Removal of methylene blue and safranin orange pollutants from liquid effluents by soy residue

Methylene blue and safranin orange dyes, which are used in the textile and pharmaceutical industries, can severely damage the environment and human health. This study investigated the use of okara residue as an alternative biosorbent for the removal of methylene blue and safranin orange dyes. Substantial amounts of okara residue are generated daily during the processing of soy milk in the agro-industrial sector. Dye adsorption was not affected by pH. An adsorption study identified the optimal experimental conditions as: 298 K, 0.03 g of adsorbent in 30 mL of dye solution at a concentration of 50 mg L⁻¹, and a contact time of 300 min for methylene blue dye, and 298 K, 0.02 g of adsorbent in 30 mL of dye solution at a concentration of 50 mg L⁻¹, and a contact time of 200 min for safranin orange dye; the maximum adsorption capacities were 93.201 ± 0.01 and 184 592 ± 0.02 mg g⁻¹, respectively. Okara has considerable advantages over other natural materials as an alternative for the treatment of industrial effluents. Because it is easily obtained and does not require any physicochemical treatment, adsorption does not require any specific operation temperature. In addition, okara exhibited a high adsorption capacity compared to other natural materials that require chemical and physical processing for adsorbent preparation.     

Toward the validation of VR-HMDs for medical education: a systematic literature review

Virtual Reality - The latest technological advancements in the domain of virtual reality (VR) have created new opportunities to use VR as a training platform for medical students and practitioners...     

Engaging the human operator: a review of the theoretical support for the vigilance decrement and a discussion of practical applications

Abstract

Vigilance, or the ability to sustain attention for extended periods of time, has been of interest to the human factors and ergonomics community for 70 years. During this time, several theories have attempted to account for the performance effects commonly associated with vigilance (i.e. the decrement, which manifests in fewer correct detections and longer response times to targets over time). We provide a cohesive review of the current empirical support for the three major theories explaining the vigilance decrement and describe the limitations of each theory herein. The three overarching theories of vigilance performance and the vigilance decrement include: cognitive resource theory, mindlessness theory and mind-wandering theory. Importantly, each of these theories relies on unique definitions of task engagement to understand operator overload or underload. The differences in the operationalisation of task engagement are problematic in the application of vigilance research to the real world. In this article, we describe the utility of a unified definition of task engagement and demonstrate how resource theory is better poised to account for cognitive task engagement than physical task engagement, which is emphasised in other theories.     

Natural graphite reinforced fluoroelastomer composites: Morphological,mechanical, thermal,dielectric, and solvent transport studies

Fluoroelastomer (FKM) composite embedded with natural graphite (NG) was prepared and its thermal, mechanical, dielectric, and solvent transport properties were explored as a function of NG loading. The morphology of the composites was done by AFM, SEM, and TEM analysis, and XRD provides the structural analysis. The composite with 20 phr of NG content showed the highest tensile properties and thermal stability and was supported by fracture surface SEM analysis. Enhancement in polymer-filler interaction in the composite was further confirmed by the increase in T_g value obtained from DSC analysis. The dielectric permittivity value showed tremendous increment by 30 phr of NG addition with decreased dielectric loss. The resistance to solvent uptake with increase in NG loading by the formation of tortuous pathway is also a clear evidence for the exfoliation of the graphite flakes in the FKM matrix. Better inclusion of NG in FKM matrix efficiently enhanced the thermal, mechanical, dielectric and the transport properties of the composites.     

Comparison of bioactive peptides prepared from sheep cheese whey using a food‐grade bacterial and a fungal protease preparation

Novel bacterial (HT) and fungal (FPII) food‐grade protease preparations were evaluated for their ability to hydrolyse sheep cheese whey (SCW) and the generation of bioactive peptides. Both protease preparations hydrolysed the whey proteins to small peptides over 24‐h hydrolysis time, but the time course hydrolysis profiles were different as evaluated by SDS‐PAGE. The HT whey hydrolysate had considerably higher antioxidant and angiotensin‐I converting enzyme (ACE)‐inhibitor activity than the FPII hydrolysate. Neither hydrolysate was cytotoxic towards Vero cells. OFFGEL electrophoresis of the small peptide pool fraction (<15 amino acids) of each hydrolysate indicated differences in the pI distribution of the bioactive peptides. This likely reflects the diverse hydrolytic specificity of the proteases. Although the antioxidant activity of both hydrolysates was not significantly affected by simulated gastrointestinal digestion, the loss of ACE‐inhibitor activity was greater with the FPII hydrolysate.     

Maize production and nitrous oxide emissions from enhanced efficiency nitrogen fertilizers

Nutrient Cycling in Agroecosystems - Enhanced efficiency nitrogen fertilisers (EENFs) attempt to improve nitrogen use efficiency (NUE) by synchronizing nitrogen (N) supply with crop demand to...     

Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering

Diabetic Retinopathy (DR) is a vision disease due to the long-term prevalence of Diabetes Mellitus. It affects the retina of the eye and causes severe damage to the vision. If not treated on time it may lead to permanent vision loss in diabetic patients. Today’s development in science has no medication to cure Diabetic Retinopathy. However, if diagnosed at an early stage it can be controlled and permanent vision loss can be avoided. Compared to the diabetic population, experts to diagnose Diabetic Retinopathy are very less in particular to local areas. Hence an automatic computer-aided diagnosis for DR detection is necessary. In this paper, we propose an unsupervised clustering technique to automatically cluster the DR into one of its five development stages. The deep learning based unsupervised clustering is made to improve itself with the help of fuzzy rough c-means clustering where cluster centers are updated by fuzzy rough c-means clustering algorithm during the forward pass and the deep learning model representations are updated by Stochastic Gradient Descent during the backward pass of training. The proposed method was implemented using python and the results were taken on DGX server with Tesla V100 GPU cards. An experimental result on the publically available Kaggle dataset shows an overall accuracy of 88.7%. The proposed model improves the accuracy of DR diagnosis compared to the existing unsupervised algorithms like k-means, FCM, auto-encoder, and FRCM with alexnet.

     

The road code: encouraging more efficient driving practices in New Zealand

Road transport contributes a significant amount towards New Zealand’s carbon emissions, mostly from light vehicles. These emissions could be partly reduced by an increase in more efficient driving practices, and reductions of 10–20% of fuel are possible without increasing trip times significantly. This study was conducted to understand whether people knew how to drive efficiently, whether they actually ever drove in an efficient manner and what ways there could be to influence people to drive more efficiently. Focus groups were conducted across New Zealand in urban and rural areas with groups of students, young professionals, parents and older people in order to cover different lifestyles and environments. These focus groups covered a wide range of topics including knowledge and practices of efficient driving, learning to drive, infrastructure and aspirations. Our results show that most people reported knowing the things they could do to be more fuel-efficient, however, despite this knowledge, they very rarely engaged in these practices. When they did consider fuel efficiency, it was almost always linked to saving fuel costs and environmental aspects were not considered. There is a clear lack of connection between carbon emissions and driving when people are in their cars. Better messages could be presented to drivers linking their driving practices to carbon emissions and therefore climate change. There are a range of other options where more efficient practices and choices could be encouraged depending on context, the driver and their way of life.