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.     

New Sliding Mode Control of 2-DOF Robot Manipulator Based on Extended Grey Wolf Optimizer

International Journal of Control, Automation and Systems - This paper presents a novel sliding mode (NSMC) to control of a 2-DOF robot manipulator based on the extended grey wolf optimizer (EGWO)....     

HVS-based scalable image watermarking

This paper proposes a novel human vision system based, spread spectrum method to scalable image watermarking. A scalable decomposition of the watermark is spread into the entire frequency sub-bands of the wavelet decomposed image. At each wavelet sub-band, the watermark data are inserted into the selected coefficients of the sub-band in a manner that the watermark embedding visual artifact occurs in the highly textured, highly contrasted and very dark/bright areas of the image. In the lowest frequency sub-band of wavelet transform, the coefficients are selected by independent analysis of texture, contrast and luminance information. In high frequency sub-bands, the coefficient selection is done by analyzing coefficients amplitude and local entropy. The experimental results show that the watermarked test images are highly transparent and robust against scalable wavelet-based image coding even at very low bit-rate coding. The proposed approach can guarantee content authentication for scalable coded images, especially on heterogeneous networks which different users with different process capabilities and network access bandwidth use unique multimedia sources.

     

A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes

Multiresolution segmentation (MRS) is one of the most commonly used image segmentation algorithms in the remote-sensing community. This algorithm has three user-defined parameters: scale, shape, and compactness. The scale parameter (SP) is the most crucial one in determining the average size of the image segments generated. Since setting this parameter typically requires a trial-and-error process, automatically estimating it can expedite the segmentation process. However, most of automatic approaches are still iterative and can lead to a time-consuming process. In this article, we propose a new, non-iterative framework for estimating the SP with an emphasis on extracting individual urban buildings. The basis of the proposed method is to investigate the feasibility of associating the size of urban buildings with a corresponding ‘optimal’ (or at least reasonable) SP using an explicit mathematical equation. Using the proposed method, these two variables are related to each other by constructing a mathematical (regression) model. In this framework, the independent variables were chosen to be the typical size of buildings in a given urban area and the spatial resolution of the image under consideration; and the dependent variable was chosen to be the corresponding optimal SP. To assess the potential of the proposed approach, two regression models that yielded explicit equations (i.e. degree-2 polynomial (DP), and regression tree (RT)) were employed. In addition, as a sophisticated and versatile nonlinear model, support vector regression (SVR) was utilized to further measure the performances of DP and RT models compared with it. According to the comparisons, the DP model was selected as a representative of the proposed approach. In the end, to evaluate the proposed methodology, we also compared the results derived from the DP model with those derived from the Estimation of Scale Parameter (ESP) tool. Based on our experiments, not only did the DP model produce acceptable results, but also it outperformed ESP tool in this study for extracting individual urban buildings.     

Envy-Free Resource Allocation and Request Routing in Hybrid CDN–P2P Networks

Hybrid CDN–P2P networks (HCDNs) combine Peer to Peer (P2P) networks and Content Delivery Networks (CDN) to benefit from their complementary advantages. In order to enable a traditional CDN to offer hybrid content delivery service, we introduce a resource scheduling mechanism to perform the request routing process and determine the desired level of contribution for users. The proposed mechanism relies on a truthful, profit maximizing, envy-free auction to optimize contributions of the end-users in P2P content delivery. Based on the proposed solution, economics of content delivery in the HCDN are studied and it is shown that through our resource scheduling mechanism, it is possible to serve a big fraction of end-users with higher quality content, increase the net profit of the HCDN provider, and decrease expenditures of the content provider simultaneously.     

Performance analysis and calculation of critical inductance and output voltage ripple of a simple non‐isolated multi‐input bidirectional DC‐DC converter

In this paper, a simple non‐isolated multiple input (MI) bidirectional DC‐DC topology is proposed which can operate in buck, boost, or buck‐boost modes. The proposed topology utilizes a battery pack to realize the bidirectional power flow operation especially when the input sources are non‐storable ones. The excess energy of input sources can be stored in the battery and be injected to the load, when required. Simultaneous or independent power transfer of input sources is also provided. For better evaluation, the proposed topology has been compared with several recently presented novel topologies, from view point of number of inductors, capacitors, switches, and diodes. Comparison results show that the proposed topology utilizes less number of components (switches, inductors, capacitors, and current sensors) which can reduce the size, cost, and complexity of converter. Different operational modes of the proposed topology (unidirectional buck, boost, buck‐boost modes, and bidirectional mode) have been presented. Also, boost mode of the proposed topology has been investigated in detail, from design point of view, and generalized relationships have been proposed for calculation of critical inductance (CI) and output voltage ripple (OVR) of proposed n‐input boost topology. To validate proposed theoretical concepts, the proposed topology has been modeled and simulated in PSCAD/EMTDC software, and the 3‐input boost version has been experimentally implemented. Simulation and experimental results confirm appropriate performance of the proposed topology.