Utilizing Radiant Nanoparticles in Silicon for Balanced White Color Adapters

Silicon - This work fully depends on the silicon nanoparticles. It is represented as SiNPs. This depends on the transparent LEDs color converters. The spectrum obtained is fully white, so it is...     

Stochastic Cellular Automata Solutions to the Density Classification Problem

In the density classification problem, a binary cellular automaton (CA) should decide whether an initial configuration contains more 0s or more 1s. The answer is given when all cells of the CA agree on a given state. This problem is known for having no exact solution in the case of binary deterministic one-dimensional CA. We investigate how randomness in CA may help us solve the problem. We analyse the behaviour of stochastic CA rules that perform the density classification task. We show that describing stochastic rules as a “blend” of deterministic rules allows us to derive quantitative results on the classification time and the classification time of previously studied rules. We introduce a new rule whose effect is to spread defects and to wash them out. This stochastic rule solves the problem with an arbitrary precision, that is, its quality of classification can be made arbitrarily high, though at the price of an increase of the convergence time. We experimentally demonstrate that this rule exhibits good scaling properties and that it attains qualities of classification never reached so far.     

A utility maximization approach for information-communication tradeoff in Wireless Body Area Networks

Healthcare systems have made a dramatic shift towards ubiquitous monitoring in the recent past. The reasons for such a change have been ease of timely diagnosis, convenience and comfort of clinical treatments. Wireless Body Area Networks (WBANs) are mainly characterized by deployment of biomedical sensors around human body which transmit vital signs measurements about the health status of the patient. Unfortunately, the huge traffic load of clinical data and limited resources of biomedical sensors make the efficiency of long-term operations almost impossible. Therefore, it is necessary to make significant advances in sensor’s energy saving. Our idea is to reduce the activities of some sensors depending on the relevance between the data they measure and the diseases to detect. This paper shows how to extend the lifetime of medical WBANs by appropriately taking benefit of correlation between the knowledge about the disease and sensing data to drive the best scheduling of the medical sensors. For that, the theoretical framework of an economic approach, i.e., network utility maximization, is developed for sensor scheduling under operations cost constraint. It is shown that the compact subset of sensors can be found to provide necessary information for timely and correct diagnoses. Based on the theoretical framework, an algorithm combining sensor selection and information gain is then proposed. Simulation results show that the algorithm achieves high performance in terms of energy saving vs latency in disease detection.     

The electrical and dielectric characterization of the Co/ZnO-Rods/p-Si heterostructure depending on the frequency

Journal of Materials Science: Materials in Electronics - Zinc oxide (ZnO) rods film was fabricated by homemade and simple spray pyrolysis technique on a p-type silicon (Si) substrate, and the film...     

An eco-friendly process: Predictive modelling of copper adsorption from aqueous solution on Spirulina platensis

The adsorption of copper ions on Spirulina platensis was studied as a function of contact time, initial metal ion concentration, and initial pH regimes. Characterization of this adsorbent was confirmed by FTIR spectrum. Modified Gompertz and Logistic models have not been previously applied for the adsorption of copper. Logistic was the best model to describe experimental kinetic data. This adsorption could be explained by the intra-particle diffusion, which was composed of more than one sorption processes. Langmuir, Freundlich, and Redlich–Peterson were fitted to equilibrium data models. According to values of error functions and correlation coefficient, the Langmuir and Redlich–Peterson models were more appropriate to describe the adsorption of copper ions on S. platensis. The monolayer maximum adsorption capacity of copper ions was determined as 67.93 mg g^?1. Results indicated that this adsorbent had a great potential for removing of copper as an eco-friendly process.     

Exergetic performance analysis of ohmic cooking process

This paper deals with the performance evaluation of ohmic cooking process by applying exergy analysis. Cylindrically shaped ground beef samples with different fat contents (2%, 9% and 15%) were used as the test material being cooked. The cooking process was conducted at three different voltage gradients (20, 30 or 40 V/cm) up to 80 °C. The effects of voltage gradient applied and the initial fat content of sample on the energy efficiency, exergy efficiency, exergy loss and improvement potential were investigated. The energy and exergy efficiency values for ohmic cooking process at the voltage gradients between 20 and 40 V/cm were found to be in the range of 0.69–0.91% and 63.2–89.2%, respectively. It was determined that the system should have been improved energetically especially for samples having high initial fat contents cooked at the low voltage gradients, since improvement potential values predicted were highest at this conditions.     

