Application of high hydrostatic pressure to decontaminate green onions from Salmonella and Escherichia coli O157:H7

Consumption of fecally contaminated green onions has been implicated in several major outbreaks of foodborne illness. The objectives of this study were to investigate the survival and growth of Salmonella and Escherichia coli O157:H7 in green onions during storage and to assess the application of high hydrostatic pressure (HHP) to decontaminate green onions from both pathogens. Bacterial strains resistant to nalidixic acid and streptomycin were used to inoculate green onions at low (∼1 log cfu/g) and high (∼2 log cfu/g) inoculum levels which were then kept at 4 or 22 °C for up to 14 days. Both pathogens grew to an average of 5-6 log cfu/g during storage at 22 °C and the bacterial populations were fairly stable during storage at 4 °C. High-pressure processing of inoculated green onions in the un-wetted, wetted (briefly dipped in water) or soaked (immersed in water for 30 min) conditions at 250-500 MPa for 2 min at 20 °C reduced the population of Salmonella and E. coli O157:H7 by 0.6 to >5 log cfu/g, depending on the pressure level and sample wetness state. The extent of pressure inactivation increased in the order of soaked > wetted > un-wetted state. The pressure sensitivity of the pathogens was also higher at elevated treatment temperatures. Overall, after pressure treatment at 400-450 MPa (soaked) or 450-500 MPa (wetted) for a retention time of 2 min at 20-40 °C, wild-type and antibiotic-resistant mutant strains of Salmonella and E. coli O157:H7 inoculated on green onions were undetectable immediately after treatment and throughout the 15-day storage at 4 °C. The pressure treatments also had minimal adverse impact on most sensorial characteristics as well as on the instrumental color of chopped green onions. This study highlights the promising applications of HHP to minimally process green onions in order to alleviate the risks of Salmonella and E. coli O157:H7 infections associated with the consumption of this commodity.     

Analysis of energy consumption in Finland based on the selected economics indicators

Energy is one of the main inputs of productions. Therefore, national policies have emphasised on accessibility, availability, and affordability of energy supply. This is very important for energy importers, in particular countries with high dependency on industrial products. Finland is one of such countries that has high-energy consumption due to its cold climate, energy-intensive industries, and low population density. Therefore, security of energy supply to respond domestic consumption is one of the main concerns of the Finnish government. To have a resilience energy supply, it is necessary to understand the energy system in Finland. Energy modelling is a tool that helps policy-makers and researchers to understand the fluctuations and changes in the energy system. This research is to investigate the relationship between energy consumption and selected macro-economic factors in Finland. Therefore, energy consumption in each sector is analysed and the importance of renewable energies is also considered. Finally, a regression model is presented to predict energy consumption.     

Evaluation of the Interface Accuracy for Power Hardware-in-the-Loop Experiments

Selecting an accurate interface algorithm is a primary goal in order to have a successful operation of power hardware-in-the-loop. The purpose of this paper is to utilize the modified damping impedance method for testing the accuracy of the interface algorithm in power hardware-in-the-loop applications in comparison to the traditional damping impedance method and ideal transformer model interfaces. The hardware-in-the-loop test-bed (a power-electronic-based system) is utilized to experimentally analyze and validate the expected advantages of the modified damping impedance method. In addition, to evaluate the power hardware-in-the-loop accuracy, a transfer function perturbation-based approach is considered to validate the results of experimentation analytically and quantitatively.     

Surface modification of silica-graphene nanohybrid as a novel stabilizer for oil-water emulsion

The surface modification of silica-graphene nanohybrid through treatment with a mixture of nitric and sulfuric acid vapors to prepare a novel stabilizer for decalin-water emulsion was investigated. The nanohybrid was prepared through chemical vapor deposition using silica aerogel and acetylene as catalyst and carbon precursor at atmospheric pressure and 600 °C. The physicochemical properties of the modified nanohybrid were characterized by FT-IR, XPS, Raman spectroscopy, and TEM. The surface modification of nanohybrid was at various duration times (24, 48, and 72 hours) to optimize the surface modification conditions. Zeta potential of ?39.9 mV revealed that the surface modification of nanohybrid after 72 hours had an excellent stability in aqueous phase due to the presence of exceptional functional groups. The emulsion average droplet size decreased by increasing the nanohybrid concentration. The negative value of the zeta potential showed the proposed nanohybrid can be applied as an appropriate stabilizer for emulsion.     

