2,478 research outputs found
A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks
A climate of increasing deregulation in traditional highway transportation,
where the private sector has an expanded role in the provision of traditional
transportation services, provides a background for practical policy issues to be investigated.
One of the key issues of interest, and the focus of this chapter, would
be the equilibrium decision variables offered by participants in this market. By assuming
that the private sector participants play a Nash game, the above problem can
be described as a Bi-Level Variational Inequality (BLVI). Our problem differs from
the classical Cournot-Nash game because each and every player’s actions is constrained
by another variational inequality describing the equilibrium route choice of
users on the network. In this chapter, we discuss this BLVI and suggest a heuristic
coevolutionary particle swarm algorithm for its resolution. Our proposed algorithm
is subsequently tested on example problems drawn from the literature. The numerical
experiments suggest that the proposed algorithm is a viable solution method for
this problem
Recommended from our members
Informed Consent for the Human Research Subject with a Neurologic Disorder.
The doctrine of informed consent sits at the intersection of law, ethics, and neuroscience, posing unique challenges for human subject research involving neurological patients. These challenges are compounded by the variegated nature of both neurological injury and the law governing research consent. This article provides a framework for investigators likely to encounter subjects with some degree of neurological impairment, whose capacity to consent requires scrupulous assessment prior to enrollment in research trials. We consider several researches and disease contexts-from emergency epilepsy research to long-term dementia research-and clarify the ethical and legal principles governing consent for participation in each. We additionally explore empirical research on consent capacity and survey several areas of emerging ethical import that will require the attention of investigators in decades to come
Ownership and Property Rights Issues in Watershed Resource Management
Well-defined property rights recognize the ownership of material and abstract things. The low enforcement of property rights for a certain resource may lead to its abuse. Hence, its application on watershed needs to be understood. This paper attempts to show the dualistic system of considering property rights and its implication to policy research.natural resources and environment, property rights, watershed
Ownership and Property Rights Issues in Watershed Resource Management
Well-defined property rights recognize the ownership of material and abstract things. The low enforcement of property rights for a certain resource may lead to its abuse. Hence, its application on watershed needs to be understood. This paper attempts to show the dualistic system of considering property rights and its implication to policy research.natural resources and environment, property rights, watershed
The Philippine Health Institutions: Some Problems, Approaches and Policy Issues
This paper presents a brief analysis of the current situation of Philippine health institutions using secondary data. It locates the different types of health institutions, describes its major problems, discusses recent approaches these institutions have adopted to improve delivery of health care services and extricates policy-related issues and data gaps from available literature.health sector, hospitals, hospital care, health centers
Effect of Organosilicone Surfactant on Uptake and Translocation of Glyphosate in Pennisetum Polystachion L
The effect of adding organosilicone surfactant, Pulse® on efficacy, uptake and
translocation of glyphosate (Roundup®) for the control of Pennisetum polystachion was
evaluated in the glasshouse. The dose-response study with glyphosate on 9-week old
P. polystachion showed that at the rate of 1.08 kg a.e.iha, glyphosate caused complete
mortality of the plants. It was estimated that dosage between 360 to 540 g a.e./ha gave
50% mortality.
When Pulse® was added to the glyphosate spray solutions, the bioefficacy of
glyphosate on P. polystachion increased as the concentration of Pulse® increased. The
optimum concentration ofPulse® was 0.2 % w/w above which no significant increase in
the bioefficacy was observed. Spray deposition studies using tlourescent tracer technique
revealed that the mixture of glyphosate and Pulse® gave 42% higher spray deposition
compared to glyphosate alone, thus contributing to the increase in bioefficacy of
glyphosate observed in the mixture
A textual-based featuring approach for depression detection using machine learning classifiers and social media texts
Depression is one of the leading causes of suicide worldwide. However, a large percentage of cases of depression go undiagnosed and, thus, untreated. Previous studies have found that messages posted by individuals with major depressive disorder on social media platforms can be analysed to predict if they are suffering, or likely to suffer, from depression. This study aims to determine whether machine learning could be effectively used to detect signs of depression in social media users by analysing their social media posts—especially when those messages do not explicitly contain specific keywords such as ‘depression’ or ‘diagnosis’. To this end, we investigate several text preprocessing and textual-based featuring methods along with machine learning classifiers, including single and ensemble models, to propose a generalised approach for depression detection using social media texts. We first use two public, labelled Twitter datasets to train and test the machine learning models, and then another three non-Twitter depression-class only datasets (sourced from Facebook, Reddit, and an electronic diary) to test the
performance of our trained models in other social media sources. Experimental results indicate that the proposed approach is able to effectively detect depression via social media texts even when the training datasets do not contain specific keywords (such as ‘depression’ and ‘diagnose’), as well as when unrelated datasets are used for testing
- …