How believable are published laboratory data? A deeper look into system-compliance and elastic modulus

Elastic modulus(E) interpretation is debatable with limited literature detailing the impact of systemcompliance.To address this impact,a comprehensive testing schedule using an aluminium 6061(Al)sample is carried out on several systems under various test setups.Al is chosen as it is extruded and adheres to well defined shape tolerances and elastic properties.A robust method,using the SavitzkyGolay filter,is introduced to identify significant slope changes in the stress-strain curve.Since the load in the test system is well defined,the recorded deformation is corrected to the expected value of Al resulting in a system-compliance factor.The results across the testing systems and test setups showed significant variance,with the recorded E always lower than the anticipated E_(Al).The number of components within the system over which the deformation is measured had the most significant impact,lowering the expected E by up to 50%.Additionally,the system-compliance factor is inconsistent across different systems and setups.Thus,it is evidently proved that each setup must be separately evaluated for its system-compliance and that no single value exists across systems and setups.The findings are then projected onto a series of uniaxial compressive strength(UCS) tests carried out on Stanstead granite(SS GR) samples.The corrected E_(t50) and E_(avg) values for system-compliance of the samples are within ±1%for each system as opposed to being ±50% pre-correction.The findings conclude that it is deemed necessary and of utmost importance that the deformation be corrected to accommodate the systemcompliance to obtain reliable results.     

Data Association in Multi-target Tracking Using Belief Function

In this paper, we present a new method for data association in multi-target tracking. The representation and the fusion of the information in our method are based on the use of belief function. The proposal generates the basic belief mass assignment using a modified Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses. Our method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases. The results demonstrate the feasibility of the proposal and show improved performance compared to the aforementioned alternative commonly used methods.     

A new preference scale mcdm method based on interval-valued intuitionistic fuzzy sets and the analytic hierarchy process

The intuitionistic fuzzy analytic hierarchy process (IF-AHP), in which intuitionistic fuzzy numbers are utilized in defining decision makers’ linguistic judgment, has been used to solve various multi-criteria decision-making problems. Previous theories have suggested that interval-valued intuitionistic fuzzy numbers (IVIFN) with hesitation degree can act as alternative fuzzy numbers that can handle vagueness and uncertainty. This paper proposes a new preference scale in the framework of the interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP). The comparison matrix judgment is expressed in IVIFN with degree of hesitation. The proposed new preference scale concurrently considers the membership function, the non-membership function and the degree of hesitation of IVIFN. To define the weight entropy of the aggregated matrix of IVIFN, a modified interval-valued intuitionistic fuzzy weighted averaging is proposed, by considering the interval number of the hesitation degree. Three multi-criteria decision-making problems are used to test the proposed method. A comparison of the results is also presented to check the feasibility of the proposed method. It is shown that the ranking order of the proposed method is slightly different from that of the other two methods because of the inclusion of the hesitation degree in defining the preference scale.     

Physiological responses and lactational performances of late-lactation dairy goats under heat stress conditions

Eight Murciano-Granadina dairy goats in late lactation were exposed to different ambient conditions, using metabolic cages in a climatic chamber. The experimental design was a crossover (2 periods of 35 d and 4 goats each) and conditions were (1) thermal neutral (TN; 15 to 20°C day-night) and (2) heat stress (HS; 12-h day at 37°C and 12-h night at 30.5°C). Humidity was maintained at 40% and light-dark was constant (12–12 h). The forage:concentrate ratio was adjusted daily for maintaining similar value in TN and HS goats (70:30). Water was freely available at ambient temperature. Rectal temperature and respiratory rate (0800, 1200 and 1700 h) and milk yield were recorded daily, whereas milk composition, nonesterified fatty acids and haptoglobin in blood were analyzed weekly. At d 25, additional blood samples were taken for analysis of metabolites and indicators of the acid-base balance. Digestibility coefficients and N balance were determined (d 31 to 35) and body weight was recorded (d 35). Compared with TN goats, HS goats experienced greater rectal temperature (+0.58°C), respiratory rate (+48 breaths/min), water intake (+77%) and water evaporation (+207%). Intake of HS goats rapidly declined until d 7 (−40%), partially recovered from d 7 to 19, and steadied thereafter (−14%). No changes in digestibility or N balance were detected. Blood nonesterified fatty acids and haptoglobin peaked at d 7 in HS goats but did not vary thereafter. Although milk yield did not vary by treatment, milk of HS goats contained −12.5% protein and −11.5% casein than TN goats. Panting reduced concentration and pressure of CO₂ in the blood of HS goats, but they were able to maintain their blood pH similar to the TN group by lowering HCO₃⁻ and increasing Cl⁻ concentrations in their blood. In conclusion, HS dairy goats showed dramatic physiological changes during the first week of treatment and partially recovered thereafter. They were able to maintain milk yield by losing body mass, but milk protein content and protein yield were depressed. Further research is needed to assess the response of dairy goats to HS at earlier stages of lactation.     

