Controlled release of highly water-soluble antidepressant from hybrid copolymer poly vinyl alcohol hydrogels

This paper reports the development of hybrid copolymer poly vinyl alcohol hydrogel (H-PVA) based on acrylic acid (AA), 2-acrylamido-2-methyl propane sulfonic acid (AMPSA) and ethylene glycol dimethacrylate (EGDMA). The H-PVA hydrogels were used for controlled release study of highly water-soluble antidepressant drug (venlafaxine). The formation of new structure was confirmed by Fourier transform infrared spectroscopy. The morphology of dry hydrogel slabs was examined by scanning electron microscopy. Thermal gravimetric analysis of hydrogels showed that there was an increase in thermal stability of H-PVA hydrogels. Swelling experiments performed in acidic and basic media provided important information on drug release properties of hydrogels. The release of venlafaxine was significantly retarded in pH 1.2, while rapid release was observed in pH 7.4. The release rate of venlafaxine increased with increasing ratio of AA and AMPSA. However, extent of drug release was significantly reduced on increasing ratio of EGDMA. Mechanism of release has been analyzed using various mathematical models. H-PVA hydrogel slabs were able to sustain the release of venlafaxine more than 20 h. The results of the study suggest that optimized H-PVA hydrogels could serve as suitable controlled release matrices.     

Biopolymer blended films of poly(butylene succinate)/cyclic olefin copolymer with enhanced mechanical strength for packaging applications

The demand for biodegradable materials is on the rise because humanity is now more concerned about a sustainable lifestyle than ever before. In this regard, we present solution casting synthesized novel biopolymer blended films of poly(butylene succinate)/cyclic olefin copolymer (PBS/COC) for packaging applications. These films were characterized by X-ray diffraction (XRD), Fourier transform infrared microscopy (FTIR), scanning electron microscopy (SEM), universal tensile testing (ASTM D882 standard), and antibacterial Disc diffusion tests using gram-negative Escherichia coli (E.coli) and gram-positive Staphylococcus aureus (S.aureus) bacteria. The XRD and FTIR revealed the type of bonding to be physical in-between the constituent polymers; ensuring the biodegradable nature of their blends, while the thickness of films was found to be <100 μm. The SEM, tensile, and antibacterial testing concluded that 30%PBS with 70%COC by weight blending is the best composition; showing a compact/pin-holes free morphology, the highest strength of 91 MPa, and contact inhibition with E.coli and S.aureus bacteria.     

Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.     

Effect of microvoid formation on the tensile properties of dual-phase steel

A steel containing 0.32 wt.% C, 0.88 wt.% Mn, 0.99 wt.% Si, 0.9 wt.% Ni, and 0.9 wt.% Cr was intercritically annealed at different temperatures from 775 to 870 °C and quenched in oil to produce dual-phase steel microstructure. Tensile testing of these samples gave a series of strengths and ductilities. The tensile strength increased with the increased annealing temperatures and the martensite percentage increased with a reduction in ductility. Microvoids were formed near the fracture surfaces. The morphology of the microvoids changed with the martensite percentage from decohesion of the martensite particles to the intergranular and transgranular cracks, which defined the ultimate fracture mode of the specimens. The change in the morphology of microvoids may be due to a high percentage of carbon in the steel, which produced stresses in the matrix (ferrite) during phase transformation.     

Enhanced sintering, microstructure evolution and mechanical properties of 316L stainless steel with MoSi2 addition

Sintering 316L stainless steel to near full density with an appropriate sintering additive can ensure high mechanical properties and corrosion resistance. We present here a sintering approach which exploits the dissociation of ceramics in steels at high temperatures to activate sintering densification to achieve near full dense 316L stainless steel materials. MoSi₂ ceramic powder was used as a sintering additive for pre-alloyed 316L stainless steel powder. Sintering behavior and microstructure evolution were investigated at various sintering temperatures and content of MoSi₂ as sintering additive. The results showed that the sintering densification was enhanced with temperature and MoSi₂ content. The distribution of MoSi₂ was characterized by XMAPs. It was found that MoSi₂ dissociated during sintering and Mo and Si segregated at the grain boundaries. Excess Mo and Si were appeared as separate phases in the microstructure. Above 98% of theoretical density was achieved when the specimens were sintered at 1300 °C for 60 min with 5 wt.% MoSi₂ content. The stainless steel sintered with 5 wt.% MoSi₂ exhibited very attractive mechanical properties.     

