Application of fungi resistance on cotton fabric using aloe vera active component

Purpose This paper aims to prevent cotton textiles from fungi damage using eco-friendly aloe vera leaf extract, which was applied at a minimum amount, and cost-effective material. Design/methodology/approach Batch extraction method using methanol solvent; phytochemical analysis was investigated and three-level factorial design of experiment and analysis of variance (ANOVA) was used for the optimization of 27 test runs. The finish was applied by pad-dry-cue at distinct concentrations, and the chemical property after treatment was studied. Colorfastness and coordinates are analyzed. Cotton fabrics were cultured with Fusarium oxysporum fungi and the anti-fungal property was examined and reported according to AATCC 30–2004 standard. Findings The maximum yield of extract was at an optimum volume of 200 ml, 65 °C for 120 min. The effective antifungal fabric was achieved with minimum concentrations. There was significant strength loss in warp and weft direction. The treatment results in yellow-colored cotton fabric with fastness grade 3. The antifungal effect is durable until fifteen washes as the tensile strength losses were less than 1%. Research limitations/implications The findings of this work were based on samples considered in the laboratory. However, it can be reproducible at the factory production scale the treatment has the potential of yielding yellow dyed cotton fabric with multifunctional finishing. Practical implications The treated fabric is against Fusarium oxysporum Fungi which is one of the vital antimicrobial properties of textile apparel products for various areas of application. Social implications The natural extract material applied to a textile material is eco-friendly effective against microbes of cotton seeds during cultivation and apparel end-uses. Originality/value The work application of fungi resistance on cotton fabric using aloe vera active component was original; this work provides extraction of the active agent from aloe vera leaf, which is optimized statically and successfully applied for anti-fungal activity on cotton fabric.

Crowdfunding and bank financing: substitutes or complements?

In this paper, we analyze a firm choice between crowdfunding and bank financing. For many entrepreneurs, it is an important issue. We analyze a model where the choice of financing is affected by moral hazard problem regarding the choice of production scale that favors bank financing, and by the uncertainty about market demand that favors crowdfunding. We argue that long crowdfunding campaigns or campaigns with large targets usually are less efficient in mitigating moral hazard problem than small/short campaigns. We also argue that high-quality firms and firms with potentially large markets will tend to select bank financing while projects with largest amount of investment should select mixed financing where the firm uses a short crowdfunding campaign and a bank loan. Most of our model empirical predictions have not been directly tested so far while they are indirectly consistent with available evidence.

Development of a Semi-Empirical Model for Droplet Size Determination of a Three-Channel Spray Nozzle for Pellet Coating Based on the Optical Method Concept

The purpose of this study was to investigate the droplet size obtained with a three-channel spray nozzle typically used in fluid bed devices and to construct a semi-empirical model for prediction of droplet size. With the aid of a custom-made optical method concept, the impact of the type of polymer and solvents used through dispersion properties (viscosity, density, and surface tension), dispersion flow rate, atomization pressure, and microclimate pressure on droplet size was investigated. A semi-empirical model with adequate predictability for calculating the average droplet size (R2 = 0.90, Q2 = 0.73) and its distribution (R2 = 0.84, Q2 = 0.61) was constructed by employing dimensional analysis and design of experiments. Newtonian and non-Newtonian dispersion and process parameters on laboratory and on production scale were included, thereby enabling constant droplet size irrespective of the scale. Based on the model results, it would be possible to scale-up the atomization process (e.g., coating process) from laboratory to production scale in a systematic fashion, regardless of the type of solvent or polymer used. For the system investigated, this can be performed by understanding the dispersion properties, such as viscosity, density, and surface tension, as well as the following process parameters: dispersion flow rate, atomization, and microclimate pressure.

Developing Methods and Algorithms for Cloud Computing Management Systems in Industrial Polymer Synthesis Processes

To date, the resources and computational capacity of companies have been insufficient to evaluate the technological properties of emerging products based on mathematical modelling tools. Often, several calculations have to be performed with different initial data. A remote computing system using a high-performance cluster can overcome this challenge. This study aims to develop unified methods and algorithms for a remote computing management system for modelling polymer synthesis processes at a continuous production scale. The mathematical description of the problem-solving algorithms is based on a kinetic approach to process investigation. A conceptual scheme for the proposed service can be built as a multi-level architecture with distributed layers for data storage and computation. This approach provides the basis for a unified database of laboratory and computational experiments to address and solve promising problems in the use of neural network technologies in chemical kinetics. The methods and algorithms embedded in the system eliminate the need for model description. The operation of the system was tested by simulating the simultaneous statement and computation of 15 to 30 tasks for an industrially significant polymer production process. Analysis of the time required showed a nearly 10-fold increase in the rate of operation when managing a set of similar tasks. The analysis shows that the described formulation and solution of problems is more time-efficient and provides better production modes. Doi: 10.28991/esj-2021-01324 Full Text: PDF

