Superparamagnetic particle dynamics and mixing in a rotating capillary tube with a stationary magnetic field

The dynamics of superparamagnetic particles subject to competing magnetic and viscous drag forces have been examined with a uniform, stationary, external magnetic field. In this approach, competing drag and magnetic forces were created in a fluid suspension of superparamagnetic particles that was confined in a capillary tube; competing viscous drag and magnetic forces were established by rotating the tube. A critical Mason number was determined for conditions under which the rotation of the capillary prevents the formation of chains from individual particles. The statistics of chain length was investigated by image analysis while varying parameters such as the rotation speed and the viscosity of the liquid. The measurements showed that the rate of particle chain formation was decreased with increased viscosity and rotation speed; the particle dynamics could be quantified by the same dimensionless Mason number that has been demonstrated for rotating magnetic fields. The potential for enhancement of mixing in a bioassay was assessed using a fast chemical reaction that was diffusion-limited. Reducing the Mason number below the critical value, so that chains were formed in the fluid, gave rise to a modest improvement in the time to completion of the reaction.     

The thermal stability of AlN

The thermal stability of AlN powders and thin films has been investigated using reflection high-energy electron diffraction (RHEED) and X-ray diffraction. AlN powder was treated thermally and chemically to assess the oxidation resistance of this compound and to identify the phases formed. The results show that AlN is stable up to 1000° C in air and remains stable up to 1400° Cin vacuo. -AIOOH is formed when AlN is treated with water at 100° C but AlN does not react readily with atmospheric moisture at room temperature. The thermal stability of thin films of AlN on GaAs has been evaluated at temperatures between 900 and 1100° C in a nitrogen atomosphere. It was found that AlN did not oxidize under these conditions. Pure AlN is a suitable encapsulant for GaAs at high annealing temperatures in an inert atmosphere.     

Effects of combined wrist deviation and forearm rotation on discomfort score

The aim of the study was to examine the pattern of the change in discomfort for combined wrist deviation and forearm rotation as joint angles increased away from neutral in a repetitive task. There were five levels of wrist deviation (neutral, 35% and 55% of the range of motion (ROM) in radial and ulnar deviation) and five levels of forearm rotation (neutral, 30% and 60% of the ROM in pronation and supination). Twenty-five participants performed a repetitive flexion task with a force of 10 N +/- 1 N at a frequency of 15 exertions per min, with replication after 1 week for six of the participants. A visual analogue scale was used for recording the discomfort scores. Repeated measures analysis of covariance with the Greenhouse-Geisser correction, where necessary, was used on transformed values of the discomfort scores. Grip test endurance time at 50% of maximum voluntary contraction was included as a covariate. Wrist deviation (p = 0.007) and forearm rotation (p = 0.001) were found to have significant effects. Interactions of the main factors were not significant and nor was the covariate. Quadratic regression equations were derived and were used to generate iso-discomfort contours, which show a useful area of low discomfort around the central neutral zone of wrist postures, but with steep increases in discomfort at the extreme combinations of wrist ulnar/radial deviation with forearm pronation/supination. Discomfort equations and contours, showing wrist and forearm postures, which are either acceptable or potentially injurious, are useful for the design of industrial tools, machine controls and workspaces. Reference to these can help to reduce the risk of musculoskeletal injury associated with the tasks or tools by avoiding poor postures with unacceptable deviations from neutral posture.     

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.     

Polyamides incorporating furan moieties. 5. Synthesis and characterisation of furan-aromatic homologues

2,2′-Bis (5-chloroformyl 2-furyl) propane and various aromatic diamines were used as monomers in the study of their interfacial polycondensation and the properties of the ensuing furanic-aromatic polyamides. The effects of such variables as the nature of the organic phase, the temperature, the reaction time, and the type and concentration of the catalyst were investigated as well as the properties of the polyamides in terms of structure, average chain length, T_g, T_m and thermal stability.     

Advanced modeling and control of wind conversion systems based on hybrid generators using fractional order controllers

The focus of this study is on modeling and management of a Wind Energy Conversion System (WECS) based on a Hybrid Excitation Synchronous Generator (HESG). Using a wind simulator, two controllers, CRONE and H_∞, are evaluated for Maximum Power Point Tracking (MPPT) and optimal rotation speed. The results show the CRONE controller's higher tracking capability, and robustness tests investigate the influence of parametric uncertainty. Space harmonic effects, commutation effects, and turbine shaft flexibility are all addressed in the sophisticated models. The introduction of Fractional Order Proportional-Integral (FOPI) control for MPPT is a game changer. Although fractional calculus-based accuracy and robustness are uncommon in WECS, they show promise for emissions reduction and increased energy efficiency. This work validates a dependable control approach inside an isolated HESG-based WECS's power-maximizing range. Extensive investigation of the parameters impacting FOPI control efficiency yields useful insights for effective MPPT control. A variable-speed wind turbine (WT) is linked to a HESG through a multiplier, with a controller controlling generator coil excitation voltages and a rectifier connecting the WECS to a load. A thorough 3 kW HESG electrical model that includes generator space harmonics and converter commutation effects is among the contributions. For HESG-based WECS stability, frequency analysis finds important resonant and anti-resonant frequencies. These issues are addressed by a FOPI control technique, which ensures system stability and performance. The necessity of exact frequency analysis in HESG-based WECS is highlighted by a step-by-step controller parameter tuning approach.     

