Combined effect of microwave and ultrasonication treatments on the quality and stability of sugarcane juice during cold storage

The main objective of this study was to investigate the combined effect of microwave (MW) treatment (2450 MHz for 120 s), ultrasonication (US) (24 KHz, 20 °C for 20 min) and combined treatment (MW-US) on the quality and stability of sugarcane juice (SCJ) during 21 days of storage at 4 °C. The effect of the treatments and storage time on physicochemical, bioactive compounds (total phenolic, flavonoids and ascorbic acid content) and microbial analysis of SCJ. No significant (P < 0.05) changes were observed in °Brix, while there was an increase in pH and a decrease in titratable acidity in all treatments. Compared to US and MW, MW-US treatments was more effective in preserving colour attributes, total phenolic and flavonoids contents, ascorbic acid and antioxidant capacities of the SCJ during storage. The results regarding the microbial count indicate that more microbial safety and longer shelf life was achieved by MW-US. MW-US treatment is an effective technology for improving the safety and shelf life of SCJ by minimising quality changes, retaining bioactive compounds and reducing microbial growth during storage.     

Fraudulent Firm Classification: A Case Study of an External Audit

This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew’s Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.     

Methods of analysis modified size exchange chromatography method for analysis of heavy oil residues

A modified size exchange chromatography method is used to obtain molecular weight distributions, average molecular weight, and other characteristics of heavy oil residues: coal asphalt, petroleum asphalt, vacuum residue oil, and ethylene residue oil. This method can be applied for developing and studying processes involved in hydrogenation of heavy oil residues, road asphalt production, and coal liquefaction.     

Investigating the effect of production process of ball mill refiner on some physical quality parameters of compound chocolate: response surface methodology approach

Chocolate compound was produced using ball mill refiner, and the effect of agitator shaft speed and refining time on the physical quality parameters (particle size, colour and steady‐state rheology) of compound chocolate was determined using response surface methodology. The shaft speed and refining time range were selected between 40–60 r.p.m. and 10–30 min, respectively. Determination coefficient of the models established for particle size, Newtonian viscosity and colour parameters (brightness, chroma and hue angle) were found to be very close to unity. Increasing shaft speed and time induced a reduction in particle size and an increase in viscosity of the samples. Temperature sweep test was also performed, and the obtained data were successfully fitted to Arrhenius equation to calculate the corresponding parameters representing temperature dependency of the compounds. The results highlighted that the establishment of such models can provide essential information in terms of optimisation of production processes regarding usage purpose of the compound chocolate.     

On human–machine relations

Cognition, Technology & Work - This paper presents an approach to human–machine interactions based on the concept of teamwork and the psychological theory of object relations. We envision...     

Reactive scheduling in a dynamic and stochastic FMS environment

In this paper, we study the reactive scheduling problems in a dynamic and stochastic manufacturing environment. Specifically, we develop a simulation-based scheduling system for flexible manufacturing systems. We also propose several reactive scheduling policies (i.e. when-to-schedule and how-to-schedule policies) and test their performances under various experimental conditions, processing time variations, and machine breakdowns. Moreover, we compare offline and online scheduling schemes in a dynamic manufacturing environment. The results of extensive simulation experiments indicate that the variable-time-response is better than the fixed-time-response. The full scheduling scheme generally performs better than the partial scheduling. Finally, the online scheduling is more robust to uncertainty and variations in processing times than the optimum-seeking offline scheduling. A comprehensive bibliography is also provided in the paper.