Development of efficient photoreactors for solar hydrogen production

The rate of hydrogen evolution from a photocatalytic process depends not only on the activity of a photocatalyst, but also on photoreactor design. Ideally, a photoreactor should be able to absorb the incident light, promoting photocatalytic reactions in an effective manner with minimal photonic losses. There are numerous technical challenges and cost related issues when designing a large-scale photoreactor for hydrogen production. Active stirring of the photocatalyst slurry within a photoreactor is not practical in large-scale applications due to cost related issues. Rather, the design should allow facile self-mixing of the flow field within the photoreactor. In this paper two types of photocatalytic reactor configurations are studied: a batch type design and another involving passive self-mixing of the photolyte. Results show that energy loss from a properly designed photoreactor is mainly due to reflection losses from the photoreactor window. We describe the interplay between the reaction and the photoreactor design parameters as well as effects on the rate of hydrogen evolution. We found that a passive self-mixing of the photolyte is possible. Furthermore, the use of certain engineering polymer films as photoreactor window materials has the potential for substantial cost savings in large-scale applications, with minimal reduction of photon energy utilization efficiency. Eight window materials were tested and the results indicate that Aclar™ polymer film used as the photoreactor window provides a substantial cost saving over other engineering polymers, especially with respect to fused silica glass at modest hydrogen evolution rates.     

Multi-scale directional-filtering-based method for follicular lymphoma grading

Follicular lymphoma (FL) is a group of malignancies of lymphocyte origin that arise from lymph nodes, spleen, and bone marrow in the lymphatic system. It is the second most common non-Hodgkins lymphoma. Characteristic of FL is the presence of follicle center B cells consisting of centrocytes and centroblasts. Typically, FL images are graded by an expert manually counting the centroblasts in an image. This is time consuming. In this paper, we present a novel multi-scale directional filtering scheme and utilize it to classify FL images into different grades. Instead of counting the centroblasts individually, we classify the texture formed by centroblasts. We apply our multi-scale directional filtering scheme in two scales and along eight orientations, and use the mean and the standard deviation of each filter output as feature parameters. For classification, we use support vector machines with the radial basis function kernel. We map the features into two dimensions using linear discriminant analysis prior to classification. Experimental results are presented.     

Effects of starter cultures and additives on the quality of Turkish style sausage (sucuk)

The effects of starter cultures and additives on the quality of Turkish style sausage (sucuk) were investigated during ripening and storage. Total aerobic plate counts (aerobic bacteria), mould and yeast counts, pH, thiobarbituric acid (TBA) value and biogenic amine formation were followed. Aerobic bacteria increased (P<0.05) during the first 10 days of ripening to 9.26 log CFU/g and they decreased (P<0.05) to 5.70 at the end of the storage. Larger reductions (P<0.05) were observed in sausages made with high levels of potassium sorbate, nitrite and nitrate than in those containing low levels of additives and those without additives at the end of storage. During the first 3 days of ripening, the pH values of all sausages decreased (P<0.05) from 5.98 to about 4.53. Later, the pH values increased slowly, due to decomposition of acids to an approximately constant value of about 5.20. TBA values were higher (P<0.05) in the sausages made without additives than in the others. Sausages prepared using high concentration of additives had lower (P<0.05) concentrations of biogenic amines than the others. Addition of starter culture with high concentration of additives (nitrite, nitrate, α-tocopherol, ascorbic acid, potassium sorbate, potassium pyrophosphate and di-potassium hydrogenphosphate) decreased the formation of biogenic amine.     

Optimization of the Cementation Process for the Removal of Copper in Process Effluent Discharges

Cementation is an effective process that meets the requirements of process cleanliness. The performance of the cementation reaction is dictated by a number of variables. In this study, the effects of various parameters on the yields of copper‐iron cementation (wire and powder) were investigated. Statistical full factorial designs were used to produce appropriate mixture models for the yields. The experimental designs were done at three different levels of three operating variables, namely initial concentration, temperature, and pH. The optimization studies were carried out with MATLAB 7.0 software. The optimum yields obtained from the predicted models were found to be 0.9916 and 0.9088 for iron wire and powder, respectively, under optimized conditions, and these were in accordance with the experimental observations.