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.     

Synthesis of heterocyclic metal complexes and their evaluation as marine antifoulants

4-Amino-3-thio-1,24-triazolidine (L₁) and 4-amino-5-thio-1,3,4-thiadiazole (L_II) as well as their metal complexes of the general formulae ML_I·2H₂O and ML_IICl (M; Pb(II), Cd(II) and Zn(II)) were prepared. With Sn(II), we obtained Sn(L_I)₂·2H₂O and SnL_IICl, respectively. The structures of the compounds were identified through elemental analysis, and IR and UV spectra measurements, in addition to thermal analysis in case of the metal complexes. The antifouling properties of the compounds were tested by their incorporation into paint formulations which were applied to PVC substrates and tested in water from Alexandria western harbour. When the compounds were added at 17·5% by volume, the coated panels were fouled after 3 months of immersion. The addition of 6·7% by volume of tributyltin oxide to 15·8% of the prepared compounds in one formulation elevated the paint efficiency and prevented fouling for 11 months. Paint containing solely tributyltin oxide at the same concentration was inactive against algae.     

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...     

Towards an Online Continuous Adaptation Mechanism (OCAM) for Enhanced Engagement: An EEG Study

ABSTRACT

Individual preferences for learning environments can be linked to a specific behavior. The tendency of such behavior can somehow be associated with an individual’s ability to cognitively engage in the learning process without being distracted by other stimuli. An online continuous adaptation mechanism (OCAM) of learning contents was developed in order to regulate the presentation of learning contents based on changes in the learner’s aptitude level. This was claimed to stimulate a better cognitive and emotional response among learners, thus stimulating their engagement. A total of 41 students (36 male and 5 female; age 20–25 years) participated in this study. The results revealed that learners’ levels of concentration and cognitive load were positively influenced by the OCAM, which significantly increased their engagement. Our findings can be used to inform designers and developers of online learning systems about the importance of regulating the presentation of learning contents according to the aptitude level of individual learners. The proposed OCAM can improve learners’ ability to process specific information meaningfully and make the inferences necessary for understanding the learning content.     

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.     

Synthesis and evaluation of 99m Tc-NIDA and 99m Tc-DMIDA complexes for hepatobiliary imaging

1-Naphthylcarbamoylmethyliminodiacetic acid (NIDA) and diphenylmethylcarbamoylmethyliminodiacetic acid (DMIDA) were synthesized, characterized, and labeled with ^99m Tc using SnCl₂ as reducing agent. The parameters affecting the yield of ^99m Tc-NIDA and ^99m Tc-DMIDA were studied in detail. The optimum conditions ensuring high yield of ^99m Tc-NIDA (94.2 ± 2%) and ^99m Tc-DMIDA (93.1 ± 2%) are as follows: 30 mg of NIDA or DMIDA, 0.3 mg of SnCl₂·H₂O, pH 6, 15 min. The biodistribution in mice injected with ^99m Tc-NIDA and ^99m Tc-DMIDA showed high liver uptake at 10 min post injection, with fast biliary excretion. Accumulation of the activity in kidneys was negligible, especially after a long time post injection. Both ^99m Tc-NIDA and ^99m Tc-DMIDA can be applied as hepatobiliary imaging agents for the evaluation of the functional status of the hepatocytes and the patency of the biliary duct.     

Subspace-based technique for speech encryption

The use of signal processing techniques in cryptographic field is an attractive approach in recent years. As an example, the intractability of the under-determined blind source separation (BSS) problem has been used for the proposal of BSS-based speech encryption. However, some weaknesses of this proposal from a cryptographic point of view have been recently published. In this paper, we propose a new encryption method that bypass these weaknesses. The proposed approach is based on the subspace concept together with the use of nonlinear functions and key signals. An interesting feature of the proposed technique is that only a part of the secret key parameters used during encryption is necessary for decryption. Furthermore, if no plain-text is fed to the encryption algorithm, the latter will provide no contents. Analysis results show that the proposed method significantly enhances the security level of existing BSS-based speech encryption methods.