Upgrading of dissolution and anti-hypertensive effect of Carvedilol via two combined approaches: self-emulsification and liquisolid techniques

Objective: The aim of the study was to design a self-emulsifying drug delivery system (SEDDS) of the anti-hypertensive Carvedilol in liquid and liquisolid forms as a way to enhance its dissolution profile and anti-hypertensive effect.

Methods: Solubility studies of Carvedilol in various oils, surfactants and co-surfactants were conducted, followed by the construction of pseudo-ternary phase diagrams and other in vitro assessments. The selected SEDDS formulation (S1) was adsorbed onto solid powder excipients and compressed into tablets. The resulting liquisolid tablets were evaluated under British Pharmacopoeia (B.P.) specifications. Pre- and post-compression studies were performed to determine the flow properties and evaluate the liquisolid systems, followed by in vivo studies in hypertensive rats.

Results: Attempts of self-emulsification, droplet size, and thermodynamic stability studies showed acceptable results for the S1 formulation containing Capryol 90, Tween 20, and Transcutol HP (10:53.3:26.2%), respectively. Pre-compression studies showed adequate flowability and compatibility of liquid and solid excipients with Carvedilol. The selected liquisolid tablet (LS7) demonstrated the best disintegration and water absorption ratio in addition to satisfactory friability and hardness. A significantly (p?®. The in vivo study of LS7 formulation revealed a rapid significant (p?®.

Conclusion: Self-emulsifying liquisolid tablets expressed rapid onset of action with enhanced anti-hypertensive effect of Carvedilol.     

Propolis emulgel: a natural remedy for burn and wound

Objectives: To develop an alternative medicine, propolis, in emulgel formulation for burn and wound treatment.

Methods: The effect of two independent variables: emulsifying agent concentration and oil concentration on the cumulative amount of propolis release (dependent variable) from liquid paraffin and isopropyl palmitate emulgels was investigated. Eight formulations were prepared and evaluated for physical appearance, pH, propolis content, viscosity, and in vitro propolis release. The release results were fitted into different kinetic equations and analyzed using contour plot, interaction surface plot and one-way ANOVA. The selected emulgel formulation was investigated for its wound and burn healing activity in rats.

Results: All the prepared emulgels showed acceptable physical properties concerning color, homogeneity, consistency, and pH value. The concentration of emulsifying agent had more pronounced effect on propolis release than oil concentration. Formulations F1–F6 showed “anomalous” drug release, while Formulations F7 and F8 showed zero-order kinetic.

Conclusion: The formulation F7 with 5% isopropyl palmitate, 5% emulsifier mixture, 1% hydroxyl propyl methyl cellulose, 1% oleic acid, and 10% propylene glycol is a promising formula for better management of wound and burn.     

Integrating symbolic images into a multimedia database system using classification and abstraction approaches

Symbolic images are composed of a finite set of symbols that have a semantic meaning. Examples of symbolic images include maps (where the semantic meaning of the symbols is given in the legend), engineering drawings, and floor plans. Two approaches for supporting queries on symbolic-image databases that are based on image content are studied. The classification approach preprocesses all symbolic images and attaches a semantic classification and an associated certainty factor to each object that it finds in the image. The abstraction approach describes each object in the symbolic image by using a vector consisting of the values of some of its features (e.g., shape, genus, etc.). The approaches differ in the way in which responses to queries are computed. In the classification approach, images are retrieved on the basis of whether or not they contain objects that have the same classification as the objects in the query. On the other hand, in the abstraction approach, retrieval is on the basis of similarity of feature vector values of these objects. Methods of integrating these two approaches into a relational multimedia database management system so that symbolic images can be stored and retrieved based on their content are described. Schema definitions and indices that support query specifications involving spatial as well as contextual constraints are presented. Spatial constraints may be based on both locational information (e.g., distance) and relational information (e.g., north of). Different strategies for image retrieval for a number of typical queries using these approaches are described. Estimated costs are derived for these strategies. Results are reported of a comparative study of the two approaches in terms of image insertion time, storage space, retrieval accuracy, and retrieval time. Received June 12, 1998 / Accepted October 13, 1998     

An adaptive automated method for identity verification with performance guarantees

Businesses often rely on customers that access an automated resource to receive information, service or products. We present an adaptive method for automated authentication with performance guarantee of probabilistic error bounds, referred to as identity verification. A database includes a record of identifiers for each identity. The identifiers are partitioned into groups, where identifiers in the same group are correlated while identifiers in different groups are independent. A claimant requesting access into the system is probed with a sequence of identifiers. The response to an identifier can be a match, no-match, or ambiguous. Each identifier is characterized by prior response probabilities for a legitimate claimant and for an impostor. Impostor’s response probabilities are updated during a session. The method guarantees that the probabilities of accepting an impostor or rejecting a legitimate claimant do not exceed specified parameters. Once a given number of identifiers have been probed and the claimant has been neither accepted nor rejected, the session terminates with an inconclusive decision and the claimant is referred to further manual interrogation. The method computes various performance characteristics such as the probability that a session of a legitimate claimant terminates inconclusively. These characteristics are valuable in the design of an effective record of identifiers for each of the claimants.     

