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Thermochemical conversion processes are one of the possible solutions for the flexible production of electric and thermal power from biomass. The pyrolysis degradation process presents, among the others, the interesting features of biofuels and high energy density bio-oil production potential high conversion rate. In this paper, numerical results of a slow batch and continuous fast pyrolyzers, are presented, aiming at validating both a tridimensional computational fluid dynamics-discrete element method (CFD–DEM) and a monodimensional distributed activation energy model (DAEM) represents with data collected in dedicated experiments. The purpose of this paper is then to provide reliable models for industrial scale-up and direct design purposes.

The slow pyrolysis experimental system, a batch of small-scale constant-pressure bomb for allothermic conversion processes, is presented. A DEM numerical model has been implemented by means of a modified OpenFOAM solver. The fast pyrolysis experimental system and a lab scale screw reactor designed for biomass fast pyrolysis conversion are also presented along with a 1D numerical model to represent its operation. The model which is developed for continuous stationary feeding conditions and based on a four-parallel reaction chemical framework is presented in detail.

The slow pyrolysis numerical results are compared with experimental data in terms of both gaseous species production and reduction of the bed height showing good predictive capabilities. Fast pyrolysis numerical results have been compared to the experimental data obtained from the fast pyrolysis process of spruce wood pellet. The comparison shows that the chemical reaction modeling based on a Gaussian DAEM is capable of giving results in very good agreement with the bio-oil yield evaluated experimentally.

As general results of the proposed activities, a mixed experimental and numerical approach has demonstrated a very good potential in developing design tools for pyrolysis development.

Health is one of the most basic human rights. Self-medication not only results in many risks, complications and mortalities but also remains a massive economic burden on governments’ pharmaceutical budgets, insurance companies and the general population. The importance of self-medication as one of the worldwide health problems, this paper aims to investigate the extent of this problem and related factors in Sanandaj, Iran in 2018.

This cross-sectional, descriptive study was conducted on 838 people who referred to pharmacies in Sanandaj, Iran in 2018. The subjects were selected in 10 pharmacies, 84 in each, randomly. A self-constructed questionnaire was used to collect information about demographic features, prevalence and reasons for self-medication. The data was analyzed statistically by SPSS software.

The results show a high prevalence of self-medication, 73.70 per cent among people who referred to the pharmacies in Sanandaj during 2018. There was a significant correlation between self-medication and occupation, education level, age groups, chronic disease, smoking or reasons to refer to pharmacies (p < 0.05). Analgesics, antibiotics; non-steroid anti-inflammatory, cold and gastrointestinal medicines were the most commonly used medicines by the subjects. People with headache, cold, infection, stomach pain, toothache and dysmenorrhea were more likely to act self-medication than people with other complaints. Among people who do self-medication, (85.60 per cent) had already experienced satisfactory results after using medicines. Mild symptoms of the disease (74.11 per cent), availability of medicines (72.98 per cent) and easy access to medicine supplies in pharmacies (71.35 per cent) are other reasons for self-medication.

In general, the study had some limitations. One of the limitations was related to the research period i.e. spring and summer. So, it is suggested to conduct studies in other seasons, too. Furthermore, as the time of self-medication was the past three months, there was a probability of recall bias.

The prevalence of self-medication in Sanandaj is high and alarming, implementing educational programs about this issue is extremely required. Health policymakers should take all necessary steps to tackle self-medication efficiently.

In this study, a different approach is introduced to generate the kinetic sub-model for the modeling of solid-state pyrolysis reactions based on the thermogravimetric (TG) experimental data over a specified range of heating rates. Gene Expression Programming (GEP) is used to produce a correlation for the single-step global reaction rate as a function of determining kinetic variables, namely conversion, temperature, and heating rate.

For a case study on the coal pyrolysis, a coefficient of determination (R²) of 0.99 was obtained using the generated model according to the experimental benchmark data. Comparison of the model results with the experimental data proves the applicability, reliability, and convenience of GEP as a powerful tool for modeling purposes in the solid-state pyrolysis reactions.

The resulting kinetic sub-model takes advantage of particular characteristics, to be highly efficient, simple, accurate, and computationally attractive, which facilitates the CFD simulation of real pyrolizers under isothermal and non-isothermal conditions.

It should be emphasized that the above-mentioned manuscript is not under evaluation in any journals and submitted exclusively for consideration for possible publication in this journal. The generated kinetic model is in the final form of an algebraic correlation which, in comparison to the conventional kinetic models, suggests several advantages: to be relatively simpler, more accurate, and numerically efficient. These characteristics make the proposed model computationally attractive when used as a sub-model in CFD applications to simulate real pyrolizers under complex heating conditions.

The purpose of this paper is to discuss some considerations about the problem of Natural Language Understanding with respect to the particular semantic domain of Robotics. The goal that makes realistic this research direction is the programming of a robot in Natural Language. A system called DONAU (Domain Oriented NAtural Language Understanding system) written in LISP and in MICROPLANNER has been developed on the UNIVAC 1108 computer of the MP‐AI Project, and experimental results have been obtained. This system represents an advanced contribution in the domain of Robotics and it is also a base for developing a more general Natural Language Understanding System.

The paper aims to propose (re)thinking participation, a much-debated issue in urban studies, planning and development, both in its grass-roots and its institutional versions.

The paper draws from two phases of people’s participation concerning the re-use of a former municipal market in Athens, the Agora, in the neighborhood of Kypseli. The two phases represent two different approaches to “participation”: an initiative of local groups implementing participation as a site of radical possibility and an invited or induced process, in which the municipal authorities initiated the whole exercise.

The radically different starting points correspond to equally different perspectives with regard to the stakes and meanings of the respective participatory process, as well as the individuals and groups who are involved.

The paper approaches participation as a spatial process, producing interactions among people, creating emotional or material connections, exchanges and inevitably conflict, re-configuring (public) space and extending (urban) citizenship.

Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data.

The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered.

The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed.

The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.