sample. TDMC in minors with severe mental disorders was not necessarily impaired. These results deserve reconsidering the interplay between minors and surrogate decision-makers as concerning treatment decisions

Reframing the EU budget- decision-making process

This paper traces the history of the EU budget and draws lessons for the review to come. Whatever reforms are proposed, the authors believe that they must serve to shift spending to policy areas and instruments where the EU can best add value while at the same time recognising the political need for member states to present EU budget negotiation results in Â?net-balanceâ?? terms. A two-stage negotiation is proposed: first member states should negotiate and agree on what constitute EU public goods. Everything else would thereafter - by default - be deemed redistributive/compensatory spending to be financed on the basis of member statesâ?? current overall net balances.

Decision Stream: Cultivating Deep Decision Trees

Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Tree node splitting based on relevant feature selection is a key step of decision tree learning, at the same time being their major shortcoming: the recursive nodes partitioning leads to geometric reduction of data quantity in the leaf nodes, which causes an excessive model complexity and data overfitting. In this paper, we present a novel architecture - a Decision Stream, - aimed to overcome this problem. Instead of building a tree structure during the learning process, we propose merging nodes from different branches based on their similarity that is estimated with two-sample test statistics, which leads to generation of a deep directed acyclic graph of decision rules that can consist of hundreds of levels. To evaluate the proposed solution, we test it on several common machine learning problems - credit scoring, twitter sentiment analysis, aircraft flight control, MNIST and CIFAR image classification, synthetic data classification and regression. Our experimental results reveal that the proposed approach significantly outperforms the standard decision tree learning methods on both regression and classification tasks, yielding a prediction error decrease up to 35%