Stabilization of refined olive oil by enrichment with chlorophyll pigments extracted from Chemlali olive leaves

This paper presents the first investigation on the effect of enrichment refined olive oil by chlorophyll pigment extracted from Chemlali olive leaves during storage (6 months). The changes that occurred in the quality indices, fatty acids, sterol, and phenolic content were investigated during the storage of refined olive oil under RT (20°C) and accelerated conditions (50°C) in the dark. Additionally, the pigments (chlorophyll and carotene) changes during 6 months of oil storage were evaluated. At the end of the storage, more than 90% of chlorophyll pigments decomposed in all samples, while, carotene pigment loss was lower showing up to 60 and 85% loss for oil stored at 20 and 50°C, respectively, at the end of storage. The reduction of total phenolic compounds exhibited similar degradation profiles, being reduced by 5% and up to 60% for the enriched refined olive oil stored at 20 and 50°C in 6 months, respectively. In the fatty acid composition, an increase in oleic acid and a decrease in linoleic and linolenic acids were less significant in enriched than non‐enriched refined olive oil. On the other hand, sterol composition was less affected by storage in enriched oil samples. However, the sterol concentration of the oil samples showed an increase in β‐sitosterol, 24‐methylene cholesterol, stigmasterol, and a decrease in cholesterol, Δ5, 24‐stigmastadienol percentage at the end of storage. Based on the Rancimat method, the oils with added leaf pigment extract had the lowest peroxide value and the highest stability. After 6 months of storage, the oxidative resistance of refined olive oil fell to 0.2 and to zero for enriched refined olive oil stored at 20 and 50°C, respectively.     

Extra-Virgin Olive Oil and Cheap Vegetable Oils: Distinction and Detection of Adulteration as Determined by GC and Chemometrics

Refined oils including corn, sunflower, soybean, and palm oils as well as low-quality olive oil such as refined lampante and pomace olive oils are commonly used for extra-virgin olive oil (EVOO) adulteration. Indeed, K ₂₇₀ could be used as a parameter for the detection of EVOO fraud for each type of the studied refined oils, 10 % olive, 4 % pomace olive, 10 % palm, 5 % corn, and 2 % soybean oils. Moreover, the adulteration could also be detected by the increase of the trans fatty acid contents with 10 % pomace olive, 3 % soybean, 3 % sunflower, 2 % corn, and 10 % palm oils. Actually, stigmasta-3,5-diene content is one of the most effective means of detecting refined oils in EVOO at low levels: 2 % olive, 0.4 % pomace olive, 1 % palm, 0.2 % soybean, 0.5 % sunflower, and 0.1 % corn oils. Finally, the application of linear discriminant analysis could represent an alternative and innovative tool for faster and cheaper evaluation of EVOO adulteration.     

Development of novel potentiometric sensors for determination of tartrazine dye concentration in foodstuff products

Tartrazine dye Na₃TZ in foodstuff products was determined by a new modified carbon paste electrode, encoded sensor A, and a coated silver wire electrode, encoded sensor B, based on tartrazine TZ- cetryltrimethyl ammoniumbromide CTAB as a chemical modifier TZ-CTA. The electrodes exhibit the following characteristics listed respectively: a Nernstian slope of 17.9 ± 0.5 and 19.4 ± 0.2 mV/decade for tartrazine ion over a wide concentration range from 4.3 × 10⁻⁷ to 1.0 × 10⁻² and 1.1 × 10⁻⁷ to 1.0 × 10⁻² M. The lower detection limits: 3.2 × 10⁻⁷ and 5.5 × 10⁻⁸ M. Short response time (5–8 s) over the pH range 3.8–7.7 and 4.2–8.1. The proposed sensors display significantly high selectivity for TZ ion over a wide variety of sugars, some anions, common organic, inorganic compounds and additives. The developed electrodes were applied to the potentiometric determination of tartrazine ion in different kinds of foodstuffs: solid jelly (strawberry and custard) powder samples and soft drink (orange) samples with satisfactory results.     

Best Practices for Procuring Commissioning Services

Owners are increasingly recognizing building commissioning as an effective means of ensuring quality and maximizing energy performance. The demand for commissioning new construction projects has increased substantially over a short period of time with the growing demand for leadership in energy and environmental design certified buildings. However, the pool of experienced commissioning providers hasn’t increased at the same rate. Many owners are procuring commissioning services for the first time and don’t know how to best procure these services. The objective of the research described in this paper was to identify best practices for procuring commissioning services for new construction projects and to compare them with current practices. Issues that were evaluated include the timing of the commissioning provider involvement, the independence of the commissioning provider, certification of the commissioning provider, selection criteria, and the importance of a clear definition of the scope of work. Research results indicate that there is variation between current practices and recommended best practices. It is important for building owners and industry professionals to understand and implement the best practices recommended in this paper to maximize the benefits of the commissioning process.     

