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

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