Three‐dimensional computational comparison of mini pinned heat sinks using different nanofluids: Part one—the hydraulic‐thermal characteristics

The hydraulic‐thermal characteristics of 3D pinned heat sink designs have been numerically compared as the first part of a three‐part investigation. Five different pin geometries (circular, square, triangular, strip, and elliptic pins) and an unpinned heat sink with three types of nanofluids (Al₂O₃–H₂O, SiO₂–H₂O, and CuO–H₂O) are considered for laminar forced convection. The range of Reynolds number is from 100 to 1000, and volume fractions vary between 0% and 5%. The finite volume method is employed to solve the Navier–Stokes and energy equations by employing a SIMPLE algorithm for a computational solution. Three parameters are presented—the Nusselt number, the bottom temperature, and the hydrothermal performance of the heat sink with pressure drop data. The findings indicated that the overall hydrothermal performance of elliptic‐pinned (EP) heat sinks produces the most substantial value of 3.10 for pure water. For different nanofluids, the SiO₂–water nanofluids with EPs have the most significant hydrothermal performance. Also, this factor is enhanced with an increase in nanofluid concentration up to nearly 3.34 for 5% of SiO₂–water. Consequently, applying the elliptic‐pinned heat sinks is recommended with pure water for considering an increase in the pressure drop, with 5% of SiO₂–water nanofluids, regardless of an enlargement of pressure drop for heat‐dissipation applications.     

Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

Heifers with positive genetic merit for fertility traits reach puberty earlier and have a greater pregnancy rate than heifers with negative genetic merit for fertility traits

This study investigated the hypothesis that dairy heifers divergent in genetic merit for fertility traits differ in the age of puberty and reproductive performance. New Zealand's fertility breeding value (FertBV) is the proportion of a sire's daughters expected to calve in the first 42 d of the seasonal calving period. We used the New Zealand national dairy database to identify and select Holstein-Friesian dams with either positive (POS, +5 FertBV, n = 1,334) or negative FertBV (NEG, ?5% FertBV, n = 1,662) for insemination with semen from POS or NEG FertBV sires, respectively. The resulting POS and NEG heifers were predicted to have a difference in average FertBV of 10 percentage points. We enrolled 640 heifer calves (POS, n = 324; NEG, n = 316) at 9 d ± 5.4 d (± standard deviation; SD) for the POS calves and 8 d ± 4.4 d old for the NEG calves. Of these, 275 POS and 248 NEG heifers were DNA parent verified and retained for further study. The average FertBV was +5.0% (SD = 0.74) and ?5.1% (SD = 1.36) for POS and NEG groups, respectively. Heifers were reared at 2 successive facilities as follows: (1) calf rearing (enrollment to ～13 wk of age) and (2) grazier, after 13 wk until 22 mo of age. All heifers wore a collar with an activity sensor to monitor estrus events starting at 8 mo of age, and we collected weekly blood samples when individual heifers reached 190 kg of body weight (BW) to measure plasma progesterone concentrations. Puberty was characterized by plasma progesterone concentrations >1 ng/mL in at least 2 of 3 successive weeks. Date of puberty was defined when the first of these samples was >1 ng/mL. Heifers were seasonally bred for 98 d starting at ～14 mo of age. Transrectal ultrasound was used to confirm pregnancy and combined with activity data to estimate breeding and pregnancy dates. We measured BW every 2 wk, and body condition and stature at 6, 9, 12, and 15 mo of age. The significant FertBV by day interaction for BW was such that the NEG heifers had increasingly greater BW with age. This difference was mirrored with the significant FertBV by month interaction for average daily gain, with the NEG heifers having a greater average daily gain between 9 and 18 mo of age. There was no difference in heifer stature between the POS and NEG heifers. The POS heifers were younger and lighter at puberty, and were at a lesser mature BW, compared with the NEG heifers. As a result, 94 ± 1.6% of the POS and 82 ± 3.2% of the NEG heifers had reached puberty at the start of breeding. The POS heifers were 20% and 11% more likely to be pregnant after 21 d and 42 d of breeding than NEG heifers (relative risk = 1.20, 95% confidence interval of 1.03–1.34; relative risk = 1.11, 95% confidence interval of 1.01–1.16). Results from this experiment support an association between extremes in genetic merit for fertility base on cow traits and heifer reproduction. Our results indicate that heifer puberty and pregnancy rates are affected by genetic merit for fertility traits, and these may be useful phenotypes for genetic selection.     

Simultaneously achievement of high recoverable energy density and efficiency in sodium niobate-based ceramics

Journal of Materials Science: Materials in Electronics - Single lead-free Na0.73Bi0.09(Nb1???xTax)O3 (x?=?0, 0.10, 0.20, 0.30, and 0.40) ceramic phases were processed...     

