Pulmonary hypertension. Response of vasoactive peptides to a nonionic contrast medium in patients undergoing pulmonary angiography

A 46-years-old male was admitted to our hospital because of productive cough and infiltrates on the chest roentogenogram. The patient had a history of left upper bullectomy ten years prior to the admission. The CT scan of the chest on admission showed infiltrats with cavitation in the left apex and multiple bullae in almost whole lung. Microscopical examination of smears of sputum and bronchoalveolar lavage fluid revealed acid-fast bacilli. They were identified as Mycobacterium szulgai by DNA-DNA hybridization method. The patient was treated with isoniazid, streptomycin and rifampicin. After treatment for about a month, the culture of sputum converted to negative for M. szulgai. After about three months hospitalization, the infiltrates decreased and the cavity wall became thin, and no recurrence sign has been observed after the discharge. There are a few case reports of pulmonary infection due to M. szulgai associated with bullous disease of the lung in Japan.     

Broadband-CDMA overlay

A Broadband Code Division Multiple Access (B-CDMA) technique is presented that will overlay the existing cellular telephone spectrum (825 to 894MHz). The overlay will provide additional capacity to the network while allowing high quality voice and high speed data services to be coexistent with the existing cellular services (AMPS and TDMA). The advantages of using B-CDMA in a fading environment are analyzed. The low level of mutual interference between the existing cellular telephone system and the B-CDMA overlay system is shown.Portions of this paper have been submitted as partial fulfillment of the requirements of the Ph.D. dissertation in electrical engineering at the City College of New York.     

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Cognitive radio wireless sensor networks (CRWSN) can be defined as a promising technology for developing bandwidth-limited applications. CRWSN is widely utilized by future Internet of Things (IoT) applications. Since a promising technology, Cognitive Radio (CR) can be modelled to alleviate the spectrum scarcity issue. Generally, CRWSN has cognitive radio-enabled sensor nodes (SNs), which are energy limited. Hierarchical cluster-related techniques for overall network management can be suitable for the scalability and stability of the network. This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering (MDMO-EAC) Scheme for CRWSN. The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN. Besides, the MDMO-EAC algorithm is based on the dwarf mongoose optimization (DMO) algorithm design with oppositional-based learning (OBL) concept for the clustering process, showing the novelty of the work. In addition, the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency. The presented model is validated using a comprehensive range of experiments, and the outcomes were scrutinized in varying measures. The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.     

A novel approach to accelerate the convergence speed of a stochastic multi-agent system using recurrent neural nets

One problem in the design of multi-agent systems is the difficulty of predicting the occurrences that one agent might face, also to recognize and to predict their optimum behavior in these situations. Therefore, one of the most important characteristic of the agent is their ability during adoption, to learn, and correct their behavior. With consideration of the continuously changing environment, the back and forth learning of the agents, the inability to see the agent’s action first hand, and their chosen strategies, learning in a multi-agent environment can be very complex. On the one hand, with recognition to the current learning models that are used in deterministic environment that behaves linearly, which contain weaknesses; therefore, the current learning models are unproductive in complex environments that the actions of agents are stochastic. Therefore, it is necessary for the creation of learning models that are effective in stochastic environments. Purpose of this research is the creation of such a learning model. For this reason, the Hopfield and Boltzmann learning algorithms are used. In order to demonstrate the performance of their algorithms, first, an unlearned multi-agent model is created. During the interactions of the agents, they try to increase their knowledge to reach a specific value. The predicated index is the number of changed states needed to reach the convergence. Then, the learned multi-agent model is created with the Hopfield learning algorithm, and in the end, the learned multi-agent model is created with the Boltzmann learning algorithm. After analyzing the obtained figures, a conclusion can be made that when learning impose to multi-agent environment the average number of changed states needed to reach the convergence decreased and the use of Boltzmann learning algorithm decreased the average number of changed states even further in comparison with Hopfield learning algorithm due to the increase in the number of choices in each situation. Therefore, it is possible to say that the multi-agent systems behave stochastically, the more closer they behave to their true character, the speed of reaching the global solution increases.     

An efficient genetic algorithm for solving the quay crane scheduling problem

This paper addresses the quay crane scheduling problem (QCSP), which has been shown to be NP-complete. For this reason, a number of studies have proposed the use of genetic algorithm (GA) as the means to obtain the solution in reasonable time. This study extends the research in this area by utilizing the GA that is available in the latest version of Global Optimization Toolbox in MATLAB 7.13 to facilitate development. It aims to improve the efficiency of the GA search by (1) using an initial solution based on the S-LOAD rule developed by Sammarra, Cordeau, Laporte, and Monaco (2007), (2) using a new approach for defining the chromosomes (i.e., solution representation) to reduce the number of decision variables, and (3) using new procedures for calculating tighter lower and upper bounds for the decision variables. The effectiveness of the developed GA is tested using several benchmark instances proposed by Meisel and Bierwirth (2011). Compared to the current best-known solutions, experimental results show that the proposed GA is capable of finding the optimal or near-optimal solution in significantly shorter time for larger problems.     

New techniques for efficiently k-NN algorithm for brain tumor detection

Multimedia Tools and Applications - The k-NN algorithm missing values is one of the current research issues, especially in 4D frequency. This study addresses the accuracy of the images, increases...