Improved dynamic equations for the generally configured Stewart platform manipulator

In this paper, a Newton-Euler approach is utilized to generate the improved dynamic equations of the generally configured Stewart platform. Using the kinematic model of the universal joint, the rotational degree of freedom of the pods around the axial direction is taken into account in the formulation. The justifiable direction of the reaction moment on each pod is specified and considered in deriving the dynamic equations. Considering the theorem of parallel axes, the inertia tensors for different elements of the manipulator are obtained in this study. From a theoretical point, the improved formulation is more accurate in comparison with previous ones, and the necessity of the improvement is clear evident from significant differences in the simulation results for the improved model and the model without improvement. In addition to more feasibility of the structure and higher accuracy, the model is highly compatible with computer arithmetic and suitable for online applications for loop control problems in hardware.     

Molecularly imprinted nanoparticles prepared by miniemulsion polymerization as selective receptors and new carriers for the sustained release of carbamazepine

Water-compatible imprinted nanoparticles were prepared for carbamazepine as a template and used for the selective extraction and controlled release of carbamazepine. Assay materials and drug delivery carriers were typically used in aqueous environments, so it is generally preferable to prepare solvent-free molecularly imprinted nanoparticles in water using the miniemulsion polymerization method. The present work investigates a bio-analytical strategy generically applicable to imprinted materials for molecular recognition studies, including equilibrium and non-equilibrium binding, and release experiments, increasing the knowledge of the molecular interactions between the template molecules and imprinted nanoparticles. The results showed that the imprinted nanoparticles exhibited a higher binding level and slower release rate than non-imprinted nanoparticles. The selectivity of imprinted nanoparticles for carbamazepine studied in comparison with an analogue compound, oxcarbazepine, the main metabolite of carbamazepine. The recovery and selectivity of carbamazepine in human serum was determined to be 100%, 1.7 times that of oxcarbazepine. The results indicated that carbamazepine-imprinted nanoparticles are appropriate for serum level determination of the drug in therapeutic range. The template to functional monomer ratio as a key factor controlling the recognition and release kinetic mechanism of imprinted nanoparticles is discussed. The imprinted nanoparticles prepared at the appropriate template to functional monomer mole ratio (2:8) exhibited the best drug affinity (5.1 times higher) and a slower drug release rate due to the interaction of carbamazepine with the imprinted cavities within the nanoparticles. Loaded imprinted nanoparticles as drug reservoirs were able to prolong carbamazepine release, in 1% wt sodium dodecyl sulfate aqueous solution, for more than 8 days.     

Recent developments in catalyst synthesis using DBD plasma for reforming applications

The catalyst has a significant role in gas processing applications such as reforming technologies for H₂ and syngas production. The stable catalyst is requisite for any industrial catalysis application to make it commercially viable. Several methods are employed to synthesize the catalysts. However, there is still a challenge to achieve a controlled morphology and pure catalyst which majorly influences the catalytic activity in reforming applications. The conventional methods are expansive, and the removal of the impurities are major challenges. Nevertheless, it is not straightforward to achieve the desired structure and stability. Therefore, significant interest has been developed on the advanced techniques to take control of the physicochemical properties of the catalyst through non-thermal plasma (NTP) techniques. In this review, the systematic evolution of the catalyst synthesis using NTP technique is elucidated. The emerging DBD plasma to synthesized and effective surface treatment is reviewed. DBD plasma synthesized catalyst performance in reforming application for H₂ and syngas production is summarised. Furthermore, the status of DBD plasma for catalyst synthesis and proposed future avenues to design environmentally suitable and cost-effective synthesis techniques are discussed.     

Barium promoted manganese oxide catalysts in low-temperature methane catalytic combustion

In this study, a series of BaO-MnO_x mixed oxide catalysts were synthesized by the mechanochemical method and employed in lean methane catalytic combustion (MCC) at low temperatures. The synthesized catalysts were characterized by XRD, BET, TGA, FT-IR, H₂-TPR, O₂-TPD, and FESEM analyses. The results indicated that the 10 wt% BaO-MnO_x catalyst with a BET surface area of 25 m² g^?1 possessed the best catalytic performance. The higher activity of the 10 wt% BaO-MnO_x catalyst was due to the higher ability to supply oxygen through the components during the MCC process. The light-off temperature corresponding to 50% of the methane conversion was about 330 °C, which was about 50 °C lower than the pure MnO_x. Moreover, for the BaO(10)-MnO_x catalyst, the 10 and 90% of methane conversion temperatures were about 305 and 427 °C, respectively. Also, the 10 wt% BaO-MnO_x catalyst exhibited high catalytic stability under dry feed condition at 450 °C for 50 h. Furthermore, the influence of various parameters such as calcination temperature, feed ratio, GHSV, pretreatment condition, and presence of water vapor in the feedstock was studied on the catalytic performance.     