New Scale-up Technologies for Hydrogenation Reactions in Multipurpose Pharmaceutical Production Plants

The classical scale-up approach for hydrogenation reaction processes usually includes numerous laboratory- and pilot-scale experiments. With a novel scale-up strategy, a significant number of these experiments may be replaced by modern computational simulations in combination with scale-down experiments. With only a few laboratory-scale experiments and information about the production-scale reactor, a chemical process model is developed. This computational model can be used to simulate the production-scale process with a range of different process parameters. Those simulations are then validated by only a few experiments in an advanced scale-down reactor. The scale-down reactor has to be geometrically identical to the corresponding production-scale reactor and should show a similar mass transfer behaviour. Closest similarity in terms of heat transfer behaviour is ensured by a sophisticated 3D-printed heating/cooling finger, offering the same heat exchange area per volume and overall heat-transfer coefficient as in production-scale. The proposed scale-up strategy and the custom-designed scale-down reactor will be tested by proof of concept with model reactions. Those results will be described in a future publication. This project is an excellent example of a collaboration between academia and industry, which was funded by the Aargau Research Fund. The interest of academia is to study and understand all physical and chemical processes involved, whereas industry is interested in generating a robust and simple to use tool to improve scale-up and make reliable predictions.

Renewable Energy Industry: Challenges and Development Opportunities

The development features of the renewable energy industry in solar and wind generation sectors, both characterized by their high manufacturability and equipment and components export potential and their extremely low production scale have been presented. The issues in relation to the solar and wind resources efficiency, cost reduction of solar and wind generation, electricity production technologies by increased application of the international practices have been studied.

Prediction of Enterprise Economic Activity Behavior Based on Neural Network and ARIMA Hybrid Model

Enterprise economy refers to the comprehensive situation reflected in the gross product, production scale, total production and efficiency, technological content, marketing means, and so on; under certain social conditions, enterprises use resources obtained by law to engage in economic activities. Under the guidance of consciousness or culture, enterprises use “legally obtained resources” to promote economic development. Enterprise economy is affected by manpower, capital, management, operation, policy, and other aspects. In the context of the rapid development of big data in the current era, this paper proposes a prediction model of enterprise economic activity behavior based on neural network and ARIMA by investigating a variety of artificial intelligence models and verifies its feasibility. Commodity circulation enterprises have a more urgent demand for the development of business audit due to their operation characteristics. Therefore, this paper takes commodity circulation enterprises as representatives and predicts business audit in the big data environment based on the model proposed in this paper.

Data-Driven Evaluation and Optimization of Agricultural Sustainable Development Capability: A Case Study of Northern Anhui

Human survival depends on the sustainable development of agriculture. This study constructs a data-driven evaluation and optimization method of agricultural sustainable development capacity, aiming to better cope with challenges such as environmental pollution and excessive consumption of resources and energy, as well as improve agricultural economic level. Further, an evaluation index system was constructed based on comprehensive consideration of energy and resources utilization, environmental pollution, and agricultural economy. After simplifying and integrating the data, a data envelopment analysis model was constructed to quantitatively evaluate the capability for agricultural sustainable development and its changing trend. Moreover, its influencing factors were analyzed from the perspective of input, which provides accurate countermeasures for improving agricultural sustainable development ability, resource utilization efficiency, and process optimization. This study shows the realization process of the aforementioned method for the agricultural development of six cities in northern Anhui from 2010 to 2019. Our results suggest that the sustainable development ability of northern Anhui is weak, but overall, has a good development trend. Based on our results, some countermeasures were proposed to control production scale reasonably, reduce environmental load, and improve resource efficiency, which provides a reference for policymakers to guide and standardize the development of regional agriculture.

Production Efficiency of Animal-Feed Obtained From Food Waste in Japan

Converting food waste into animal-feed is highly useful for tackling the problem of food waste, which is particularly severe in developed countries. This study quantified inefficiencies in converting food waste into animal-feed and identified their causes through a data envelopment analysis (DEA) of the monthly input–output data of two producers of animal-feed obtained from food waste in Japan. Our empirical analysis revealed that the producers of animal-feed obtained from food waste (especially those treating food waste from retail and service industries) demonstrated inefficiencies in production technology and scale; moreover, expanding the production scale and improving the quality of food waste could enhance production efficiency. Based on the empirical results, specific policy implications were provided for the widespread use of animal-feed obtained from food waste in Japan and elsewhere, globally. Furthermore, it was found that the COVID-19 pandemic contributed to a severe reduction in the production efficiency of animal-feed producers treating food waste obtained from retail and service industries.