Formulation,characterisation and sensory evaluation of Bio-Cal instant jelly fortified with chicken bone-based nano-biogenic hydroxyapatite

This study was aimed to develop Bio-Cal instant jelly enriched with nano-biogenic hydroxyapatite (HAp) that was extracted from chicken bone bio-waste. For HAp preparation, collected sample was proceeded for washing, drying, grinding, sieving, sintering (750°C) and ultra-sonication. Resultant HAp was characterised by spectroscopy techniques. The Fourier transform infrared spectroscopy (FTIR) peak of PO₄³- was visible at 1019.76 cm⁻¹ in nano-biogenic HAp that corresponds to existence of calcium salt. Particle size ranged from 100 to 800 nm, whereas calcium content was found 26.9 ± 0.09 ppm by Atomic absorption spectroscopy (AAS). Scanning electron microscopy (SEM) confirmed irregular agglomerated nano-sized crystals while X-Ray diffraction (XRD) presented crystalline behaviour at 2θ with intensity 213 a.u. The Ca/P ratio based on Laser-induced breakdown spectroscopy (LIBS) analysis was 1.82. HAp was further used to develop Bio-Cal instant jelly with four different formulations; 0%, 0.33%, 0.66% and 1%. Texture profile analysis (TPA) was done to determine cohesiveness, adhesiveness and elasticity of jelly, whereas viscosity ranged from 0.68 ± 0.01 to 0.89 ± 0.01 mm. pH of all formulations lied within the range of 5.52 ± 0.04 to 5.57 ± 0.01. The best acceptability of jelly was seen in ‘nano-biogenic HAp-A₁’ formulation with mean 7.87 ± 0.35 that may play significant role in minimising calcium risk among end-users.     

A Robust Least Mean Square Algorithm for Adaptive Array Signal Processing

Least Mean Square (LMS) has been the most popular scheme in the realization of adaptive beamforming algorithms. In this paper a Robust Least Mean Square (R-LMS) algorithm is proposed which uses ratio parameters to control the contribution of product vectors in the weight upgrading process. The idea behind the proposed scheme is inclusion of previous information in place of relying solely on current sample. The performance enhancement by R-LMS algorithm is achieved with insignificant increase in computational complexity of LMS algorithm, so the crux of the conventional technique is not lost. Simulation results are also presented which illustrate that R-LMS provides relatively fast convergence, less Brownian motion and improved stability.     

A novel algorithm for the computation of systems containing different types of integral and integro-differential equations

An easy and efficient technique is applied to get a reliable analytic approximate solution of linear and nonlinear integral and integro-differential equations arising in the phenomena of everyday life. The proposed technique consists of a series only in which the unknown constants are determined in the usual way. The obtained results by this technique are in good agreement with the exact solution and it is proved that this technique is effective and easy to apply. Some problems are solved to prove the above claims and also the results are compared with exact solutions as well as with the results obtained by already existing different techniques.     

Effects of nutrition strategy on the levels of nutrients and bioactive compounds in blackberries

The effects of nutrition strategy on levels of nutrients and bioactive compounds in fruit and leaves of blackberries were studied in greenhouse-grown blackberry plants fertilised with combinations of two levels (low, high) of nitrogen (60 and 100 kg ha⁻¹, respectively) and potassium (66.4 and 104 kg ha⁻¹, respectively). Plant concentrations of organic phytochemicals were quantitatively analysed by high-performance liquid chromatography. High amounts of both fertilisers produced high amounts of all nutrients and bioactive compounds analysed in fruit except total acidity and ellagic acid. There were major differences in compounds affecting taste in fruit, e.g., sugars (fructose and glucose), total soluble solids and pH, and also in anthocyanin content. The concentrations of secondary metabolites, vitamin C and ellagic acid in fruit also varied significantly between treatments, although the differences were smaller. Storage of blackberries showed variable effects in the different levels of compounds, and the changes found were small. Nutrient regime did not affect blackberry leaves to the same extent, and only minor changes were found. The findings show that by optimising plant nutrition, phytonutrient levels can be maximised and maintained in fresh and stored berry crops, especially those grown in greenhouses, where conditions can easily be regulated.