Participation of people in waste source separation program

One of the basic problems of current cities is solid waste and its correct management. Solid waste material is the unavoidable product of routine life of human being. These wastes affect the quality and quantity of life in the present era. Increased population, development, human activities and shortage of resources have caused the solid waste management a necessity. Waste reduction management and its separation at source are performed with the citizens’ participation. The present study aimed to evaluate the waste source separation and determine the participation of citizens in Azimie of Karaj city in Alborz province, Iran.This study is questionnaire based and to achieve the study purpose, question-based questionnaires are distributed randomly among 100 citizens of Azimieh. The data were analyzed. Based on the results of study, in separation at source activity, 70% of people participated and the highest participation was via media and teachers. 100% of subjects were inclined to participate in this program. The effect of this plan was 90% and effectiveness of this plan from economic, social and environmental aspects was high.Keywords: Waste, Separation at source, People participation, Azimi

Quantum Probes for the Characterization of Nonlinear Media

Active optical media leading to interaction Hamiltonians of the form $H = \tilde{\lambda}\, (a + a^{\dagger})^{\zeta}$ represent a crucial resource for quantum optical technology. In this paper, we address the characterization of those nonlinear media using quantum probes, as opposed to semiclassical ones. In particular, we investigate how squeezed probes may improve individual and joint estimation of the nonlinear coupling $\tilde{\lambda}$ and of the nonlinearity order $\zeta$. Upon using tools from quantum estimation, we show that: i) the two parameters are compatible, i.e. the may be jointly estimated without additional quantum noise; ii) the use of squeezed probes improves precision at fixed overall energy of the probe; iii) for low energy probes, squeezed vacuum represent the most convenient choice, whereas for increasing energy an optimal squeezing fraction may be determined; iv) using optimized quantum probes, the scaling of the corresponding precision with energy improves, both for individual and joint estimation of the two parameters, compared to semiclassical coherent probes. We conclude that quantum probes represent a resource to enhance precision in the characterization of nonlinear media, and foresee potential applications with current technology

Deep Shape Matching

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.Comment: ECCV 201

A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval

The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB

Early chronic kidney disease: diagnosis, management and models of care

Chronic kidney disease (CKD) is prevalent in many countries, and the costs associated with the care of patients with end-stage renal disease (ESRD) are estimated to exceed US$1 trillion globally. The clinical and economic rationale for the design of timely and appropriate health system responses to limit the progression of CKD to ESRD is clear. Clinical care might improve if early-stage CKD with risk of progression to ESRD is differentiated from early-stage CKD that is unlikely to advance. The diagnostic tests that are currently used for CKD exhibit key limitations; therefore, additional research is required to increase awareness of the risk factors for CKD progression. Systems modelling can be used to evaluate the impact of different care models on CKD outcomes and costs. The US Indian Health Service has demonstrated that an integrated, system-wide approach can produce notable benefits on cardiovascular and renal health outcomes. Economic and clinical improvements might, therefore, be possible if CKD is reconceptualized as a part of primary care. This Review discusses which early CKD interventions are appropriate, the optimum time to provide clinical care, and the most suitable model of care to adopt

FRICTIONAL MODELING AND OPTIMIZATION FOR A VIBRATION MODAL ANALYSIS SIMULATOR DEVICE USING GENETIC ALGORITHM

The aim of this study is friction modeling and optimization of a âvibration modal analysis simulatorâ. This device has been used for observation and measurement of natural frequencies and mode shapes ofvibrating components and parts under the free or forced vibration conditions. In this paper to obtain linear vibrating motion with less measurement errors and optimum friction factor, the new prototype has been designed using the genetic algorithm. Mass linear motion is modeled by viscous friction agent so that optimized friction factor C with six sub-designation factors is calculated. Obtained results with respect to precursorâs availability, fabrication ability and from the economical point of view are more amenable and applicable than the preliminary devices and show a 70% reduction in friction factor

On the quantumness of multiparameter estimation problems for qubit systems

The estimation of more than one parameter in quantum mechanics is a fundamental problem with relevant practical applications. In fact, the ultimate limits in the achievable estimation precision are ultimately linked with the non-commutativity of different observables, a peculiar property of quantum mechanics. We here consider several estimation problems for qubit systems and evaluate the corresponding quantumness R, a measure that has been recently introduced in order to quantify how incompatible the parameters to be estimated are. In particular, R is an upper bound for the renormalized difference between the (asymptotically achievable) Holevo bound and the SLD Cram\ue9r-Rao bound (i.e., the matrix generalization of the single-parameter quantum Cram\ue9r-Rao bound). For all the estimation problems considered, we evaluate the quantumness R and, in order to better understand its usefulness in characterizing a multiparameter quantum statistical model, we compare it with the renormalized difference between the Holevo and the SLD-bound. Our results give evidence that R is a useful quantity to characterize multiparameter estimation problems, as for several quantum statistical model, it is equal to the difference between the bounds and, in general, their behavior qualitatively coincide. On the other hand, we also find evidence that, for certain quantum statistical models, the bound is not in tight, and thus R may overestimate the degree of quantum incompatibility between parameters