Staging and management of lung cancer: sleeve resection

Sleeve lobectomy is a lung-saving procedure usually indicated for central tumors for which the alternative is a pneumonectomy. It preserves normal lung tissue and may enable pulmonary resection to be done in selected patients with inadequate cardiac or pulmonary reserve. One experience extends from January 1972 to December 1991, during which time 142 patients underwent a variety of sleeve resections for bronchogenic neoplasms. The majority of operations were upper-lobe sleeve resections (N = 110) and most procedures were considered complete and potentially curative (87%). There were three postoperative deaths (surgical mortality of 2.5%) and prolonged atelectasis was the most common major complication (N = 9). Follow-up was complete for the 139 survivors (mean follow-up time of 2,149 days) and overall survival was 46% at 5 years and 33% at 10 years. Five- and 10-year survivals for patients with stage I disease were 63% and 52%, respectively, while only 14% of patients with stage III disease survived 5 years. Local recurrences occurred in 23% of patients but when the resection had been complete, this incidence was 17% (21/124). These results indicate that sleeve resection is an adequate cancer operation for both compromised and uncompromised patients. Operative mortality, survival, and incidence of local recurrence are not different than what is seen after more conventional procedures.     

Synthesis and characterization of a novel pH-responsive nanocomposite hydrogel based on chitosan for targeted drug release

Over the last decade, nanocomposite hydrogels have been provided a new approach for the biomedical field. In this work, a novel pH-responsive nanocomposite hydrogel was fabricated using simultaneous in situ formation of magnetite iron oxide nanoparticles and hydrogel networks of poly(acrylic acid) grafted onto chitosan. The effects of various types of precursor molecules, pH, salt, and loading pressure were examined on the swelling properties of resulting nanocomposite hydrogels. The synthesized nanocomposite hydrogel was well characterized using different instruments. In vitro drug releasing behavior of doxorubicin was studied at pH 5.4 and 7.4. The drug release mechanism was investigated through different kinetic models. These experimental results open a new opportunity to make pH-responsive nanocomposite hydrogel devices for controlled delivery of drug.     

MMLT: A mutual multilevel trust framework based on trusted third parties in multicloud environments

In this article, a mutual multilevel trust framework is proposed, which involves managing trust from the perspective of cloud users (CUs) and cloud service providers (CSPs) in a multicloud environment based on a set of trusted third parties (TTPs). These independent agents are trusted by CUs and CSPs and distributed on different clouds. The TTPs evaluate the CUs' trustworthiness based on the accuracy of feedback ratings and assess the CSPs' trustworthiness based on the quality of service monitoring information. They are connected themselves through the trusted release network, which enables a TTP to obtain trust information about CSPs and CUs from other clouds. With the objective of developing an effective trust management framework, a new approach has been provided to improve trust-based interactions, that is, able to rank the trusted cloud services (CSs) based on CU's priorities via fuzzy logic. Fuzzy logic is applied to manage the different priorities of CUs, all the CUs do not have the same priorities to use trusted CSs. Customizing service ranking allows CUs to apply trusted CSs based on their priorities. Experiments on real datasets well matched the analytical results, indicating that our proposed approach is effective and outperforms the existing approaches.     

An EM based probabilistic two-dimensional CCA with application to face recognition

Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction. The method instead of concatenating the columns of the images to the one-dimensional vectors, directly works with two-dimensional image matrices. Although 2DCCA works well in different recognition tasks, it lacks a probabilistic interpretation. In this paper, we present a probabilistic framework for 2DCCA called probabilistic 2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the parameters. Experimental results on synthetic and real data demonstrate superior performance in loading factor estimation for P2DCCA compared to 2DCCA. For real data, three subsets of AR face database and also the UMIST face database confirm the robustness of the proposed algorithm in face recognition tasks with different illumination conditions, facial expressions, poses and occlusions.