In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Example Input: Estimating inhalation dose accurately under realistic conditions can enhance the accuracy of risk assessment. Conventional methods to quantify aerosol concentration that susceptible victims in contaminated environments are exposed to use real time particle counters to measure concentrations in environments without occupancy. Breathing-induced airflow interacts and influences concentration around nostrils or mouth and alter the ultimate exposure. This subject has not yet been systematically studied, particularly under transient emission. In this work, an experimental facility comprising two manikins was designed and fabricated. One of them mimicked realistic breathing, acting as a susceptible victim. Both steady and episodic emissions were generated in an air-conditioned environmental chamber in which two different ventilation schemes were tested. The scaled-dose of the victim under different expiratory velocities and pulmonary ventilation was measured. Inferring from results obtained from comprehensive tests, it can be concluded that breathing has very significant influence on the ultimate dose compared with that without breathing. Majority of results show that breathing reduces inhalation quantity and the reduction magnitude increases with breathing rate. This is attributed to the fact that the exhalation process plays a more significant role in reducing the dose level than the enhanced effect during inhalation period. The higher the breathing rate, the sharper the decline of the resultant concentration would be leading to lower dose. Nevertheless, under low pulmonary ventilation, results show that breathing increases dose marginally. Results also reveals that ventilation scheme also affects the exposure.
Example Output: An experimental study quantifying pulmonary ventilation on inhalation of aerosol under steady and episodic emission

Example Input: Background/Aims: Interferon and ribavirin combination therapy for chronic hepatitis C induces a low response rate in human immunodeficiency virus (HIV) infected patients. To assess the impact of intensification of interferon administration and of the addition of amantadine on the efficacy and safety of standard anti-hepatitis C virus (HCV) treatment in HIV-infected patients. Methods: Multicentre, prospective, open-label, randomized, phase III clinical trial. Eighty co-infected patients were randomized to receive ribavirin 800 -1000 mg/day in combination with, group A: interferon alpha2a 3 MIU thrice weekly; group B: IFNa2a 3 MIU daily, plus amantadine 200 mg/day; treatment duration was 24 -48 weeks according to HCV genotype. Results: Forty-one patients were randomized in group A and 39 in group B. Intention-to-treat analysis showed a sustained virological response, defined as HCV-RNA negativization, 6 months after stopping treatment in 22% of patients from group A and 13% from group B (P > 0.05). The lack of a 2-log drop in HCV-RNA levels after 12 weeks of treatment showed a 100% predictive value of lack of sustained response. Conclusions: Amantadine addition and interferon intensification do not improve the low efficacy of combination of interferon alfa plus ribavirin in HIV/HCV co-infected patients. Patients with no early virologic response did not have any probability of sustained response. q
Example Output: A randomized, controlled trial of triple antiviral therapy as initial treatment of chronic hepatitis C in HIV-infected patients q

Example Input: Traditionally, most of the proposed probabilistic models of decision under uncertainty rely on numerical measures and representations. Alternative proposals call for qualitative (non-numerical) treatment of uncertainty, based on preference relations and belief orders. The automation of both numerical and non-numerical frameworks surely represents a preliminary step in the development of inference engines of intelligent agents, expert systems, and decision-support tools. In this paper we exploit Answer Set Programming to formalize and reason about uncertainty expressed as belief orders. The availability of ASP-solvers supports the design of automated tools to handle such formalizations. Our proposal reveals particularly suitable whenever the domain of discernment is partial. We first illustrate how to automatically "classify ^^, according to the most well-known uncertainty frameworks, any given qualitative uncertainty assessment. Then, we show how to compute an enlargement of the assessment, to any other new inference target.
Example Output:
Management of uncertainty orderings through ASP