Impact of initial moisture content levels,freezing rate and instant controlled pressure drop treatment (DIC) on dehydrofreezing process and quality attributes of quince fruits

Dehydrofreezing process involves water partial removal before freezing. This treatment has been proposed in order to reduce the negative impacts of conventional or even accelerated freezing, especially on the textural quality of high water content fruits and vegetables. Indeed, in such cases, freezing and thawing processes result in severe damage of the integrity of product’s cell structure due to the formation of ice crystals. For this purpose, quince fruits (7?g H₂O/g db) were subjected to convective air drying of 40?°C and 3m/s to reach different water content levels of 2, 1, and 0.3?g H₂O/g db. Freezing profiles obtained at various freezing rates (V₁, V₂, and V₃) for different water contents allowed the main freezing characteristics such as the Initial Freezing Temperature (IFT), the Practical Freezing time (PFt), and the Specific Freezing time (SFt) to be assessed. The impact of freezing rate was important on PFt and SFt, and more pronounced for high water contents (W between 7 and 2?g H₂O/g db (dry basis)). Furthermore, IFT decreased sharply when initial sample water content decreased. Indeed, it started at ?0.8?°C for W?=?7g H₂O/g db, while it reached a value of ?8.2?°C for samples of W?=?1g H₂O/g db. Since convective air drying normally triggers shrinkage which causes a detrimental deformation of fruit structures, instant controlled pressure drop (DIC) treatment was used to improve the texture and enhance the whole dehydrofreezing performance and the final frozen-thawed product quality. Moreover, DIC implied a slight increase of PFt compared to untreated ones. On the other hand, quality attributes were estimated through the assessment of thawed water exudate (TWE g H₂O/100?g db), color and texture (maximum puncture force as index of firmness): freezing rate and water content had great impacts on TWE. Hence, the lower the water content, the weaker the TWE. Furthermore, the TWE of the pre-dried quince (0.3?g H₂O/g db) had higher value for DIC-textured samples than for the un-treated ones. Indeed, DIC-texturing leads to a well-controlled structure expansion of the cell wall. These textural changes resulted in more lixiviation of residual water. Consequently, water becomes more available, hence more releasable after thawing. Finally, the partial removal of water by air drying before freezing remarkably reduced the negative impact of freezing/thawing processes on final quince color. Decisively, the firmness of quince fruit increased with the decrease of water content level.

Abbreviations: DMC: Dry Matter Concentration (%); DIC: Instant controlled pressure drop; W: Water content dry basis (g H₂O/g db); IFT: Initial Freezing Temperature (°C); PFt: Practical Freezing time (min); SFt: Specific Freezing time (min); TWE: Thawed Water Exudate (g H₂O/100?g db); L, a, and b: Color coordinates; (L): The degrees of lightness; (a) and (–a): The redness (a) or greenness (?a), respectively; (b) and (?b): The yellowness (b) or blueness (?b), respectively; ΔE*_ab: Total color difference; L₀, a₀, and b₀: Color coordinates of fresh or dried quince samples; SD: Standard Deviation; ANOVA: Analysis of variances; LSD: Least Significant Differences; cp: Specific Heat of the product depending on composition (dry material and water content)(KJ/kg K); cp_d: Specific Heat of the dry material (KJ/kg K); cp_W: Specific Heat of water (KJ/kg K); V₁: Freezing rate without insulation; V₂: Freezing rate with a food stretch film insulation with thickness e2?=?3?mm and thermal conductivity λ₂?=?0.17 W/m K; V₃: Freezing rate with a versatile flexible insulation (Armacell) with thickness e3?=?13mm and weak thermal conductivity λ₃?=?0.036 W/m K; v_d: Volume of dry material of quince sample (mm³); v_H2O: Volume of quince sample water (mm³); v_t: Total volume of quince sample (mm³); e₀: Quince sample thickness (mm); e₂: Insulation thickness in the case V2; = 3?mm; ; e₃: Insulation thickness in the case V3; = 13?mm; ; λ₀: Quince sample conductivity (W/m K); λ₂: Insulation conductivity in the case V2;?=?0.17 W/m K; ; λ₃: Insulation conductivity in the case V3;?=?0.036 W/m K; λ_d: Conductivity of quince sample dry material (W/m K); λ_H2O: Conductivity of water (W/m K); λ_equiv: Equivalent conductivity of quince sample versus water content (W/m K); m_i and m_f: Weights of the frozen and thawed samples, respectively     

Circle-criterion Based Nonlinear Observer Design for Sensorless Induction Motor Control

This paper deals with the design of a nonlinear observer for sensorless induction motor control. Based upon the circle criterion approach, a nonlinear observer is designed to estimate pertinent but unmeasurable state variables of the considered induction machine for sensorless control purpose. The observer gain matrices are computed as a solution of linear matrix inequalities(LMI) that ensure the stability conditions of the state observer error dynamics in the sense of Lyapunov concepts. Measured and estimated state variables can be exploited to perform a state feedback control of the machine system. The simulation results are presented to illustrate the effectiveness of the proposed approach for nonlinear observer design.     

Prediction and optimization of electroplated Ni-based coating composition and thickness using central composite design and artificial neural network

Journal of Applied Electrochemistry - The choice of the electroplating conditions of Ni-based alloys has always been a serious research question. In this study, an artificial neural network based...     