Synthesis and characterization of caprolactone based polyurethane with degradable and antifouling performance

In this work,a degradable polyurethane composed of caprolactone(CL)and L-Lactide(LLA)as soft seg-ments,and 4,4'-methylenebis(cyclohexyl isocyanate)(H12MDI)and polytetramethylene ether glycol(PTMEG)as hard segments,was prepared.Hydrolytic degradation experiment revealed that the degrad-able polyurethane(PU)could be degraded in artificial seawater.It also showed that caprolactone-co-polyurethane(CL-PU)copolymer with higher crystallinity degraded much slower in artificial seawater.However,the introduction of LLA resulted in an increase in the hydrophilicity and reduction in the crys-tallinity of degradable PU,as demonstrated by the contact angle analysis.The result of the scanning elec-tron microscope showed that the surface of degradable PU renewed under static condition.Moreover,degradable PU was able to be used as a carrier,and it controlled the release rate of 4,5-dichloro-2-octyl-isothiazolone(DCOIT).The anti-diatom(Navicula incerta)test demonstrated that the(caprolactone-co-L-lactide)-co-polyurethane 4(CL/LAx-PU4)with DCOIT contents prevented the adhe-sion of diatom Navicula incerta(88.37％reduction)due to their self-polishing and the release of antifou-lants.Therefore,the degradable PU consisted of CL,LLA,and DCOIT could be a durable resin with good antifouling activity for the application in the marine anti-biofouling field.     

A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera

Bus passenger flow calculation system is a critical part of the smart public transportation framework. Bus passenger flow information can help to make data statistics report of the passenger at a bus station which can be used by public transport operator to evaluate the quality of the transportation. Statistics report of crowded passengers in the bus station help managers to understand the bus transit operations, can provide the database for the intelligent transportation scheduling, help to provide more and better services for passengers, overall data statistics of passengers has important practical significance to improve public transport environment. This paper presents a passenger counting algorithm based on hybrid machine learning approach. In the first step, an advanced method is used to extract the Histogram of oriented gradients (HOG) feature of passenger’s heads. Classification of head features is done by using support vector machine (SVM) as a classifier for the liner model. Heads are detected successfully after performing all steps. In next step Kanade-Lucas-Tomasi (KLT) is used to reality head tracking, the multiple target tracking is achieved and the head motion trajectory of passenger target is captured stably. At last, the trajectory is analyzed and the automatic counting of bus passenger flow is realized. In the last step, the proposed algorithm is move to embedded system for practical implementation. In this paper, the algorithm intends to use ADSP-BF609 embedded platform for transplantation. The experimental results demonstrate that the statistical accuracy of the proposed algorithm is enhanced successfully; especially during the daytime with the good illustration, the effective counting of the passenger flow is achieved and the inward and outward passenger counting can be realized. In this paper three feature extraction models are used namely local binary patterns, histograms of oriented gradients and binarized statistical image in order to get accurate features. Furthermore, three common classification techniques including naïve bayes classifier, boosted tress and support vector machines are used for fine classification of extracted vectors obtained from different features extractors model. 94.50% accuracy is achieved when support vector machine (SVM) classifies the features extracted using Histogram of oriented gradients (HOG). SVM surpasses the accuracy obtained by Boosted tree namely 81.30% using Histogram of oriented gradients (HOG) features.

     

A survey on search results diversification techniques

The quantity of information placed on the web has been greater than before and is increasing rapidly day by day. Searching through the huge amount of data and finding the most relevant and useful result set involves searching, ranking, and presenting the results. Most of the users probe into the top few results and neglect the rest. In order to increase user’s satisfaction, the presented result set should not only be relevant to the search topic, but should also present a variety of perspectives, that is, the results should be different from one another. The effectiveness of web search and the satisfaction of users can be enhanced through providing various results of a search query in a certain order of relevance and concern. The technique used to avoid presenting similar, though relevant, results to the user is known as a diversification of search results. This article presents a survey of the approaches used for search result diversification. To this end, this article not only provides a technical survey of existing diversification techniques, but also presents a taxonomy of diversification algorithms with respect to the types of search queries.