Investigation of Heat‐Induced Degradation of Virgin Olive Oil Using Front Face Fluorescence Spectroscopy and Chemometric Analysis

A simple setup using a 365‐nm light‐emitting diode coupled to a USB spectrometer through an optical fiber, in a front‐face fluorescence configuration, was used to investigate the heat‐induced deterioration of virgin olive oil at different heating temperatures and times. The samples were heated for 30, 60, 120 and 180 min for every temperature setting of 140, 160 and 180 °C, respectively. Two important results are reported in this article. First, a neo‐formed compound around 665 nm due to the degradation of chlorophyll was observed. This new peak was attributed to pyropheophytins. The second result showed an important rise of the peak around 489 nm, which corresponded to the oxidation products. The correlation obtained between the peroxide value and the 489 nm peak using principal component analysis revealed the mechanism of the oxidation process. It further showed that the peak around 489 nm is a direct consequence of the degradation of hydroperoxide.     

Optimum design of cam-roller follower mechanism using a new evolutionary algorithm

Selection of tooling to perform specific operations like drilling and milling on ceramic materials using rotary ultrasonic machining process is an important aspect to meet stringent dimensions on workpiece as well as intended performance of tool. This phenomenon is more critical for micro rotary ultrasonic machining. In the present study, an effort was made to do micro drilling operation of Ø0.3 mm tool with varying geometry, having different wall thicknesses and abrasive grain sizes using design of experiments. The effect of tool-based parameters like grain size and wall thickness has been studied on axial cutting force, radial cutting force, tool wear, edge chipping area and taper. After examining axial and radial cutting forces, it has been concluded that lower wall thickness (80 μm) tool is good for drilling operation; and higher wall thickness (100 μm) tool is good for milling operation under same material removal rate conditions. It has been also investigated that lower wall thickness (80 μm) tool has less edge chipping area and less taper and can impart high drilling depth as compared to higher wall thickness (100 and 150 μm) tool. It is also concluded that lesser grain size (15 μm) tools are advantageous in terms of edge chipping area and cutting force for drilling and milling operations as compared to higher grain size (30, 35 and 45 μm) tool at constant material removal rate. Higher grain size tools have been broken at 1.13 mm³/h material removal rate conditions due to bad profile accuracy. But higher grain size tools have worked fairly well at less material removal rate condition. Higher grain size tools produced less wear. Tool wear was found minimum in higher wall thickness (100 μm) tool having higher abrasive grain size (30 μm). Using inferred results, Ø0.3 mm drilling experiments have been carried out on six aerospace ceramic materials. Also, groove of 0.5 mm size using Ø0.3 mm optimised tool has been successfully carried out in sintered SiC.     

Synthesis and Anticancer Properties of Oxazepines Related to Azaisoerianin and IsoCoQuines

In this article, we report the synthesis and biological properties of a series of novel oxazepines related to isoCA-4 having significant antitumor properties. Among them, three oxazepin-9-ol derivatives display a nanomolar or a sub-nanomolar cytotoxicity level against five human cancer cell lines (HCT116, U87, A549, MCF7, and K562). It was demonstrated that the lead compound in this series inhibits tubulin assembly with an IC₅₀ value of 1 μM and totally arrests the cellular cycle in the G2/M phase at the low concentration of 5 nM in HCT116 and K562 cells. Molecular modeling studies perfectly corroborates these promising results.     

The role of the experimental data base used to identify material parameters in predicting the cyclic plastic response of an austenitic steel

The first objective of this paper investigates the influence of the previous strain history on ratcheting. New tests were performed where different strain-controlled histories have been applied prior to ratcheting tests. It is demonstrated that under the same conditions, one can observe ratcheting, plastic shakedown or elasticity according to the prior strain-controlled history. The second objective points out the correlation between the experimental data base devoted to the identification of the material parameters and the quality of the predictions in cyclic plasticity. The results suggest that the choice of the tests should be closely linked to the capabilities of the model. In particular, the presence of non proportional strain-controlled tests in the data base may be not a good choice if the model itself is not able to represent explicitly such a character. All tests considered here were performed on 304L SS at room temperature.     

