Distributed optimization framework for in-network data processing

In-Network Processing (INP) is an effective way to aggregate and process data from different sources and forward the aggregated data to other nodes for further processing until it reaches the end user. There is a trade-off between energy consumption for processing data and communication energy spent on transferring the data. Specifically, aggressive data aggregation consumes much energy for processing, but results in less data for transmission, thus using less energy for communications, and vice versa. An essential requirement in the INP process is to ensure that the user expectation of quality of information (QoI) is delivered during the process. Using wireless sensor networks for illustration and with the aim of minimising the total energy consumption of the system, we study and formulate the trade-off problem as a nonlinear optimisation problem where the goal is to determine the optimal data reduction rate, while satisfying the QoI required by the user. The formulated problem is a Signomial Programming (SP) problem, which is a non-convex optimisation problem and very hard to be solved directly. We propose two solution frameworks. First, we introduce an equivalent problem which is still SP and non-convex as the original one, but we prove that the strong duality property holds, and propose an efficient distributed algorithm to obtain the optimal data reduction rates, while delivering the required QoI. The second framework applies to the system with identical nodes and parameter settings. In such cases, we prove that the complexity of the problem can be reduced logarithmically. We evaluate our proposed frameworks under different parameter settings and illustrate the validity and performance of the proposed techniques through extensive simulation

Preparation of nanostructured nickel aluminate spinel powder from spent NiO/Al2O3 catalyst by mechano-chemical synthesis

In this paper, the possibility of mechano-chemical synthesis, as a single step process for preparation of nanostructured nickel aluminate spinel powder from NiO/Al2O3 spent catalyst was investigated. Powder samples were characterized in terms of composition, morphology, structure, particle size and surface area using complementary techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), differential thermal analysis (DTA) and volumetric adsorption of nitrogen. It was found that formation of spinel was possible after 60 h of milling with no heat treatment. Additionally, influence of mechanical activation on the heat treatment temperature was discussed. It was observed that heat treatment of 15 h milled sample at 1100 °C is enough to produce nickel aluminate spinel. A product of direct mechanical milling showed higher value of surface area (42.3 m2/g) and smaller crystallite size (12 nm) as compared to the heat treated product.Gobierno de España ENE 2007-67926-C02-0

Introducing a precise system for determining volume percentages independent of scale thickness and type of flow regime

When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent

Dietary intakes of adolescent girls in relation to weight status

Background: To examine macronutrient and micronutrient intake of adolescent girls of Tehran, capital of Iran to discover any malnutrition in relation to weight status and dieting. Methods: A cross-sectional study was conducted. Four hundred 11- to 17-year-old students were selected by multistage cluster sampling from secondary and high schools of Tehran. The information about dietary intakes was taken by food frequency questionnaire and 24-hour recall form. The students' body mass indices (BMIs) were measured and were classified according to National Center for Health Statistics/Center for Disease Control and Prevention (2000) growth charts. Participants were also questioned about body image and dieting. Results: 6.7 of adolescent girls were classified as being obese, 14.6 overweight, 75.4 normal and 3.2 underweight. Students 11-13 year old, had mean intakes lower than estimated average requirement (EAR) for folic acid, vitamin E, calcium, magnesium, phosphorus, potassium and sodium, and 14-18 year old students had mean intakes lower than EAR for niacin, pyridoxine, folic acid, pantothenic acid, vitamin E, calcium, magnesium, phosphorus, potassium, sodium and zinc. Obese and overweight adolescents had less carbohydrate, thiamin, niacin, iron and selenium intake. The participants, who were dieting, used significantly less amounts of proteins, carbohydrates, thiamin, niacin, iron, selenium, sodium and zinc. Conclusion: Knowing the harmful consequences of nutrient deficiency especially in adolescents, nutrition education must be emphasized in schools to promote nutritional literacy

The extracellular matrix supports breast cancer cell growth under amino acid starvation by promoting tyrosine catabolism

Breast tumours are embedded in a collagen I-rich extracellular matrix (ECM) network, where nutrients are scarce due to limited blood flow and elevated tumour growth. Metabolic adaptation is required for cancer cells to endure these conditions. Here, we demonstrated that the presence of ECM supported the growth of invasive breast cancer cells, but not non-transformed mammary epithelial cells, under amino acid starvation, through a mechanism that required macropinocytosis-dependent ECM uptake. Importantly, we showed that this behaviour was acquired during carcinoma progression. ECM internalisation, followed by lysosomal degradation, contributed to the up-regulation of the intracellular levels of several amino acids, most notably tyrosine and phenylalanine. This resulted in elevated tyrosine catabolism on ECM under starvation, leading to increased fumarate levels, potentially feeding into the tricarboxylic acid (TCA) cycle. Interestingly, this pathway was required for ECM-dependent cell growth and invasive cell migration under amino acid starvation, as the knockdown of p-hydroxyphenylpyruvate hydroxylase-like protein (HPDL), the third enzyme of the pathway, opposed cell growth and motility on ECM in both 2D and 3D systems, without affecting cell proliferation on plastic. Finally, high HPDL expression correlated with poor prognosis in breast cancer patients. Collectively, our results highlight that the ECM in the tumour microenvironment (TME) represents an alternative source of nutrients to support cancer cell growth by regulating phenylalanine and tyrosine metabolism

Self-Assembly of Nanoparticles onto the Surfaces of Polystyrene Spheres with a Tunable Composition and Loading

Functional colloidal materials were prepared by design through the self-assembly of nanoparticles (NPs) on the surfaces of polystyrene (PS) spheres with control over NP surface coverage, NP-to-NP spacing, and NP composition. The ability to control and fine tune the coating was extended to the first demonstration of the co-assembly of NPs of dissimilar composition onto the same PS sphere, forming a multi-component coating. A broad range of NP decorated PS (PS@NPs) spheres were prepared with uniform coatings attributed to electrostatic and hydrogen bonding interactions between stabilizing groups on the NPs and the functionalized surfaces of the PS spheres. This versatile two-step method provides more fine control than methods previously demonstrated in the literature. These decorated PS spheres are of interest for a number of applications, such as catalytic reactions where the PS spheres provide a support for the dispersion, stabilization, and recovery of NP catalysts. The catalytic properties of these PS@NPs spheres were assessed by studying the catalytic degradation of azo dyes, an environmental contaminant detrimental to eye health. The PS@NPs spheres were used in multiple, sequential catalytic reactions while largely retaining the NP coating

Measure Twice: Promise of Liquid Biopsy in Pediatric High-Grade Gliomas

Purpose To review and critique the current state of liquid biopsy in pHGG. Materials and Methods Published literature was reviewed for articles related to liquid biopsy in pediatric glioma and adult glioma with a focus on high-grade gliomas. Results This review discusses the current state of liquid biomarkers of pHGG and their potential applications for liquid biopsy development. Conclusions While nascent, the progress toward identifying circulating analytes of pHGG primes the field of neuro-oncoogy for liquid biopsy development