A theoretical evaluation of the effect of water on the electronic properties of low density amorphous carbon nanoparticles

Monte Carlo simulations combined with first-principles calculations were carried out to investigate the structure and electronic properties of amorphous carbon nanoparticles containing between 287 and 467 atoms, isolated and in an aqueous environment. Carbon interactions were described in terms of the semiemperical Tersoff potential and the interactions of the nanoparticles with water were described by an all-atom Lennard–Jones potential. The nanostructures were generated from low mass density amorphous carbon phases simulated under ambient conditions. Our study has indicated that the formation energy of these nanomaterials is slightly affected by the initial configuration of the amorphous phase. The large number of dangling bonds present in the nanostructures leads to huge magnetic moments, in a 31–42 μ_B range, calculated for the amorphous nanostructures containing distinct sp²/sp³ ratios. Also, these nanoparticles exhibit strong interaction energies with the aqueous environment, which are in agreement with the high reactivity expected for these amorphous systems. Most importantly, the calculated magnetism appears to be reduced between 1% and 14% in the presence of water, depending on both the nanoparticle density and surface.     

Response of Corn Seedlings (Zea mays L.) to Different Concentrations of Nitrogen in Absence and Presence of Silicon

Silicon - Corn plants are highly demanding of nitrogen and the application of silicon has been studied because it minimizes stress from different natures, and for the better utilization of some...     

Scanner Digital Images Combined with Color Parameters: A Case Study to Detect Adulterations in Liquid Cow’s Milk

This study presents a proposition of a low-cost screening method for detection and quantification of adulterations in liquid cow’s milk samples. The studied adulterations were made with water and NaOH. Digital images from the adulterated samples were obtained using a flatbed scanner, and the means of ten color parameters were used to evaluate the information from images: red, green, blue, hue, saturation, value, relative colors (r, g, and b), and intensity. Regression models for water quantification were proposed using multiple linear regression (MLR), principal components regression (PCR), and partial least squares (PLS). The best models were obtained using PCR and PLS, with root mean square error for prediction smaller than 7%. These results were compared with near-infrared (NIR), and the prediction capability was very similar. In the case of adulterations with NaOH, the colors B, S, g, and b presented the highest differences between fresh and adulterated milk samples.     

Effects of supplemental progesterone after artificial insemination on expression of interferon-stimulated genes and fertility in dairy cows

The objectives of the current study were to evaluate the effects of supplemental progesterone after artificial insemination (AI) on expression of IFN-stimulated genes (ISG) in blood leukocytes and fertility in lactating dairy cows. Weekly cohorts of Holstein cows were blocked by parity (575 primiparous and 923 multiparous) and method of insemination (timed AI or AI on estrus) and allocated randomly within each block to untreated controls, a controlled internal drug release (CIDR) containing 1.38 g of progesterone from d 4 to 18 after AI (CIDR4), or a CIDR on d 4 and another on d 7 after AI and both removed on d 18 (CIDR4+7). Blood was sampled to quantify progesterone concentrations in plasma and mRNA expression in leukocytes for the ubiquitin-like IFN-stimulated gene 15-kDa protein (ISG15) and receptor transporter protein-4 (RTP4) genes. Pregnancy was diagnosed on d 34 ± 3 and 62 ± 3 after AI. Treatment increased progesterone concentrations between d 5 and 18 after AI in a dose-dependent manner (control = 3.42, CIDR4 = 4.97, and CIDR4+7 = 5.46 ng/mL). Cows supplemented with progesterone tended to have increased luteolysis by d 19 after AI (control = 17.2; CIDR4 = 29.1; CIDR4+7 = 30.2%), which resulted in a shorter AI interval for those reinseminated after study d 18. Pregnancy upregulated expression of ISG in leukocytes on d 19 of gestation, but supplementing progesterone did not increase mRNA abundance for ISG15 and RTP4 on d 16 after insemination and tended to reduce mRNA expression on d 19 after AI. For RTP4 on d 19, the negative effect of supplemental progesterone was observed only in the nonpregnant cows. No overall effect of treatment was observed on pregnancy per AI on d 62 after insemination and averaged 28.6, 32.7, and 29.5% for control, CIDR4, and CIDR4+7, respectively. Interestingly, an interaction between level of supplemental progesterone and method of AI was observed for pregnancy per AI. For cows receiving exogenous progesterone, the lower supplementation with CIDR4 increased pregnancy per AI on d 62 in cows inseminated following timed AI (CIDR4 = 39.2; CIDR4+7 = 27.5%); in those inseminated following detection of estrus, however, the use of a second insert on d 7 resulted in greater pregnancy per AI (CIDR4 = 26.9; CIDR4+7 = 31.5%). Pregnancy loss did not differ among treatments. Supplemental progesterone post-AI using a single intravaginal insert on d 4 was beneficial to pregnancy in cows inseminated following timed AI, but incremental progesterone with a second insert on d 7 did not improve fertility of dairy cows.     

Assessment of resistance spot welding quality based on ultrasonic testing and tree-based techniques

Classification and regression tree (CART) and random forest techniques were proposed as pattern recognition tools for classification of ultrasonic oscillograms of resistance spot welding (RSW) joints. The results showed that CART models produced an acceptable error rate with high interpretability. These features may be used to understand and control the decision processes, instruct other human operators, compare margins of safety or modify them depending on the criticality of the industrial process. Compared with CART trees, random forests reduced the error rate at the cost of decreasing decision interpretability. The use of the agreement of the forest was proposed as a measure to reduce the workload of human operators, who would only have to focus on the analysis of ultrasonic oscillograms that are difficult to interpret.     

Endotoxin levels in settled airborne dust in European schools: the HITEA school study

Indoor exposure to microbial agents is known to influence respiratory health. Besides home exposure, exposure in schools can affect respiratory health. In this study, we measured endotoxin in settled dust in primary schools in three European countries from three different geographical regions with different climates. Our aim was to characterize endotoxin levels in primary schools and evaluate associations with potential determinants. Endotoxin levels were repeatedly assessed in 23 schools in Spain (n = 7), the Netherlands (n = 10), and Finland (n = 6) using electrostatic dustfall collectors. In total, 645 measurements were taken in 237 classrooms. Endotoxin levels differed significantly between countries; Dutch schools had the highest levels, while Finnish schools showed the lowest levels. In each country, differences in endotoxin levels were observed between schools and over the sampling periods. Estimates improved after adjustment for sampling period. Factors affecting endotoxin levels in a school differed per country. In general, endotoxin levels were higher in lower grades and in classrooms with higher occupancy. School endotoxin levels may contribute significantly to total endotoxin exposure in children and teachers. As the correlation between the repeated measurements is reasonable, single endotoxin measurements form a reasonable basis for estimating annual endotoxin levels in schools.