3D Dynamic Motion Planning for Robot-Assisted Cannula Flexible Needle Insertion into Soft Tissue

In robot-assisted needle-based medical procedures, insertion motion planning is a crucial aspect. 3D dynamic motion planning for a cannula flexible needle is challenging with regard to the nonholonomic motion of the needle tip, the presence of anatomic obstacles or sensitive organs in the needle path, as well as uncertainties due to the dynamic environment caused by the movements and deformations of the organs. The kinematics of the cannula flexible needle is calculated in this paper. Based on a rapid and robust static motion planning algorithm, referred to as greedy heuristic and reachability-guided rapidly-exploring random trees, a 3D dynamic motion planner is developed by using replanning. Aiming at the large detour problem, the convergence problem and the accuracy problem that replanning encounters, three novel strategies are proposed and integrated into the conventional replanning algorithm. Comparisons are made between algorithms with and without the strategies to verify their validity. Simulations showed that the proposed algorithm can overcome the above-noted problems to realize real-time replanning in a 3D dynamic environment, which is appropriate for intraoperative planning. © 2016 Author

Evaluation of the Effect of Microbial Combination Flooding

The microbial combination flooding is a method of microbial enhanced oil recovery, which use the composite system produced by biopolymers and bio-surfactants to drive oil. In this paper, we chose B2/DJ composite system through the stability test, and study the experimental of indoor flooding with the B2/DJ composite, it drew a conclusion that the oil recovery was improved obviously after water flooding. The oil recovery was enhanced 13%

The early design stage of a novel Solar Thermal Façade (STF) for building integration: energy performance simulation and socio-economic analysis

This paper provides a feasibility study of a new solar thermal façade (STF) concept for building integration from both technical and economic aspects in Shanghai area of China. The whole set of technical evaluation and economic analysis was investigated through simulation of a reference DOE residential building model in IES-VE software and a dedicated dynamic business model consisting of several critical financial indexes. In order to figure out the cost effectiveness of the STF concept, research work consisted of: (1) exploring the overall feasibility, i.e. energy load, energy savings, operational cost and environmental benefits, and (2) investigating the financial outputs for investment decisions within three different purchase methods. This paper presents a multidisciplinary research method that is expected to be beneficial and supportive for the strategic decision at the early design stage and it also offers a different angle to assess the economic performance of the STF application

Assessment of the effectiveness of investment strategy in solar photovoltaic (PV) energy sector: a case study

Solar photovoltaic (PV) energy is now promising to offer potential solutions for sustainable development, especially in China. A representative Chinese solar PV manufacturer - Shunfeng International Clean Energy Limited (SFCE) - is therefore assessed in this paper, including (1) investment strategies in China's recent macroeconomic exposure; (2) the market exposure and vulnerability. The macroeconomic challenges in case of China's continuous GDP growth would have significant implications for SFCE's investment strategy. Although SFCE's vulnerability is high, it has mediated its macro exposure and protect itself by advanced non-pricing competition, product/service differentiation, vertical and horizontal integration, and high-profit diversification etc. The research result is expected to offer useful indications for solar PV companies to adapt and succeed in the future energy industry and simultaneously help the world to mitigate climate change

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

Abstract Objective Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Methods Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. Results This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. Conclusion The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.This study was supported in part by grants from the NHLBI 5U19HL065962 and the NCI R01CA141307. ML is supported by the NLM training grant 3T15LM007450-08S1. JS is partially supported by the 2010 NARSAD Young Investigator Award. ZZ is partially supported by the 2009 NARSAD Maltz Investigator Award. MM is supported by a Veterans Administration HSR&D Career Development Award (CDA-08-020)

Parametric study of a novel gravity assisted loop heat pipe (galhp) with composite mesh-screen wick structure

This article carried out a parametric study of the thermal performance of a novel gravity assisted loop heat pipe (GALHP) with composite mesh-screen wick structure. A refined threeway structure with interior liquid-vapour separator was developed on top of the evaporator to enable a gravity-assisted operation, which not only simplified the corresponding wick structure but also eliminated the ‘dry-out’ potential in conventional GALHPs. A dedicated simulation model was developed on basis of the heat transfer and the flow characteristics derived from the governing equations of mass, energy, and momentum. This model has been validated by authors’ experiment work with the ability to predict the GALHP thermal performance at a reasonable accuracy. It was found that the GALHP thermal performance, represented by the reciprocal of overall thermal resistance, varies directly with applied heat load, evaporator diameter, vapour-liquid separator diameter, and mass flow rate of cooling fluid in the jacket, but inversely with condensation temperature. The research results will be useful for further design, optimisation, and application of such GALHP in the gravity-assisted circumstance.Papers presented to the 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Costa de Sol, Spain on 11-13 July 2016