Male Rat Offspring Are More Impacted by Maternal Obesity Induced by Cafeteria Diet than Females—Additive Effect of Postweaning Diet

Maternal obesity increases the risk of health complications in offspring, but whether these effects are exacerbated by offspring exposure to unhealthy diets warrants further investigation. Female Sprague-Dawley rats were fed either standard chow (n = 15) or ‘cafeteria’ (Caf, n = 21) diets across pre-pregnancy, gestation, and lactation. Male and female offspring were weaned onto chow or Caf diet (2–3/sex/litter), forming four groups; behavioural and metabolic parameters were assessed. At weaning, offspring from Caf dams were smaller and lighter, but had more retroperitoneal (RP) fat, with a larger effect in males. Maternal Caf diet significantly increased relative expression of ACACA and Fasn in male and female weanling liver, but not CPT-1, SREBP and PGC1; PPARα was increased in males from Caf dams. Maternal obesity enhanced the impact of postweaning Caf exposure on adult body weight, RP fat, liver mass, and plasma leptin in males but not females. Offspring from Caf dams appeared to exhibit reduced anxiety-like behaviour on the elevated plus maze. Hepatic CPT-1 expression was reduced only in adult males from Caf fed dams. Post weaning Caf diet consumption did not alter liver gene expression in the adult offspring. Maternal obesity exacerbated the obesogenic phenotype produced by postweaning Caf diet in male, but not female offspring. Thus, the impact of maternal obesity on adiposity and liver gene expression appeared more marked in males. Our data underline the sex-specific detrimental effects of maternal obesity on offspring.     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

Stability analysis of a capacitive micro-resonator with embedded pre-strained SMA wires

This paper presents a study on the effects of the SMA wires’ characteristics on tuning the stability of a capacitive micro-resonator. In the proposed model, pre-strained SMA wires have been embedded in a double clamped resonant microbeam which is actuated electrostatically. The governing equations of the system have been introduced and then an eigen-value problem has been adopted to investigate stability. Galerkin-based numerical methods have been used to solve the governing equation of motion for obtaining the motion trajectories of the beam. The effects of the number of SMA wires, their diameter, pre-strain and temperature on the pull-in instability and frequency response of the resonator have been shown. Critical values of recovery stress and SMA temperature for avoiding instability, with and without applying DC voltage have been obtained. The results have shown that by changing each of the SMA parameters, one can reach a needed magnitude of recovery stress as well as transmitted longitudinal force to the host beam, and consequently control and tune the stability and resonance frequency of the micro-resonator.

     

Energy and Exergy Analyses of Thin-Layer Drying of Potato Slices in a Semi-Industrial Continuous Band Dryer

This article is concerned with the energy and exergy analyses of the continuous-convection drying of potato slices. The first and second laws of thermodynamics were used to calculate the energy and exergy. A semi-industrial continuous-band dryer has been designed and used for drying experiments. The equipment has a drying chamber of 2 m length and the inlet air used for drying is heated by gas power. The experiments were conducted on potato slices with thickness of 5 mm at three different air temperatures of 50, 60 and 70°C, drying air mass flow rates of 0.61, 1.22, and 1.83 kg/s and feeding rates of 2.31 × 10^?4, 2.78 × 10^?4, and 3.48 × 10^?4 kg/s. The energy utilization and energy utilization ratio were found to vary between 3.75 and 24.04 kJ/s and 0.1513 and 0.3700, respectively. These values show that only a small proportion of the supplied energy by the heater was used for drying. The exergy loss and exergy efficiency were found to be in the range of 0.5987 to 13.71 kJ/s and 0.5713 to 0.9405, respectively, indicating that the drying process was thermodynamically inefficient and much energy was vented in the exhaust air. In addition, the results showed that the feeding rate and the temperature and flow rate of the drying air had an important effect on energy and exergy use. This knowledge will provide insights into the optimization of a continuous dryer and the operating parameters that causes reduction of energy consumption and losses in continuous drying.     

Thermodynamic analysis of fluidized bed drying of carrot cubes

In this study, the energy and exergy analyses of fluidized bed drying of carrot cubes were investigated. Drying experiments were conducted at inlet air temperatures of 50, 60, and 70 °C, BD (bed depths) of 30, 60, and 90 mm and square-cubed carrot dimensions of 4, 7, and 10 mm. The effects of drying variables on energy utilization, energy utilization ratio, exergy loss and exergy efficiency were studied. The energy utilization and energy utilization ratio varied between 0.105–1.949 kJ/s and 0.074–0.486, respectively. The exergy loss and exergy efficiency were found to be in the range of 0.206–1.612 kJ/s and 0.103–0.707, respectively. The results showed that small particles, deep beds and high inlet air temperatures increased energy utilization, energy utilization ratio, and exergy loss due to high value of heat and mass transfer. Also, the exergy efficiency had maximum value when higher drying air temperature, larger CS (cube size) and shorter BD were used for drying experiment.     