Reasoning from last conflict(s) in constraint programming

Constraint programming is a popular paradigm to deal with combinatorial problems in artificial intelligence. Backtracking algorithms, applied to constraint networks, are commonly used but suffer from thrashing, i.e. the fact of repeatedly exploring similar subtrees during search. An extensive literature has been devoted to prevent thrashing, often classified into look-ahead (constraint propagation and search heuristics) and look-back (intelligent backtracking and learning) approaches. In this paper, we present an original look-ahead approach that allows to guide backtrack search toward sources of conflicts and, as a side effect, to obtain a behavior similar to a backjumping technique. The principle is the following: after each conflict, the last assigned variable is selected in priority, so long as the constraint network cannot be made consistent. This allows us to find, following the current partial instantiation from the leaf to the root of the search tree, the culprit decision that prevents the last variable from being assigned. This way of reasoning can easily be grafted to many variations of backtracking algorithms and represents an original mechanism to reduce thrashing. Moreover, we show that this approach can be generalized so as to collect a (small) set of incompatible variables that are together responsible for the last conflict. Experiments over a wide range of benchmarks demonstrate the effectiveness of this approach in both constraint satisfaction and automated artificial intelligence planning.     

Municipal solid waste compost application improves productivity, polyphenol content, and antioxidant capacity of Mesembryanthemum edule

Organic wastes were successfully used as soil amendment to improve agrosystems productivity. Yet, the effectiveness of this practice to enhance plant antioxidant capacities has received little attention. Here, we assess the effect of municipal solid waste (MSW) compost (at 40 t ha(-1)) on growth, polyphenol contents and antioxidant activities of Mesembryanthemum edule. MSW compost application significantly increased the soil contents of carbon, nitrogen, calcium, phosphorus and potassium. This was associated with higher nutrient (N, P, and K) uptake, which likely led to the significant improvement of the plant biomass and relative growth rate (RGR) (+93% on average) as compared to the control. In the same way, the fertilizing effect of the added organic matter significantly enhanced the antioxidant potential M. edule, assessed by radical scavenging activity, iron reducing power and β-carotene bleaching capacity. This was associated with significantly higher antioxidant contents, mainly total phenols and flavonoids. Heavy metal (Pb, Cd, Cu, and Zn) concentrations were slightly increased upon compost application, but remained lower than phytotoxic values. Overall, our results point out that short-term MSW compost application at 40 t ha(-1) is efficient in enhancing the productivity together with the antioxidant potentiality of M. edule without any adverse environmental impact.     

Joined Spectral Trees for Scalable SPIHT-Based Multispectral Image Compression

In this paper, the compression of multispectral images is addressed. Such 3-D data are characterized by a high correlation across the spectral components. The efficiency of the state-of-the-art wavelet-based coder 3-D SPIHT is considered. Although the 3-D SPIHT algorithm provides the obvious way to process a multispectral image as a volumetric block and, consequently, maintain the attractive properties exhibited in 2-D (excellent performance, low complexity, and embeddedness of the bit-stream), its 3-D trees structure is shown to be not adequately suited for 3-D wavelet transformed (DWT) multispectral images. The fact that each parent has eight children in the 3-D structure considerably increases the list of insignificant sets (LIS) and the list of insignificant pixels (LIP) since the partitioning of any set produces eight subsets which will be processed similarly during the sorting pass. Thus, a significant portion from the overall bit-budget is wastedly spent to sort insignificant information. Through an investigation based on results analysis, we demonstrate that a straightforward 2-D SPIHT technique, when suitably adjusted to maintain the rate scalability and carried out in the 3-D DWT domain, overcomes this weakness. In addition, a new SPIHT-based scalable multispectral image compression algorithm is used in the initial iterations to exploit the redundancies within each group of two consecutive spectral bands. Numerical experiments on a number of multispectral images have shown that the proposed scheme provides significant improvements over related works.     

A Cryptographic-Based Approach for Electricity Theft Detection in Smart Grid

In order to strengthen their security issues, electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies integration in smart grid fields. Having been treated earlier by several researchers, various detection schemes adapted from attack models that benefit from the smart grid topologies weaknesses, aiming primarily to the identification of suspicious incoming hazards. Wireless meshes have been extensively used in smart grid communication architectures due to their facility, lightness of conception and low cost installation; however, the communicated packets are still exposed to be intercepted maliciously in order either to falsify pertinent information like the smart meter readings, or to inject false data instead, aiming at electricity theft during the communication phase. For this reason, this paper initiates a novel method based on RSA cryptographic algorithm to detect electricity fraud in smart grid. This new method consists of generating two different cryptograms of one electricity measurement before sending, after which the recipient is used to find the same value after decrypting the two cyphers in a normal case. Otherwise, a fraudulent manipulation could occur during the transmission stage. The presented method allows us to kill two birds with one stone. First, satisfactory outcomes are shown: the algorithm accuracy reaches 100%, from one hand, and the privacy is protected thanks to the cryptology concept on the other hand.     

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.