An Estimation of Distribution Improved Particle Swarm Optimization Algorithm

PSO is a powerful evolutionary algorithm used for finding global solution to a multidimensional problem. Particles in PSO tend to re-explore already visited bad solution regions of search space because they do not learn as a whole. This is avoided by restricting particles into promising regions through probabilistic modeling of the archive of best solutions. This paper presents hybrids of estimation of distribution algorithm and two PSO variants. These algorithms are tested on benchmark functions having high dimensionalities. Results indicate that the methods strengthen the global optimization abilities of PSO and therefore, serve as attractive choices to determine solutions to optimization problems in areas including sensor networks

Computational intelligence methods in wireless sensor networks

Wireless sensor networks (WSNs) are networks of autonomous nodes that sense, compute and communicate in order to monitor an environment collectively. Ad hoc deployment, dynamic environment and resource constraints in nodes need to be considered while addressing WSN challenges such as deployment, localization, routing and scheduling. Adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments are desirable to address these challenges. The potential of computational intelligence ( CI) based approaches for addressing WSN challenges is investigated in this study. Contributions of this dissertation are in the following three areas: critical literature analysis, new architectures and approaches, and new solutions to WSN challenges. Challenges in WSNs are discussed, paradigms of CI are introduced and a comprehensive survey of CI-based WSN applications is conducted with an emphasis on pros, cons and suitability of CI methods for WSN applications. A discussion on multidimensional optimization in WSNs and a survey of the applications of particle swarm optimization (PSO) in WSNs are presented. An adaptive critic design (ACD) having a new combination of a PSO-based actor and a multilayer perceptron (MLP) critic is introduced for dynamic optimization. Its effectiveness is demonstrated through dynamic sleep scheduling of WSN nodes for wildlife monitoring. Compact generalized neuron (GN) is investigated as a resource-efficient alternative to MLPs for classification, nonlinear function approximation and time series prediction. A recurrent GN (RGN) structure is introduced. The performance of GN and RGN is shown to be comparable to that of MLPs having a larger number of trainable parameters. Autonomous deployment of sensor nodes from an unmanned aerial vehicle and distributed iterative node localization are investigated. These tasks are formulated as multidimensional optimization problems, and addressed through PSO and bacterial foraging algorithm. Lastly, an adaptive critic is developed using two GNs for dynamic sleep scheduling of WSN nodes. Its performance is compared with the results of the ACD having a PSO actor and an MLP critic --Abstract, page iv

Neural Network Based Secure Media Access Control Protocol for Wireless Sensor Networks

This paper discusses an application of a neural network in wireless sensor network security. It presents a multilayer perceptron (MLP) based media access control protocol (MAC) to secure a CSMA-based wireless sensor network against the denial-of-service attacks launched by adversaries. The MLP enhances the security of a WSN by constantly monitoring the parameters that exhibit unusual variations in case of an attack. The MLP shuts down the MAC layer and the physical layer of the sensor node when the suspicion factor, the output of the MLP, exceeds a preset threshold level. Backpropagation and particle swarm optimization algorithms are used for training the MLP. The MLP-guarded secure WSN is implemented using the Vanderbilt Prowler simulator. Simulation results show that the MLP helps in extending the lifetime of the WSN

Microbial biotechnology alchemy: Transforming bacterial cellulose into sensing disease- A review

