An insight into curcumin‐based photosensitization as a promising and green food preservation technology

Consumer awareness on the side effects of chemical preservatives has increased the demand for natural preservation technologies. An efficient and sustainable alternative to current conventional preservation techniques should guarantee food safety and retain its quality with minimal side effects. Photosensitization, utilizing light and a natural photosensitizer, has been postulated as a viable and green alternative to the current conventional preservation techniques. The potential of curcumin as a natural photosensitizer is reviewed in this paper as a practical guide to develop a safe and effective decontamination tool for industrial use. The fundamentals of the photosensitization mechanism are discussed, with the main emphasis on the natural photosensitizer, curcumin, and its application to inactivate microorganisms as well as to enhance the shelf life of foods. Photosensitization has shown promising results in inactivating a wide spectrum of microorganisms with no reported microbial resistance due to its particular lethal mode of targeting nucleic acids. Curcumin as a natural photosensitizer has recently been investigated and demonstrated efficacy in decontamination and delaying spoilage. Moreover, studies have shown the beneficial impact of an appropriate encapsulation technique to enhance the cellular uptake of photosensitizers, and therefore, the phototoxicity. Further studies relating to improved delivery of natural photosensitizers with inherent poor solubility should be conducted. Also, detailed studies on various food products are warranted to better understand the impact of encapsulation on curcumin photophysical properties, photo‐driven release mechanism, and nutritional and organoleptic properties of treated foods.     

The effect of maturity and season on health-related bioactive compounds in wild harvested fruit of Terminalia ferdinandiana (Exell)

This study reports changes in the concentrations of important health-related bioactive compounds (vitamin C and ellagic acid) and morphology of wild harvested Kakadu plum fruits that were collected during three harvest seasons and four maturities. The results showed that fruit weight increased, whereas fruit length and width changed slightly with the advance in maturity. Vitamin C increased up to 20 folds (from 1.2 to 21.2% dry weight [DW]) from immature to mature stage, whereas ellagic acid decreased approximately three times (from 6.5 to 2.1% DW). Similar trends were observed over the three harvest seasons studied. A positive correlation between fruit weight and vitamin C, whereas a negative correlation with ellagic acid was observed, indicating that maturity plays an important role in contributing to the variation of ellagic acid and vitamin C. Season also had an effect and showed the influences of rainfall, temperature and solar exposure on the biosynthesis of vitamin C and ellagic acid.     

Acacia seed proteins: Low or high quality? A comprehensive review

The popularity of adding value to indigenous plant protein sources has increased due to the rise in the world population, high costs of animal protein as compared to plant proteins, and an increase in the consumer awareness of the nutritional and functional roles of dietary plant protein. Seeds of acacia plants (containing over 1,350 species) have considerable amount of protein (18.25% to 35.5%) and nutritionists have shown great interest in assessing the quality and functionality of proteins from these protein‐rich plants. In this review, the overall nutritional and health‐promoting properties of acacia seed (AS) species are introduced. Extraction, quality, and functional properties of proteins from different AS species are discussed. Furthermore, anti‐nutritional components and protease inhibitors present in AS species and the effects of processing methods applied to lower the levels of anti‐nutrients are also discussed. Previous applications of AS in food formulations are highlighted. This review aims to provide updated findings that have been reported on AS proteins and to highlight areas for further studies in order to increase the utilization potential of the seeds.     

Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

Concrete carbonation is one of the main causes of corrosion of the reinforcement and consequently causing damage to the reinforced concrete structures. The progress of the carbonation front depends on many factors including mixture proportions and exposure conditions. Several carbonation prediction models including mathematical and analytical predictions are available. Most of these models, however, are based on simple regression equations and cannot predict or accurately reflect the various factors involved in concrete carbonation. The current published results in this issue are in conflict. In view of this, our research aims to apply an artificial neural network (ANN) approach for predicting the carbonation of fly-ash concrete taking into account the most influential parameters, including mixture proportions and exposure conditions. Six parameters were considered as inputs to the ANN model, covering, binder and fly-ash content, water-to-binder ratio, CO₂ concentration, relative humidity, and time of exposure; one output is carbonation depth. The ANN model was prepared, trained, and tested with 300 datasets from experiments as well as past research. The performance of training, validation, and test sets shows a high correlation between the experimental and the ANN predicted values of the carbonation depth. In addition, the proposed prediction model was in good agreement with the experimental data in comparison with other model. This study concludes that the use of this model for numerical investigations on the parameters affecting the carbonation depth in fly-ash concrete is successful and provides scientific guidance for durability design.