Use of solution crystallization analysis by laser light scattering for studying the solution crystallization of various Ziegler–Natta‐catalysed polypropylenes

Solution crystallization analysis by laser light scattering (SCALLS) involves the observation of the scattering of diode mercury laser lamp light after it passes through a polymer solution. An increase in turbidity occurs when the hot polymer solution is cooled and the polymer starts to crystallize out of solution. This causes a decrease in the amount of laser light that can pass through the solution and an increase in the amount of scattered light. The reverse of this process leads to the turbidity decreasing with an increase in temperature. According to this concept, it is possible to follow the solution crystallization of various polypropylenes under controlled cooling. In this study, SCALLS was able to differentiate between different isotactic and syndiotactic polypropylenes with similar chemical structures, but different tacticity and molecular weights. Furthermore, SCALLS provided good crystallization information that is similar to that from crystallization analysis fractionation and temperature rising elution fractionation. In addition, SCALLS can be used as a quantitative tool for the measurement of weight fractions during dissolution. © 2014 Society of Chemical Industry     

Banana peel: an effective biosorbent for aflatoxins

This work reports the application of banana peel as a novel bioadsorbent for in vitro removal of five mycotoxins (aflatoxins (AFB1, AFB2, AFG1, AFG2) and ochratoxin A). The effect of operational parameters including initial pH, adsorbent dose, contact time and temperature were studied in batch adsorption experiments. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and point of zero charge (pH_pzc) analysis were used to characterise the adsorbent material. Aflatoxins’ adsorption equilibrium was achieved in 15 min, with highest adsorption at alkaline pH (6–8), while ochratoxin has not shown any significant adsorption due to surface charge repulsion. The experimental equilibrium data were tested by Langmuir, Freundlich and Hill isotherms. The Langmuir isotherm was found to be the best fitted model for aflatoxins, and the maximum monolayer coverage (Q₀) was determined to be 8.4, 9.5, 0.4 and 1.1 ng mg^?1 for AFB1, AFB2, AFG1 and AFG2 respectively. Thermodynamic parameters including changes in free energy (ΔG), enthalpy (ΔH) and entropy (ΔS) were determined for the four aflatoxins. Free energy change and enthalpy change demonstrated that the adsorption process was exothermic and spontaneous. Adsorption and desorption study at different pH further demonstrated that the sorption of toxins was strong enough to sustain pH changes that would be experienced in the gastrointestinal tract. This study suggests that biosorption of aflatoxins by dried banana peel may be an effective low-cost decontamination method for incorporation in animal feed diets.     

Non-linear diffusion of image noise with minimal iterativity

Non-linear diffusion (ND) is an iterative difference equation used in several image processing applications such as denoising, segmentation, or compression. The number of iterations required to achieve optimal processing can be very high, making ND not suitable for real-time requirements. In this paper, we study how to reduce complexity of ND so as to achieve minimal number of iterations for real-time image denoising. To do this, we first study the relations between parameters of the iterative equation: the number of iterations, the time step, and the edge strength. We then proceed by estimating the minimally required number of iterations to achieve effective denoising. Then, we relate the edge strength to the number of iterations, to noise, and to the image structure. The resulted minimal iterativity ND is very fast, while still achieves similar or better noise reduction compared to related ND work. This paper also shows how the proposed spatial filter is suitable for structure-sensitive object segmentation and temporal noise reduction.     

Modeling moisture sorption isotherms in roasted green wheat using least square regression and neural-fuzzy techniques

Roasted green wheat at moisture content from 0.052 to 0.25 (decimal d.b.) and temperatures from 25 to 43 °C was used to model moisture sorption isotherms using conventional non-linear least square regression (NLR) and neural-fuzzy (NF) techniques. The results showed that neural-fuzzy techniques provided a better fit than conventional least square regression with: R² = 0.99 and 0.97, RMSE = 0.01 and 0.0038, E% = 1.01 and 5.9 and SSE = 0.0008 and 0.009, for NF and NLR techniques, respectively. Differential enthalpy decreased from 477.9 kJ/kg at 0.052 (decimal d.b. mc) to 44.7 kJ/kg at 0.25 (decimal d.b. mc) and entropy decreased from 1.16 kJ/kg K at 0.052 (decimal d.b. mc) to 0.014 kJ/kg K at 0.25 (decimal d.b. mc). A linear plot between enthalpy and entropy showed that compensation exists. The isokinetic temperature T_β was 376.13 K which was larger than the harmonic mean temperature T_hm = 307.31 K, showing that the water sorption was entropy-driven. The free energy change ΔG was positive (+38.42 kJ/kg) indicating a non-spontaneous water sorption process.     

A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers

This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO).     

Development of planning level transportation safety tools using Geographically Weighted Poisson Regression

A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models.