A Multistage In Silico Study of Natural Potential Inhibitors Targeting SARS-CoV-2 Main Protease

Among a group of 310 natural antiviral natural metabolites, our team identified three compounds as the most potent natural inhibitors against the SARS-CoV-2 main protease (PDB ID: 5R84), M^pro. The identified compounds are sattazolin and caprolactin A and B. A validated multistage in silico study was conducted using several techniques. First, the molecular structures of the selected metabolites were compared with that of GWS, the co-crystallized ligand of M^pro, in a structural similarity study. The aim of this study was to determine the thirty most similar metabolites (10%) that may bind to the M^pro similar to GWS. Then, molecular docking against M^pro and pharmacophore studies led to the choice of five metabolites that exhibited good binding modes against the M^pro and good fit values against the generated pharmacophore model. Among them, three metabolites were chosen according to ADMET studies. The most promising M^pro inhibitor was determined by toxicity and DFT studies to be caprolactin A (292). Finally, molecular dynamics (MD) simulation studies were performed for caprolactin A to confirm the obtained results and understand the thermodynamic characteristics of the binding. It is hoped that the accomplished results could represent a positive step in the battle against COVID-19 through further in vitro and in vivo studies on the selected compounds.     

A Nonparametric Approach to Design Fixed-order Controllers for Systems with Constrained Input

International Journal of Control, Automation and Systems - This paper presents an approach for designing fixed-structure controllers for input-constrained linear systems using frequency domain...     

Neural Networks Pipeline for Offline Machine Printed Arabic OCR

In the context of Arabic optical characters recognition, Arabic poses more challenges because of its cursive nature. We purpose a system for recognizing a document containing Arabic text, using a pipeline of three neural networks. The first network model predicts the font size of an Arabic word, then the word is normalized to an 18pt font size that will be used to train the next two models. The second model is used to segment a word into characters. The problem of words segmentation in the Arabic language, as in many similar cursive languages, presents a challenge to the OCR systems. This paper presents a multichannel neural network to solve the offline segmentation of machine-printed Arabic documents. The segmented characters are then fed as an input to a convolutional neural network for Arabic characters recognition. The font size prediction model produced a test accuracy of 99.1%. The accuracy of the segmentation model using one font is 98.9%, while four-font model showed 95.5% accuracy. The whole pipeline showed an accuracy of 94.38% on Arabic Transparent font of size 18pt from APTI data set.     

Secure image sharing method over unsecured channels

Image sharing has become a prominent field in large and small networks. However, in order to deliver the shares to their respective shareholders, we need a secure channel to protect the shares from potential attacks by other users on the network. These users can locate the shares and retrieve the image using the Lagrange interpolation method if they are able to locate the minimum required number of shares. This paper proposes a method to protect the privacy and security of the shares when sending them through unsecured channels. The proposed method is divided into three phases. In the first phase, image pixels are changed using the column and row indices, then the shares are created using Shamir’s secret sharing (SSS) method. Then a relationship is created between the shares using a linear independence function. In the second phase, the first part of the image is encrypted to hide the solver data. Finally, in the third phase, the data is shared using the SSS method. Participants can retrieve the whole image by applying the reverse order of the proposed method using only the information from the sender through the same channel. The simulation results show that the proposed method is efficient and robust against different types of attacks and can be used to send the shares over unsecured channels.     

Digital image scrambling based on elementary cellular automata

Image scrambling is the process of converting an image to an unintelligible format, mainly for security reasons. The scrambling is considered as a pre-process or a post-process of security related applications such as watermarking, information hiding, fingerprinting, and encryption. Cellular automata are parallel models of computation that prove an interesting concept where a simple configuration can lead to a complex behavior. Since there are a lot of parameters to configure, cellular automata have many types and these types differ in terms of complexity and behavior. Cellular automata were previously used in scrambling different types of multimedia, but only complex two-dimensional automata were explored. We propose a scheme where the simplest type of cellular automata is used that is the elementary type. We test the scrambling degree for different cellular automata rules that belong to classes three and four of Wolfram’s classification which correspond to complex and chaotic behavior; we also check the effect of other parameters such as the number of generations and the boundary condition. Experimental results show that our proposed scheme outperforms other schemes based on cellular automata in terms of scrambling degree.     

Modeling the Effectiveness of Rain Barrels,Cisterns, and Downspout Disconnections for Reducing Combined Sewer Overflows in a City-Scale Watershed

Green Infrastructure / Low Impact Development (GI/LID) is an increasingly popular strategy to manage urban stormwater for individual properties, but the aggregate effect on runoff reduction at the city scale has not been thoroughly investigated. This study examined the potential combined effects of rain barrels, cisterns, and downspout disconnections on combined sewer overflows (CSOs) for a medium-sized urban center. To support a city-wide analysis, a novel simulation strategy was implemented using the Storm Water Management Model (SWMM). In this new approach, a modeling at the source technique for subcatchment delineation was combined with a set of R-language utilities to automatically configure GI/LID management scenarios. The reconfigured SWMM model was used to examine 99 distinct management scenarios based on different sizes, numbers, and locations of the targeted GI/LID features for the city of Buffalo, New York. For a typical hydrologic year, the deployment of large residential rain barrels (1000-gallon) resulted in up to a 12% reduction in predicted CSO volume, while the inclusion of large commercial-roof cisterns (5000-gallon) contributed up to an additional 12% reduction. Large variations in the predicted CSO reductions were observed across the various management scenarios, and the simulation tools were able to identify locations where the GI/LID features were most effective. In general, the modeling at the source approach and the R-language tools substantially enhanced the utility of SWMM for evaluating the effectiveness of GI/LID deployment as a CSO management strategy at the city scale, and the methodology can readily be adapted to cities with similar CSO issues.