Examination of the role of competitive advantage in the relationship between the marketing intelligence and export performance of the companies located in the industrial town of Ilam city

The current paper was conducted with the aim of investigating the role of the competitive advantage in the relationship between the marketing intelligence on the export performance within the scope of the activities of the Industrial towns in the city of Ilam. To attain the research goals, as many as 155 people of mangers and experts were selected through simple random method for responding to the research questionnaires. The research sample group responded to the Marketing Intelligence (L.A. Cacciolatti and A. Fearne) and the Competitive advantage and Exports performance (Murray et al) questionnaires on the Likert scale. The data taken from the research questionnaires were analyzed by using the Pearson correlation coefficient, structural equation modeling and regression analysis. The findings resulting from the data analysis implicated that there is a significant and direct relationship between dimensions of the marketing intelligence (type of information, sources of information, and alternation in using information) and the competitive advantage (r=0.986) and between the competitive advantage and the export performance (r=0.925). The results of the structural equation modeling and regression analysis illustrated that the competitive advantage plays a mediating role in the relationship between the marketing intelligence and export performance of the companies among the industrial companies. The findings suggest that the marketing intelligence will at first result in enhanced competitive advantage of the companies and then the competitive advantage will bring about a boost to the export performance of the companies

A fuzzy-tabu real time controller for sampling-based motion planning in unknown environment

Sampling-based path planning methods for autonomous agents are one of the well-known classes of robotic navigation approaches with significant advantages including ease of implementation and efficiency in problems with high degrees of freedom. However, there are some serious drawbacks like inability to plan in unknown environments, failure in complex workspaces, instability of results in different runs, and generating non-optimal solutions; which make sampling-based planners less efficient in practice. In this paper, a fuzzy controller is proposed which utilizes the heuristic rules of Tabu search to improve the quality of generated samples. The main contribution of this work is the ability of the proposed sampling-based planner to work effectively in unknown environments and to plan efficiently in complex workspaces by letting the fuzzy-Tabu controller check the quality of the generated samples before any further processing. The efficiency of the proposed planner is tested in several workspaces and the comparison studies show significant improvement in runtime and failure rate. Furthermore, the decision variables of the proposed controller are discussed in detail to determine their effect on the performance of the algorithm

A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning

In this paper, we propose a new learning strategy for a probabilistic roadmap (PRM) algorithm. The proposed strategy is based on reducing the dispersion of the generated set of samples. We defined a forbidden range around each selected sample and ignored this region in further sampling. The resultant planner, called low dispersion-PRM, is an effective multi-query sampling-based planner that is able to solve motion planning queries with smaller graphs. Simulation results indicated that the proposed planner improved the performance of the original PRM and other low-dispersion variants of PRM. Furthermore, the proposed planner is able to solve difficult motion planning instances, including narrow passages and bug traps, which represent particularly difficult tasks for classic sampling-based algorithms. For measuring the uniformity of the generated samples, a new algorithm was created to measure the dispersion of a set of samples based on a predetermined resolution

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance

Detection Of Cryptococcus Neoformans By Semi-Nested Pcr In Cerebrospinal Fluid

Life-threatening infections caused by the encapsulated fungal pathogen Cryptococcus neoformans have been increasing steadily over the past 10 years. Cryptococcus neoformans is recognized as the most frequent fungal infection of the central nervous system (CNS) in immunocompetent as well as immunocompromised patients. We report the development of a semi-nested-PCR-based assay for the detection of C. neoformans in less than 100 yeast cells per ml of cerebrospinal fluid (CSF)

Can community health worker home visiting improve care-seeking and maternal and newborn care practices in fragile states such as Afghanistan? A population-based intervention study

Abstract Background The effects of community health worker (CHW) home visiting during the antenatal and postnatal periods in fragile- and conflicted-affected countries such as Afghanistan are not known. Methods We conducted a non-randomised population-based intervention study from March 2015 to February 2016. Two intervention and two control districts were selected. All female CHWs in the intervention districts were trained to provide eight home visits and behaviour change communication messages from pregnancy to 28 days postpartum. The primary outcome was the proportion of women who reported delivering in a health facility. Secondary outcomes were the proportion of women who reported attending a health facility for at least one antenatal and one postnatal visit. Outcomes were analysed at 12 months using multivariable difference-in-difference linear regression models adjusted for clustering. Results Overall, 289 female CHWs in the intervention districts performed home visits and 1407 eligible women (less than 12 months postpartum) at baseline and 1320 endline women provided outcome data (94% response rate). Facility delivery increased in intervention villages by 8.2% and decreased in the control villages by 6.3% (adjusted mean difference (AMD) 11.0%, 95% confidence interval (CI) 4.0–18.0%, p = 0.002). Attendance for at least one antenatal care visit (AMD 10.5%, 95% CI 4.2–16.9%, p = 0.001) and postnatal care visit (AMD 7.2%, 95% CI 0.2–14.2%, p = 0.040) increased in the intervention compared to the control districts. Conclusions CHW home visiting during the antenatal and postnatal periods can improve health service use in fragile- and conflict-affected countries. Commitment to scale-up from Ministries and donors is now needed. Trial registration This trial was retrospectively registered at the Australian and New Zealand Clinical Trial Registry (ACTRN12618000609257)