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.     

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.     

Cumulative voting consensus method for partitions with variable number of clusters

Over the past few years, there has been a renewed interest in the consensus clustering problem. Several new methods have been proposed for finding a consensus partition for a set of n data objects that optimally summarizes an ensemble. In this paper, we propose new consensus clustering algorithms with linear computational complexity in n. We consider clusterings generated with random number of clusters, which we describe by categorical random variables. We introduce the idea of cumulative voting as a solution for the problem of cluster label alignment, where, unlike the common one-to-one voting scheme, a probabilistic mapping is computed. We seek a first summary of the ensemble that minimizes the average squared distance between the mapped partitions and the optimal representation of the ensemble, where the selection criterion of the reference clustering is defined based on maximizing the information content as measured by the entropy. We describe cumulative vote weighting schemes and corresponding algorithms to compute an empirical probability distribution summarizing the ensemble. Given the arbitrary number of clusters of the input partitions, we formulate the problem of extracting the optimal consensus as that of finding a compressed summary of the estimated distribution that preserves maximum relevant information. An efficient solution is obtained using an agglomerative algorithm that minimizes the average generalized Jensen-Shannon divergence within the cluster. The empirical study demonstrates significant gains in accuracy and superior performance compared to several recent consensus clustering algorithms.     

A Report on CNSM 2010

The 6th International Conference on Network and Service Management (CNSM 2010) was held on October 25–29, 2010 in Niagara Falls, Canada. CNSM is a premier annual conference, sponsored by IEEE Communications Society and IFIP Working Group on Network and Distributed Systems Management, in the general area of network, systems, and service management. Built upon the success starting in 2005 to collocate six management workshops within the same week (MANWEEK), this year we debuted the single track conference CNSM ().     

Gesture behavior in unmedicated schizotypal adolescents.

Schizotypal personality disorder is characterized by interpersonal and verbal communication deficits. Despite the important role of gesture in social communication, no published reports examine the use of gesture by individuals with SPD. In this study, raters code gesture from videotaped interviews of unmedicated adolescents with SPD, other personality disorders, or no Axis II disorder. Results indicate that SPD adolescents show significantly fewer gestures but do not differ from the other groups in overall rate of movement. The findings are discussed in light of brain regions involved in dysfunction, parallels to schizophrenia, and treatment implications. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Designing tributary networks with multiple ring families

Advances in telecommunication technology result in improved service, but can also lead to difficult and challenging network design problems. For example, networks in which nodes are connected by rings of optical fiber can now be used to provide rapid service restoration in the event of a failure. However, as a result, network designers are faced with the new problem of designing networks based on topological ring structures. In this paper, we consider the particular case of tributary network design. In a tributary network, a group of nodes are connected to a hub node, which is used as a point of interconnection with other parts of the network. For a particular network architecture, we describe an algorithm to determine how many topological ring structures are required, and which nodes should be included on each. We highlight connections between this problem and problems in vehicle routing.A common architecture for a telecommunications network consists of several tributary (often called access) networks, which connect locations to hubs, and a backbone network, which interconnects the hubs. This paper describes a heuristic approach for designing tributary networks based on self-healing rings (SHRs). The tributary network consists of multiple ring families, and each of those is comprised of one or more SHRs, called “stacked” rings. The SHRs in a given ring family are routed over the same cycle of optical fiber cables, but each SHR serves only a subset of the locations along the cycle. Each demand location is assigned to a single SHR on one of the ring families, whereas the hub is assigned to all SHRs on all ring families. A link that is used by some ring family incurs a fixed cost plus a variable cost per SHR associated with that family. Each SHR is constrained by the demand volume it can handle and by the number of locations it can serve. This tributary ring network design problem can be viewed as a complex version of a vehicle routing problem with a single-depot andmultiple vehicles. Our algorithm is initiated with numerous ring families. It then attempts to merge these families, while ensuring that savings are realized in terms of the sum of fixed and variable costs.