Design,Modeling and Control of a Simulator of an Aircraft Maneuver in the Wind Tunnel Using Cable Robot

International Journal of Control, Automation and Systems - In this paper, a new controllable simulator is proposed and modeled by which, experimental tests of the aircraft’s models can be...     

低密度微泡沫压井液的研究与应用

吐哈油田部分区块地层压力系数小于0.9,常规水基压井液对储层伤害大,若使用油基压井液成本高,环境污染严重。通过实验优选出了一种低密度水基微泡沫压井液。该压井液具有密度低、泡沫强度高、稳定性好、携砂能力强等优点。现场应用表明,低密度微泡沫压井液稳定时间大于48 h,密度在0.70~0.99 g/cm3之间可调,抗油污染能力强,抗油大于8%,抗温在100℃以上,岩心污染后渗透率恢复值大于80%;并且施工方便,成本低,具有储层保护能力,使用微泡沫压井液的井表皮系数在0.20~2.34之间。     

台北凹陷小草湖次凹油气成藏特点及有利区带

吐哈盆地小草湖次凹发育多套烃源岩，油源条件好，油气资源丰富，油气勘探潜力巨大，中侏罗统发育三套储盖组合，凹陷内油气藏类型主要为侏罗系三间房组，七克台组的构造－岩性复合型和侏罗系西山窑组的深盆气藏，燕山运动末期是小草湖次凹一次重要的成藏期，古构造，古构造背景和构造－岩性复合型圈闭发育是小草湖次凹成藏主控因素，根据小草湖次凹成藏主控因素评价和优选有利区带，勘探工作取得了良好效果。     

Amifostine (WR-2721) shortens the engraftment period of 4-hydroperoxycyclophosphamide-purged bone marrow in breast cancer patients receiving high-dose chemotherapy with autologous bone marrow support

4-Hydroperoxycyclophosphamide (4-HC), a commonly used marrow-purging agent, is active against many tumors, but is also toxic to normal marrow progenitors. Amifostine (WR-2721) is a sulfhydryl compound with chemoprotectant activity. Preclinical studies using suspensions of bone marrow and breast cancer cells demonstrated that ex vivo treatment with amifostine followed by 4-HC resulted in protection of marrow progenitors, with no compromise in the antitumor effect of 4-HC. This fact stimulated the development of a clinical trial. Bone marrow was harvested from 15 poor-prognosis breast cancer patients and randomly assigned to ex vivo treatment with amifostine followed by 4-HC (amifostine + 4-HC), or treatment with 4-HC alone. High-dose chemotherapy was then administered followed by infusion of the purged autologous bone marrow support (ABMS). Leukocyte engraftment, defined as a white blood cell count > or = 1 x 10(9)/L, was achieved in an average of 26 days for patients whose marrow was purged with amifostine + 4-HC versus 36 days for patients whose marrow was purged with 4-HC alone (P = .032). The average number of platelet transfusions (12 v 29; P = .017) and days of antibiotic therapy (28 v 40; P = .012) were significantly less for patients whose marrow was exposed to amifostine + 4-HC, compared with 4-HC alone. Unpurged backup marrow fractions were infused into three patients whose marrow was purged with 4-HC alone, because of inadequate marrow recovery. None of the patients who received amifostine + 4-HC-purged marrow required a backup marrow fraction. Complete remissions were achieved in 83% of patients with measurable disease, with no difference between the two cohorts. Forty-three percent of patients remained alive and progression-free at a mean of 13 months posttransplant. There was no significant difference in the rate or pattern of relapse for patients whose marrow was purged with amifostine + 4-HC compared with those whose marrow was purged with 4-HC alone. Ex vivo treatment of marrow with amifostine significantly shortens the time to marrow recovery, thereby reducing the risk of myelosuppressive complications in breast cancer patients receiving high-dose chemotherapy and 4-HC-purged ABMS. Since supportive care requirements are also significantly decreased, amifostine may reduce the cost of such therapy.     

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.