Magnetized inulin by Fe3O4 as a bio-nano adsorbent for treating water contaminated with methyl orange and crystal violet dyes

Current work focuses on fabricating a new bio-nano adsorbent of Fe3O4@inulin nanocomposite via an in-situ co-precipitation procedure to adsorb methyl orange (MO) and crystal violet (CV) dyes from aqueous solutions. Different physical characterization analyses verified the successful fabrication of the magnetic nanocomposite. The adsorbent performance in dye removal was evaluated by varying initial dye concentration, adsorbent dosage, pH and temperature in 5110 mg/L, 0.10.8 g/L, 111 and 283 – 338 K, respectively. Due to the pH of zero point of charge and intrinsic properties of dyes, the optimum pHs were 5 and 7 for MO and CV adsorption, respectively. The correlation of coefficient (R2) and reduced chi-squared value were the criteria in order to select the best isotherm and kinetics models. The Langmuir model illustrated a better fit for the adsorption data for both dyes, demonstrating the maximum adsorption capacity of 276.26 and 223.57 mg/g at 338 K for MO and CV, respectively. As well, the pseudo-second-order model showed a better fitness for kinetics data compared to the pseudo-first-order and Elovich models. The thermodynamic parameters exhibited that the dye adsorption process is endothermic and spontaneous, which supported the enhanced adsorption rate by increasing temperature. Moreover, the nanocomposite presented outstanding capacity and stability after 6 successive cycles by retaining more than 87% of its initial dye removal efficiency. Overall, the magnetized inulin with Fe3O4 could be a competent adsorbent for eliminating anionic and cationic dyes from water

Stormwater harvesting potential for local reuse in an urban growth area: a case study of Melton growth area in the west of Melbourne

Integrated urban water management approaches (IUWM) are implemented to address challenges from increases in water demand as a result of population growth and the impact of climate change. IUWM aims to utilize all water resources (stormwater, wastewater, and rainwater) based on fit-for-purpose concepts. Here, a local water utility in Melbourne’s Melton growth area explored the availability of stormwater as an alternative water resource for water service planning for a proposed residential development in an existing greenfield area of 13,890 hectares for 160,000 new houses by 2040. A methodology was developed for assessing the stormwater quantity and quality under land use change and different climatic conditions considering the availability of stormwater from the proposed urban development. The modelling results indicated that the amount of annual stormwater generated in the region increased by nearly four times to 32 GL/year under the 2040 full urban land use with high climate change. The provision of constructed wetlands in proposed development blocks was found to be efficient at removing TSS, TP, and TN, and able to retain over 90% of TSS, 77% of TP, and 52% of TN in all scenarios. Harvested stormwater, if treated to potable standards, can meet nearly 40% of water requirements for residential area needs

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm

ABSTRACT: Diesel engine emission standards are being more stringent as it gains more publicity in industry and transportation. Hence, designers have to suggest new controlling strategies which result in small amounts of emissions and a reasonable fuel economy. To achieve such a target, multi-objective optimization methodology is a good approach inasmuch as several types of objective are minimized or maximized simultaneously. In this paper, this technique is implemented on a closed cycle two-zone combustion model of a DI (direct injection) diesel engine. The main outputs of this model are the quantity of NOx, soot (which are the two main emissions in diesel engines) and engine performance. The optimization goal is to minimize NOx and soot while maximizing engine performance. Fuel injection parameters are selected as design variables. A neural network model of the engine is developed as an alternative for the complicated and time-consuming combustion model in a wide range of engine operation. Finally design variables are optimized using an evolutionary genetic algorithm, called NSGA-II

Comparison of the efficiency of ultrafiltration, precipitation, and ultracentrifugation methods for exosome isolation

Extracellular vesicles (EVs) are enclosed by a lipid-bilayer membrane and secreted by all types of cells. They are classified into three groups: apoptotic bodies, microvesicles, and exosomes. Exosomes play a number of important roles in the intercellular communication and crosstalk between tissues in the body. In this study, we use three common methods based on different principles for exosome isolation, namely ultrafiltration, precipitation, and ultracentrifugation. We use field emission scanning electron microscopy (FESEM) and dynamic light scattering (DLS) analyses for characterization of exosomes. The functionality and effect of isolated exosomes on the viability of hypoxic cells was investigated by alamarBlue and Flow-cytometry. The results of the FESEM study show that the ultrafiltration method isolates vesicles with higher variability of shapes and sizes when compared to the precipitation and ultracentrifugation methods. DLS results show that mean size of exosomes isolated by ultrafiltration, precipitation, and ultracentrifugation methods are 122, 89, and 60 nm respectively. AlamarBlue analysis show that isolated exosomes increase the viability of damaged cells by 11%, 15%, and 22%, respectively. Flow-cytometry analysis of damaged cells also show that these vesicles increase the content of live cells by 9%, 15%, and 20%, respectively. This study shows that exosomes isolated by the ultracentrifugation method are characterized by smaller size and narrow size distribution. Furthermore, more homogenous particles isolated by this method show increased efficiency of the protection of hypoxic cells in comparison with the exosomes isolated by the two other methods