Biosensors have the potential to revolutionize healthcare by providing rapid and accurate diagnosis of diseases. Biosensors are analytical devices that convert molecular recognition of a target analyte into a measurable signal. Older diagnostic techniques, such as immunoaffinity column assays, fluorometric, and enzyme-linked immunosorbent assays, are laborious, require qualified personnel, and can be time consuming. In contrast, biosensors offer improved accuracy, sustainability, and rapidness due to their ability to detect specific biomarkers with high sensitivity and specificity. The review covers various bacterial cellulose (BC)-based biosensors, from SARS-CoV-2 detection to wearable health monitoring and interaction with human-computer interfaces. BC's integration into ionic thermoelectric hydrogels for wearable health monitoring shows its potential for real-time health tracking. Incorporating BC in biosensors for low-noise electrodes, and wearable sensors has been elaborated. The invention of a phage-immobilized BC biosensor for S. aureus detection is a significant contribution to the field, highlighting the biosafety and efficiency of BC in pathogen identification and demonstrating BC's versatility across multiple sensing platforms. Palladium nanoparticle-bacterial cellulose hybrid nanofibers show excellent electrocatalytic activity for dopamine detection, whereas Au-BC nanocomposite biosensors show efficacy in glucose detection, with potential therapeutic applications. The “lab-on-nanopaper” device, utilizing BC nanopaper, not only visually detects human serum albumin but also establishes itself as a new-generation optical biosensing platform with superiority over conventional substrates. This review contributes to the ongoing advancements in biosensor technology, highlighting the potential of BC as a versatile material for developing innovative biosensors. This is crucial for improving the accuracy, sensitivity, and efficiency of diagnostic tools in healthcare

Interpenetrating network hydrogel beads of carboxymethylcellulose and egg albumin for controlled release of lipid lowering drug

Novel interpenetrating network hydrogel beads of sodium carboxymethylcellulose and egg albumin loaded with a lipid lowering drug, simvastatin, were prepared by ionotropic gelation and covalent cross-linking method. The IPN beads were characterized by differential scanning colorimetric analysis, X-ray diffractometry to understand the crystalline nature of the drug after entrapment into IPN matrix. Fourier transform infrared spectroscopy was used to find the chemical stability of drug in the polymer matrix and scanning electron microscopy was performed to study the surface morphology. The ionically cross-linked beads were capable of releasing drug up to 7 h, whereas the drug release was extended up to 12 h in case of dual cross-linked beads. The beads which were prepared with higher concentration of glutaraldehyde released the drug more slowly. The release data were fitted to an empirical equation to determine the transport mechanism, which indicated the non-Fickian trend for drug transport. © 2010 Informa UK Ltd. All rights reserved

Ternary structured magnesium cobalt oxide/graphene/polycarbazole nanohybrids for high performance electrochemical supercapacitors

In the present work, polycarbazole (PCz)/magnesium cobalt oxide (MgCo2O4)/reduced graphene oxide (RGO) based ternary nanocomposite was prepared through in-situ polymerization, and utilized it as an active electrodes for electrochemical energy storage supercapacitor applications. The electrochemical behaviour of PCz and its nanocomposites were investigated by measuring specific capacitance using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and Galvanostatic charge–discharge (GCD) analysis. The PCz/MgCo2O4/RGO hybrids exhibited higher capacitance (548.54 F/g) than that of PCz (117.65 F/g) and PCz/MgCo2O4 (482.92 F/g) at the scan rate of 50 mV/s, as determined by CV method. The enhanced supercapacitance indicates high power and energy storage capabilities of the ternary metal oxide-graphene based polycarbazole nanocomposites. Electrochemical impedance spectroscopy confirmed low solution resistance of PCz/MgCo2O4/RGO. Thermogravimetric analysis affirmed the increased thermal stability of PCz/MgCo2O4/RGO composite compared to that of pure polycarbazole and PCz/MgCo2O4 nanocomposite. The scanning electron micrographs of nanocomposite confirmed the successful incorporation of nanofillers into the PCz matrix. On the basis of the research findings, PCz/MgCo2O4/RGO can be expected to be a promising electrode active material for high performance energy storage supercapacitors

An unusual presentation of mixed tumor of salivary gland

Pleomorphic adenoma is one of the salivary gland tumors affecting both major and minor salivary glands. Parotid gland is the most commonly affected of the major group, and hard palate is the most common site of the minor salivary glands affected. Pleomorphic adenoma is a benign mixed tumor of the salivary glands that has elements of both epithelial and mesenchymal tissues. We report a case of pleomorphic adenoma of minor salivary glands in the soft palate. Incisional biopsy revealed features of pleomorphic adenoma