Experimental design to optimize preparation of activated carbons for use in water treatment

A series of seven activated carbons was obtained for use in drinking water treatments by steam-activation of olive-waste cakes. This raw material is an abundant and cheap waste byproduct of oil production, making these activated carbons economically feasible. The activated carbons, prepared by the one step method, were characterized, and the evolution of their characteristics (yield, adsorption capacities, and porosity) was analyzed as a function of the experimental parameters (activation temperature and activation time), using the Doehlert matrix. The Doehlert matrix allows the response surface to be studied with a good quality parameter estimation of the quadratic model. Each response has been described by a second order model that was adequate to predict responses in all experimental regions. The coefficients of the postulated model were calculated from the experimental responses by means of least squares regression, using the NEMROD software. We determined the region in which the optimum values of both activation temperature and activation time were achieved for the preparation of activated carbons suitable for use in water treatments. The "optimal activated carbon" was experimentally obtained, and its characteristic parameters showed a good agreement with those calculated from the model. The results obtained for activated carbons prepared by the one-step method were compared with those for activated carbons prepared by the two-step method. The characteristics of activated carbons obtained by the one-step and two-step methods showed that "one-step" activated carbons have a highly developed porous texture formed mainly of large macropores and micropores, whereas "two-step" activated carbons have a predominance of mesopores and narrow micropores. These activated carbons from olive-waste cakes showed a high capacity to adsorb herbicides (2,4-dichlorophenoxyacetic acid, 2,4-D; and 2-methyl, 4-chlorophenoxyacetic acid, MCPA) from water, with adsorption capacity values higher than those corresponding to a commercial activated carbon used from drinking water treatments.     

Topotactic transformation of (T-T’) La1.8Nd0.2CuO4: Synthesis,structure, electrical properties and oxygen diffusion pathways simulation

The starting sample T-La_1.8Nd_0.2CuO₄ (SG: Bmab) has been synthesized by the conventional solid-state reaction at 900 °C. The T′-La_1.8Nd_0.2CuO₄ (SG: I4/mmm) sample has been obtained via a topotactic reduction reaction of T-La_1.8Nd_0.2CuO₄ (SG: Bmab) using CaH₂ (as reductor) followed by an oxidation at 400 °C in air. The temperature of the phase transition (T-T′) has been determined using the differential thermal analysis (DTA) and the temperature-dependent powder X-ray diffraction (TDXD). The crystal structures of the title compounds have been refined by using the Rietveld method and confirmed by the means of the charge distribution model (CHARDI). The electrical properties of the title compounds have been studied by impedance complex spectroscopy between 200 °C and 650 °C. This study shows two slopes in the Arrhenius plot with experimental activation energies 1.033 eV and 1.657 eV which correspond to the reduced phase and the T′ phase respectively. The simulation of oxygen diffusion in structures by using the Bond Valence Site Energy (BVSE) method shows three-dimensional pathways of oxygen diffusion. The calculated activation energies of the T′ and T structure are 1.619 and 2.369 eV, respectively.     

Widespread infection of lake whitefish Coregonus clupeaformis with the swimbladder nematode Cystidicola farionis in northern lakes Michigan and Huron

We estimated the prevalence, intensity, and abundance of swimbladder nematode infection in 1281 lake whitefish (Coregonus clupeaformis) collected from four sites in northern lakes Huron (Cheboygan and DeTour Village) and Michigan (Big Bay de Noc and Naubinway) from fall 2003 through summer 2006. Morphological examination of nematode egg, larval, and mature stages through light and scanning electron microscopy revealed characteristics consistent with that of Cystidicola farionis Fischer 1798. Total C. farionis prevalence was 26.94%, while the mean intensity and abundance of infection was 26.72 and 7.21 nematodes/fish, respectively. Although we detected C. farionis in all four stocks that were examined, Lake Huron stocks generally had higher prevalence, intensity, and abundance of infection than Lake Michigan stocks. A distinct seasonal fluctuation in prevalence, abundance, and intensity of C. farionis was observed, which does not coincide with reported C. farionis development in other fish species. Lake whitefish that were heavily infected with C. farionis were found to have thickened swimbladder walls with deteriorated mucosa lining, which could affect swimbladder function. Whether C. farionis infection may be negatively impacting lake whitefish stocks in the Great Lakes is unclear; continued monitoring of C. farionis infection should be conducted to measure responses of lake whitefish stocks to infection levels.     

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.