     

The triangle scheduling problem

This paper introduces a novel scheduling problem, where jobs occupy a triangular shape on the time line. This problem is motivated by scheduling jobs with different criticality levels. A measure is introduced, namely the binary tree ratio. It is shown that the Greedy algorithm solves the problem to optimality when the binary tree ratio of the input instance is at most 2. We also show that the problem is unary NP-hard for instances with binary tree ratio strictly larger than 2 and provide a quasi-polynomial time approximation scheme. The approximation ratio of Greedy on general instances is shown to be between 1.5 and 1.05.     

Effect of adhesive thickness and surface roughness on the shear strength of aluminium one-component polyurethane adhesive single-lap joints for automotive applications

Adhesively bonded technology is now widely accepted as a valuable tool in mechanical design, allowing the production of connections with a very good strength‐to‐weight ratio. The bonding may be made between metal–metal, metal–composite or composite–composite. In the automotive industry, elastomeric adhesives such as polyurethanes are used in structural applications such as windshield bonding because they present important advantages in terms of damping, impact, fatigue and safety, which are critical factors. For efficient designs of adhesively bonded structures, the knowledge of the relationship between substrates and the adhesive layer is essential. The aim of this work, via an experimental study, is to carry out and quantify the various variables affecting the strength of single-lap joints (SLJs), especially the effect of the surface preparation and adhesive thickness. Aluminium SLJs were fabricated and tested to assess the adhesive performance in a joint. The effect of the bondline thickness on the lap-shear strength of the adhesives was studied. A decrease in surface roughness was found to increase the shear strength of the SLJs. Experimental results showed that rougher surfaces have less wettability which is coherent with shear strength tests. However, increasing the adhesive thickness decreased the shear strength of SLJs. Indeed, a numerical model was developed to search the impact of increasing adhesive thickness on the interface of the adherend.     

Service life prediction of fly ash concrete using an artificial neural network

Carbonation is one of the most aggressive phenomena affecting reinforced concrete structures and causing their degradation over time. Once reinforcement is altered by carbonation, the structure will no longer fulfill service requirements. For this purpose, the present work estimates the lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neural network technique. A collection of 300 data points was made from experimental results available in the published literature. Backpropagation training of a three-layer perceptron was selected for the calculation of weights and biases of the network to reach the desired performance. Six parameters affecting carbonation were used as input neurons: binder content, fly ash substitution rate, water/binder ratio, CO₂ concentration, relative humidity, and concrete age. Moreover, experimental validation carried out for the developed model shows that the artificial neural network has strong potential as a feasible tool to accurately predict the carbonation depth of fly ash concrete. Finally, a mathematical formula is proposed that can be used to successfully estimate the service life of fly ash concrete.     

Antimicrobial effects of six Algerian propolis extracts

This study aimed to study the antimicrobial effect of propolis and determine the essential compounds which give it the antimicrobial activity. The antimicrobial effect was investigated for six ethyl acetate extracts of propolis (EAPs) collected from different regions of Algeria on four pathogenic strains (Shigella dysenteriae, Shigella sonnei, Staphylococcus aureus and Escherichia coli) and two benefit strains (Bifidobacterium animalis and Lactobacillus rhamnosus). The results obtained in this study indicated that the different EAPs showed potential inhibitory effects on tested pathogenic strains with variable minimum inhibitory concentration (MIC) values of 0.3 and 9 mg ml^?1, while the beneficial bacteria showed resistance against EAPs of propolis. The results demonstrated also that the flavonoid compounds have very low MIC values, while phenolic acid compounds have variables MIC values of 1–10 mg ml^?1. These results indicated that propolis contains proven substances with antimicrobial activity and are a prelude to the investigations aimed at empowerment of the bee substance as a potential source of antimicrobial agents with multiple outlets.     

Performance analysis and improvement of the use of wind tower in hot dry climate

Wind towers for passive evaporative cooling offer real opportunity for improving the ambient comfort conditions in building whilst reducing the energy consumption of air-conditioning systems.This study aims at assessing the thermal performance of a bioclimatic housing using wind towers realized in a hot dry region of Algeria. Performance monitoring and site measurement of the system provide data which assist model validation. The analysis and site measurement are encouraging, and they confirm the advantage of the application of this passive cooling strategies in hot dry climate.A mathematical model is developed using heat and mass transfer balances. For a more effective evaporative cooling, a number of improvements on wind tower configurations are proposed.