Correction to: Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model

Wireless Personal Communications -     

Dynamic and scalable multi-level trust management model for Social Internet of Things

The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by offering them the ability to connect to the Internet and communicate. Integrating the social component into IoT gave rise to the Social Internet of Things (SIoT), which has helped overcome various issues such as heterogeneity and navigability. In this kind of environment, participants compete to offer a variety of attractive services. Nevertheless, some of them resort to malicious behaviour to spread poor-quality services. They perform so-called Trust-Attacks and break the basic functionality of the system. Trust management mechanisms aim to counter these attacks and provide the user with an estimate of the trust degree they can place in other users, thus ensuring reliable and qualified exchanges and interactions. Several works in literature have interfered with this problem and have proposed different Trust-Models. The majority tried to adapt and reapply Trust-Models designed for common social networks or peer-to-peer ones. That is, despite the similarities between these types of networks, SIoT ones present specific peculiarities. In SIoT, users, devices and services are collaborating. Devices entities can present constrained computing and storage capabilities, and their number can reach some millions. The resulting network is complex, constrained and highly dynamic, and the attacks-implications can be more significant. In this paper, we propose DSL-STM a new dynamic and scalable multi-level Trust-Model, specifically designed for SIoT environments. We propose multidimensional metrics to describe and SIoT entities behaviours. The latter are aggregated via a Machine Learning-based method, allowing classifying users, detecting attack types and countering them. Finally, a hybrid propagation method is suggested to spread trust values in the network, while minimizing resource consumption and preserving scalability and dynamism. Experimentation made on various simulated scenarios allows us to prove the resilience and performance of DSL-STM.

     

Applying Dynamic Training‐Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training‐subset selection methods. These methods manipulate the training data in order to improve the out‐of‐sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out‐of‐sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training‐subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non‐fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive‐random training‐subset selection method applied to the whole set of training samples.     

The effect of daily-activity patterns on crash involvement

The main purpose of this study is to analyze the effect of daily-activity and travel patterns on the risk of crash involvement. To this end, we develop a model that integrates daily-activity and travel choices in a single framework, recognizing that these variables affect the risk of crashes. This model can therefore provide predictions of the expected changes in risk levels from the implementation of measures that affect the daily-activity patterns and the socio-economic characteristics of the population.The empirical analysis makes use of data collected during a household survey that includes crash information and trip diaries. The model is applied in a case study of an Arab town in Israel to analyze various transportation policies. The results of this research show that in addition to individuals’ demographic and socio-economic characteristics, their daily-activity and travel patterns also have an impact on the risk of being involved in car crashes. The case study showed the potential of this framework for analyzing the effect of various social and transportation policies on road safety. To the best of our knowledge, this is the first time such relationships have been tested by using a disaggregate model and the first time activity-based models have been used to analyze exposure to the risk of road crashes.     

Physicochemical properties of fermented Arabian mares' milk

Due to its numerous benefits, including health-promoting characteristics, there is increasing interest in finding product applications for mares' milk. In the present study, the physicochemical properties, turbidity and conductivity of Arabian mares' milk were studied during acidification by fermentation at 30 °C and compared with those of bovine milk. The data for the acidification rate were modelled using a logistic equation. Mares' milk showed a longer latency phase (284 min) and lower acidification rate (μ_max = 0.0052 dpH min^?1) compared with bovine milk (194 min and 0.0098 dpH min^?1 respectively). The general shape of the curves of the turbidity versus time and pH were similar in nature for the acidification of both milks. However, the characteristic points were different, due to the differences in casein micelle composition. Measurement of electrical conductivity during acidification showed that maximum demineralization of casein micelles started around pH 6.09 and 5.31, for mares' and bovine milk, respectively. In conclusion, the technological characteristics of mares' milk were very different from those of bovine milk, due to intrinsic physicochemical properties of both milks.     

Corrosion of tin in aqueous solutions of mono-, Di- and trichloroacetic acids

The corrosion behaviour of tin in stagnant mono-, di- and trichloroacetic acids solutions in the pH range 1–6 and at concentrations 4.0 to 5 × 10^?4 M was investigated. The results indicate behaviour that is generally the same but there is some dependence on the acid concentration and the pH value. In 4.0 to 10^?2 M solutions, the corrosion rate (W) increased with increasing acid concentration and decreasing pH value from 1 to 4 as follows: log W=a+b log C, where b=0.70, 0.42 and 0.35 for tri-, di- and mono-chloroacetic acids respectively. At high concentrations 4.0 to 10^?2 M and in the pH range 1–6, the steady state corrosion potential shifted in the negative direction with increase of acid concentration accompanied by an increase in the corrosion rate, indicating that the corrosion process becomes anodically controlled by the complexing of Sn²⁺ ions with organic acid anions and that the order of aggressiveness is mono-<di-<trichloroacetic acids. In dilute solutions (10^?2 to 5.10^?4 M) in the pH range 1–6 the steady state potential shifted in the noble direction with increase of acid concentration (accompanied by a remarkable decrease in the corrosion rate). Corrosion inhibition in dilute solutions was attributed to film formation on the surface of tin which may result from the hydrolysis of tin species.