Computation of Gutman Index of Some Cactus Chains

Let G be a finite connected graph of order n. The Gutman index Gut(G) of G is defined as $\sum_{\{x,y\}\subseteq V(G)}deg(x)deg(y)d(x, y)$, where deg(x) is the degree of vertex x in G and d(x, y) is the distance between vertices x and y in G. A cactus graph is a connected graph in which no edge lies in more than one cycle. In this paper we compute the exact value of Gutman index of some cactus chains

Community based needs assessment in an urban area; A participatory action research project

<p>Abstract</p> <p>Background</p> <p>Community assessment is a core function of public health. In such assessments, a commitment to community participation and empowerment is at the heart of the WHO European Healthy Cities Network, reflecting its origins in health for all and the Ottawa Charter for Health Promotion. This study employs a participation and empowerment plan in order to conduct community assessment.</p> <p>Methods</p> <p>The method of participatory action research (PAR) was used. The study was carried out in an area of high socio-economic deprivation in Ardabil, a city in the northwest of Iran, which is currently served by a branch of the Social Development Center (SDC). The steering committee of the project was formed by some university faculty members, health officials and delegates form Farhikhteh non-governmental organization and representatives from twelve blocks or districts of the community. Then, the representatives were trained and then conducted focus groups in their block. The focus group findings informed the development of the questionnaire. About six hundred households were surveyed and study questionnaires were completed either during face-to-face interviews by the research team (in case of illiteracy) or via self-completion. The primary question for the residents was: 'what is the most important health problem in your community? Each health problem identified by the community was weighted based on the frequency it was selected on the survey, and steering committee perception of the problem's seriousness, urgency, solvability, and financial load.</p> <p>Results</p> <p>The main problems of the area appeared to be <it>the asphalt problem</it>, <it>lack of easy access to medical centers</it>, <it>addiction among relatives </it>and <it>unemployment of youth</it>. High participation rates of community members in the steering committee and survey suggest that the PAR approach was greatly appreciated by the community and that problems identified through this research truly reflect community opinion.</p> <p>Conclusions</p> <p>Participatory action research is an effective method for community assessments. However, researchers must rigorously embrace principles of mutual cooperation, respect for public ideas, and a robust belief in community empowerment in order to pave the way for responsible and active citizen participation in the various stages of research.</p

Computational analysis of optimisation algorithms with artificial intelligence

Artificial intelligence is a way of making a computer behave 'intelligently'. This can be accomplished by: studying how people think when they are trying to make decisions and solve problems; breaking those thought processes down into basic steps, and finally designing a computer program that solves problems using those same steps.  AI  thereby provides  a simple, structured approach to designing complex decision making programs. The goal of an AI system is to analyse human behaviour in the fields of perception, comprehension and decision making with the ultimate hope of reproducing the  behaviour on  a machine, namely a computer. One major category of AI techniques is 'genetic algorithm'. Although it is recognised that the performance of an  evolutionary system such as GA is affected by the parameters that are employed to implement them, there is  hardly any work known to us that has shed much light on the  interdependencies and interactions between these parameters. Most studies on the effects of these parameters on performance of GA-based systems have focused on a parameter,  at a time, without considering the  effect of other parameters on that parameter and vice versa. Consequently, there is hardly any theory about the interactions and interdependencies of these parameters. This paper contributes towards correcting the situation mentioned above by examining empirically the relationship between three  parameters of GAs

A Case Report of Incomplete Capgras Syndrome: a Rare Variant of the Syndrome

Abstract: Capgras syndrome is one of the misidentification syndromes. In this phenomenon the patient has a delusional belief that familiar persons have been replaced by imposters. We report a middle aged schizophrenic female who believed that the lower half of her body has been replaced by that of another person, known as prostitute by the patient. She also reported hallucination of sexual intercourse. Rare variants of Capgras syndromes have been reported so far, but there is no report of delusion of half of body replacement. The mentioned case can be considered as incomplete Capgras syndrome that is another variant of this syndrome. Keywords: Capgras syndrome, Schizophrenia, Incomplet

Integrating CSF, AHP and genetic algorithms for information systems planning

A new integrated model is developed for information systems planning. In this model, entitled HISSPM, a combination of the strengths of Critical Success Factors (CSF) method, Analytic Hierarchy Process (AHP) technique and Genetic Algorithms (GA) is used to identify and prioritize the required information systems. This model considers both qualitative and quantitative factors, as well as the importance of the factors to decision-makers, and can solve problems with relatively wide search space. Comparing the results of the proposed model with those of the famous available models reveals that the proposed model is valid and efficient. The model tested on a real world problem, resulting in a portfolio of prioritized information systems, under certain conditions and constraints, which have alignment with organizational goals, strategies and plans. This model can help encourge managers in optimal or near-optimal decision-making boost investments on required information systems

A scoping review of importation and predictive models related to vector-borne diseases, pathogens, reservoirs, or vectors (1999-2016).

BACKGROUND:As globalization and climate change progress, the expansion and introduction of vector-borne diseases (VBD) from endemic regions to non-endemic regions is expected to occur. Mathematical and statistical models can be useful in predicting when and where these changes in distribution may happen. Our objective was to conduct a scoping review to identify and characterize predictive and importation models related to vector-borne diseases that exist in the global literature. METHODS:A literature search was conducted to identify publications published between 1999 and 2016 from five scientific databases using relevant keywords. All publications had to be in English or French, and include a predictive or importation model on VBDs, pathogens, reservoirs and/or vectors. Relevance screening and data characterization were performed by two reviewers using pretested forms. The data were analyzed using descriptive statistics. RESULTS:The search initially identified 19 710 unique articles, reports, and conference abstracts. This was reduced to 428 relevant documents after relevance screening and data charting. About half of the models used mathematical techniques, and the remainder were statistical. Most of the models were predictive (87%), rather than importation (5%). The most commonly investigated diseases were malaria and dengue fever. Around 12% of the publications did not report all the parameters used in their model. Only 29% of the models incorporated the impacts of climate change. CONCLUSIONS:A wide variety of mathematical and statistical models on vector-borne diseases exist. Researchers creating their own mathematical and/or statistical models may be able to use this scoping review to be informed about the diseases and/or regions, parameters, model types, and methodologies used in published models