Prediction and optimization of hydrogen yield and energy conversion efficiency in a non-catalytic filtration combustion reactor for jet A and butanol fuels

Hydrogen production is one of main subjects in fuel cells. The traditional method of synthesis gas production is based on fuel reforming using catalysts. The main problem of these methods is sensitivity and fast degradation of catalysts especially when fuels with high sulfur content are used. A new technique for hydrogen production is fuel-reforming using non-catalytic filtration combustion in porous media reactors. Various experimental works have been carried out to increase hydrogen production under different operating conditions such as inlet fuel velocity and equivalence ratio. First, we investigated the ability of adaptive neuro fuzzy inference system (ANFIS) for predicting the filtration combustion characteristics. Four distinct ANFIS models were developed for estimating the hydrogen yield and energy conversion efficiency for fuels of jet A and butanol. Eight different membership functions of dsigmf, gauss2mf, gaussmf, gbellmf, pimf, psigmf, trapmf and trimf were tested for training of the ANFIS networks. The results showed that the RMSE of the best developed ANFIS models for estimating of the hydrogen yield of jet fuel, hydrogen yield of butanol, conversion efficiency of jet fuel and conversion efficiency of butanol were 1.399, 1.213, 0.508 and 2.191, respectively. Moreover the R² values of 0.998, 0.998, 0.999 and 0.999 were obtained for predicting the above mentioned variables, respectively. In the second step, a novel algorithm based on imperialist competitive algorithm (ICA) was used for optimization of hydrogen yield and energy efficiency. The maximum value of hydrogen yield and energy efficiency was 50.46% and 67.88% for jet A and 47.27% and 96.93% for butanol, respectively. The results showed that the imperialist competitive algorithm is an efficient and powerful algorithm to optimize combustion processes.     

Effect of the Spectrum Sensing Period on the TCP Performance Over Cognitive Radio Ad-hoc Networks

Wireless Personal Communications - Modeling of spectrum occupancy by both primary and secondary users in the Cognitive Radio Ad-hoc Network (CRAHN) can be helpful to improve spectrum usage...     

Exploring the Potential of Drug Response Assays for Precision Medicine in Ovarian Cancer

One of the major challenges in the treatment of cancer are differential responses of patients to existing standard of care anti-cancer drugs. These differential responses may, in part, be due to a diverse range of genomic, epigenomic, proteomic, and metabolic alterations among individuals suffering from the same type of cancer. Precision medicine is an emerging approach in cancer therapeutics that takes into account specific molecular alterations, environmental factors as well as lifestyle of individual patients. This approach allows clinicians and researchers to select or predict treatments that would most likely benefit the patient based on their individual tumor characteristics. One class of precision medicine tools are predictive, in vitro drug-response assays designed to test the sensitivity of patient tumor cells to existing or novel therapies. These assays have the potential to rapidly identify the most effective treatments for cancer patients and thus hold great promise in the field of precision medicine. In this review, we have highlighted several drug-response assays developed in ovarian cancer and discussed the current challenges and future prospects of these assays in the clinical management of this disease.     

Synthesis and characterization of MgAl2O4 spinel precursor sol prepared by inorganic salts

Magnesium aluminate (MgAl₂O₄) spinel precursor sol was prepared using aluminum nitrate and magnesium nitrate as the precursors for alumina and magnesia, respectively. The obtained sol owned a translucent, homogenous, and stable appearance with a density of 1.19 g/cm³, and at the temperatures above 60 °C, converted to a soft and clear gel. The measured pH of sol was in the range of 3–4, and at the calcination temperature of 1000 °C, the solid content of 6% was reached. TGA/DSC analysis was utilized to study the thermal behavior of the sol. Fourier transforms infrared spectroscopy (FTIR) was applied to recognize the existing bounds in the dried and calcined sol. X-ray diffraction patterns revealed the formation of single-phase MgAl₂O₄ up to 600 °C. According to the FESEM images, the grain size for the sol calcined at 1000 °C was estimated at around 50 nm.     

(Electro)Sensing of Phenicol Antibiotics—A Review

The presence of residues from frequent antibiotic use in animal feed can cause serious health risks by contaminating products meant for human consumption such as meat and milk. The present paper gives an overview of the electrochemical methods developed for the detection of phenicol antibiotic residues (chloramphenicol, thiamphenicol, and florfenicol) in different kinds of foodstuffs. Electrochemical sensors based on different biomolecules and nanomaterials are described. The detection limit of various developed methods with their advantages and disadvantages will be highlighted.