In vitro evaluation and in vivo performance of lyophilized gliclazide

Enhancement of the dissolution rate of the poorly water-soluble hypoglycemic agent, gliclazide, by the aid of lyophilization was investigated. Mannitol, sodium lauryl sulfate (SLS) and polyvinyl pyrrolidone (PVP-k-30) were employed in different weight ratios (43%, 56% and 64% w/w, respectively) as water-soluble excipients in the formulation. Lyophilized systems were found to exhibit extremely higher in vitro dissolution rate compared to the unprocessed drug powder. Solid state characterization of the lyophilized systems using X-ray powder diffraction, Fourier transform infrared spectroscopy and differential scanning calorimetry techniques revealed that dissolution enhancement was attributable to transformation of gliclazide from the crystalline to an amorphous state in the solid dispersion formed during the lyophilization process. The gastrointestinal absorption and hypoglycemic effect of the lyophilized gliclazide/SLS system were investigated following oral administration to Albino rabbits. C_max and area under the plasma concentration–time curve of gliclazide (AUC_0–12) after administration of the lyophilized formulations were significantly higher than those obtained after administration of the unprocessed gliclazide.     

Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

Genetic algorithm-based initial contour optimization for skin lesion border detection

Multimedia Tools and Applications - Automated segmentation has an essential role in detecting several diseases, such as skin lesions. In segmentation, the active contour (AC) is an efficient method...     

Optimization of dynamic response using a monolithic-time formulation

The design of systems for dynamic response may involve constraints that need to be satisfied over an entire time interval or objective functions evaluated over the interval. Efficiently performing the constrained optimization is challenging, since the typical response is implicitly linked to the design variables through a numerical integration of the governing differential equations. Evaluating constraints is costly, as is the determination of sensitivities to variations in the design variables. In this paper, we investigate the application of a temporal spectral element method to the optimization of transient and time-periodic responses of fundamental engineering systems. Through the spectral discretization, the response is computed globally, thereby enabling a more explicit connection between the response and design variables and facilitating the efficient computation of response sensitivities. Furthermore, the response is captured in a higher order manner to increase analysis accuracy. Two applications of the coupling of dynamic response optimization with the temporal spectral element method are demonstrated. The first application, a one-degree-of-freedom, linear, impact absorber, is selected from the auto industry, and tests the ability of the method to treat transient constraints over a large-time interval. The second application, a related mass-spring-damper system, shows how the method can be used to obtain work and amplitude optimal time-periodic control force subject to constraints over a periodic time interval. This research was performed while the first author held a National Research Council Research Associateship Award at the Air Force Research Laboratory. An early version of this paper was presented at the 46th AIAA Aerospace Sciences Meeting and Exhibit, Jan 7–10, 2008, Reno, Nevada.     

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Small/medium nuclear reactors for potential desalination applications : Mini review

Small/medium nuclear reactors (SMRs) are a promising alternative for powering large scale desalination plants. The modern generations of these systems manifest cost effectiveness and built-in safety features. The compatibility with geological and topological challenges is an added advantage. Moreover, funding opportunities and packages could be easily arranged for small/medium nuclear reactors (SMR). This mini review article provides the latest technical features of SMR nuclear plants with emphasis on pressurized light water reactors (PWR), boiling water reactors (BWR), heavy water reactors (HWR), gas cooled reactors (GCR), and liquid metal fast breeder reactors (LMFBR). Preliminary cost indicators for typical units were investigated as a part of joint effort to develop a cost database for these types of reactors. Security and safety features of small/medium reactors are identified and reviewed. This paper identifies and briefly discusses the various types of small/medium nuclear reactors to provide a preliminary evaluation and consideration of using this type of reactor in potential seawater desalination applications.     

Preparation of dendritic adamantane‐based polymers/layered silicate nanocomposites

An adamantane‐based atom transfer radical initiator (Adm‐Br) was prepared by the treatment of 1‐[[N‐[2‐Hydroxy‐l,l‐bis(hydroxymethyl)ethyl]amino]carbonyl]adamantane with bromopropionyl bromide. The resulting initiator was subsequently used in the atom transfer radical polymerization (ATRP) of methyl methacrylate (MMA) to form (Adm‐p‐MMA), which was successfully used, as a macroinitiator, in further ATRP reactions with 3‐O‐methacryloyl‐1,2 5,6‐di‐O‐isopropylidene‐α‐d ‐glucofuranose (gly), a glycomonomer, to afford the Adm‐p‐MMA‐b‐gly polymer. The new Adm‐p‐MMA‐b‐gly polymer subsequently was employed to form a nanocomposite with chitosan‐modified, Nanofil clay (NC). The resulting Adm‐p‐MMA‐b‐gly/NC composite material was progressively hydrolyzed to regenerate the OH groups of the glucose units within the Adm‐p‐MMA‐b‐gly copolymer. The polymer/NC nanocomposites were characterized by X‐ray diffraction, thermal gravimetric analysis, differential scanning calorimetry, and transmission electron microscopy. POLYM. ENG. SCI., 54:2669–2675, 2014. © 2013 Society of Plastics Engineers     

Exploring the fitness landscape and the run-time behaviour of an iterated local search algorithm for cost-based abduction

Cost-based abduction (CBA) is an important problem in reasoning under uncertainty, and can be considered a generalization of belief revision. CBA is known to be NP-hard and has been a subject of considerable research over the past decade. In this paper, we investigate the fitness landscape for CBA, by looking at fitness–distance correlation for local minima and at landscape ruggedness. Our results indicate that stochastic local search techniques would be promising on this problem. We go on to present an iterated local search algorithm based on hill-climbing, tabu search, and simulated annealing. We compare the performance of our algorithm to simulated annealing, and to Santos' integer linear programming method for CBA.