Disinfection of dairy wastewater effluent through solar photocatalysis processes

Due to the strict regulations and reuse policies that govern wastewater's use as an irrigation water resource for agricultural purposes, especially in dry climates, optimization of the disinfection process is of the utmost importance. The effects of solar radiation along with Titanium dioxide(TiO_2) nanoparticles applied to optimization of the photolysis and photocatalysis processes for inactivating heterotrophic bacteria were investigated. Temperature, p H, and dissolved oxygen fluctuations in the dairy wastewater effluent treated by activated sludge were examined. In addition,different dosages of TiO_2 were tested in the solar photocatalysis(ph-C S) and concentrated solar photocatalysis(ph-C CS) processes. The results show that the disinfection efficiencies of the solar photolysis(ph-L S) and concentrated solar photolysis(ph-L CS) processes after 30 min were about 10.5% and 68.9%, respectively, and that the ph-C S and ph-C CS processes inactivated 41% and 97% of the heterotrophic bacteria after 30 min, respectively. The p H variation in these processes was negligible. Using the ph-L CS and ph-C CS processes, the synergistic effect between the optical and thermal inactivation caused complete disinfection after three hours. However, disinfection was faster in the ph-C CS process than in the ph-L CS process. Significant correlations were found between the disinfection efficiency and the variation of the dissolved oxygen concentration in the ph-C S and ph-C CS processes, while the correlations between the disinfection efficiency and temperature variation were not significant in the ph-L S and ph-C S processes. Moreover, the oxygen consumption rate was greatest(3.2 mg··L~(-1)) in the ph-C CS process. Hence,it could be concluded that the ph-C CS process is an efficient photocatalysis process for disinfection of dairy wastewater effluent.     

Parametric studies of DD3R particles seeding on the modified surface of α-alumina support and synthesis of DD3R zeolite membrane

The effects of suspension concentration, number of coating stages and dipping time on seeding quality of DD3R particles on the ceramic supports were investigated. The supports were immersed in aqueous suspensions (0.1, 0.2 and 0.3?wt. %) of the DD3R seeds for three different dipping times 30, 90 and 240?s with different stages of 1, 2 and 3. The SEM analysis was used to study the quality of the seeded layers. The optimized seeding conditions of 0.2?wt. % suspension concentration, 2 number of coating stages and 30?s for dipping time leaded to obtain a uniform seeded layer with monolayer structure. The DD3R zeolite membrane was synthesized via hydrothermal method under the optimized seeding conditions. The XRD and SEM analyses confirmed the synthesis of DD3R membrane with proper quality. The single gas permeation results showed a good performance in the separation of CO₂ from CH₄.     

Optimization of an Artificial Neural Network Topology for Predicting Drying Kinetics of Carrot Cubes Using Combined Response Surface and Genetic Algorithm

In this study, the advantages of integrated response surface methodology (RSM) and genetic algorithm (GA) for optimizing artificial neural network (ANN) topology of convective drying kinetic of carrot cubes were investigated. A multilayer feed-forward ANN trained by back-propagation algorithms was developed to correlate output (moisture ratio) to the four exogenous input variables (drying time, drying air temperature, air velocity, and cube size). A predictive response surface model for ANN topologies was created using RSM. The response surface model was interfaced with an effective GA to find the optimum topology of ANN. The factors considered for building a relationship of ANN topology were the number of neurons, momentum coefficient, step size, number of training epochs, and number of training runs. A second-order polynomial model was developed from training results for mean square error (MSE) of 50 developed ANNs to generate 3D response surfaces and contour plots. The optimum ANN had minimum MSE when the number of neurons, step size, momentum coefficient, number of epochs, and number of training runs were 23, 0.37, 0.68, 2,482, and 2, respectively. The results confirmed that the optimal ANN topology was more precise for predicting convective drying kinetics of carrot cubes.     

Calling communities analysis and identification using machine learning techniques

The analysis of social communities related logs has recently received considerable attention for its importance in shedding light on social concerns by identifying different groups, and hence helps in resolving issues like predicting terrorist groups. In the customer analysis domain, identifying calling communities can be used for determining a particular customer’s value according to the general pattern behavior of the community that the customer belongs to; this helps the effective targeted marketing design, which is significantly important for increasing profitability. In telecommunication industry, machine learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behavior such as churn prediction. In this paper, we pursue identifying the calling communities and demonstrate how cluster analysis can be used to effectively identify communities using information derived from the CDR data. We use the information extracted from the cluster analysis to identify customer calling patterns. Customers calling patterns are then given to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply different machine learning techniques to build classifier models and compare them in terms of classification accuracy and computational performance. The reported test results demonstrate the applicability and effectiveness of the proposed approach.