Simulation of the Electrochemical Machining Process

Electrochemical machining (ECM) or erosion, is a process for shaping materials by means of the anodic dissolution of a work-piece using suitably shaped cathodes? However, the predictability of the process is poor due to current density variations over the electrode contour leading to poor dimensional tolerances. This paper describes how the process can be entirely simulated by computer. A model of the electric field during erosion is constructed based on the Laplace equations for the field. From the distribution of the electric field, it is possible to continuously calculate the current density at each point on the work-piece for the whole machining process. In this way, it is possible to predict the final work-piece contour by running the simulation program instead of the real process. Simulations for cylindrical, conical and spherical electrodes were carried out and compared to actual eroded parts.Mechanical Engineerin

Report of the NAFO Commission Ad hoc Working Group to Reflect on the Rules Governing Bycatches, Discards and Selectivity (WG-BDS) in the NAFO Regulatory Area Meeting

1. Opening by the Chair, Temur Tairov (Russian Federation) 2. Appointment of Rapporteur 3. Adoption of Agenda 4. Discussion of the bycatch analysis performed by Scientific Council and the Secretariat 5. Action Plan in the Management and Minimization of Bycatch and Discards 6. Other Matters a. STACTIC Intersessional Meeting, May 2017 b. WG-CR/CDAG Meeting, February and May 2017 c. NAFO Working Group on Improving Efficiency of NAFO Working Group Process 7. Recommendations to forward to the Commission 8. Adoption of Report 9. Adjournmen

Company success estimation during economic crisis: an artificial neural network based approach

Makale Endüstri Mühendisliği dergisinin "YA/EM 2008 özel sayısı"nda yayımlanmıştır.Bu çalışmanın amacı, ekonomik kriz dönemlerinde firma başarısının tahmin edilebilmesi için yapay sinir ağları (YSA) tabanlı bir yöntem önerisi sunmaktır. Yazında firmaların kriz dönemlerini başarı ile atlatabilmeleri konusunda çok sayıda çalışma yer almaktadır. Başarı veya başarısızlık yorumu, firmaların kriz dönemlerinden önce sahip oldukları finansal göstergeler temel alınarak yapılabilir, bu göstergeler aynı zamanda firma başarısı üzerine tahmin yapmayı sağlar. Bu çalışmada, 2001 yılında Türkiye'de meydana gelen ekonomik krizden önceki yıl içerisinde firmaların sahip oldukları finansal göstergeler üzerinden, bu süreci başarı ile atlatabilmiş olma durumuna göre bir YSA modeli geliştirilmiştir. Çalışmamızda, kalkınmada öncelikli sektör olması nedeniyle imalat firmalarından oluşturulan türdeş bir veri seti ile prototip bir model önerisi tasarlanmıştır.The purpose of this paper is to present an artificial neural network (ANN) based method for the estimation of company success during economic crisis periods. There are a number of studies about recovering the crisis successfully by companies. Success or failure interpretation can be made upon financial indicators of companies for the period before the crisis. These indicators also provide estimation about the success of the companies. In this study, an ANN model is proposed estimating the success of companies after crisis period by means of financial indicators of the companies for the year before 2001 economic crisis of Turkey. A prototype model is designed with regards to homogeneous data set retrieved from the companies facilitating in manufacturing sector

Strategies in PRholog

PRholog is an experimental extension of logic programming with strategic conditional transformation rules, combining Prolog with Rholog calculus. The rules perform nondeterministic transformations on hedges. Queries may have several results that can be explored on backtracking. Strategies provide a control on rule applications in a declarative way. With strategy combinators, the user can construct more complex strategies from simpler ones. Matching with four different kinds of variables provides a flexible mechanism of selecting (sub)terms during execution. We give an overview on programming with strategies in PRholog and demonstrate how rewriting strategies can be expressed

Metaheuristic optimization of reinforced concrete footings

The primary goal of an engineer is to find the best possible economical design and this goal can be achieved by considering multiple trials. A methodology with fast computing ability must be proposed for the optimum design. Optimum design of Reinforced Concrete (RC) structural members is the one of the complex engineering problems since two different materials which have extremely different prices and behaviors in tension are involved. Structural state limits are considered in the optimum design and differently from the superstructure members, RC footings contain geotechnical limit states. This study proposes a metaheuristic based methodology for the cost optimization of RC footings by employing several classical and newly developed algorithms which are powerful to deal with non-linear optimization problems. The methodology covers the optimization of dimensions of the footing, the orientation of the supported columns and applicable reinforcement design. The employed relatively new metaheuristic algorithms are Harmony Search (HS), Teaching-Learning Based Optimization algorithm (TLBO) and Flower Pollination Algorithm (FPA) are competitive for the optimum design of RC footings

Time-resolved dual transcriptomics reveal early induced Nicotiana benthamiana root genes and conserved infection-promoting Phytophthora palmivora effectors

BACKGROUND: Plant-pathogenic oomycetes are responsible for economically important losses in crops worldwide. Phytophthora palmivora, a tropical relative of the potato late blight pathogen, causes rotting diseases in many tropical crops including papaya, cocoa, oil palm, black pepper, rubber, coconut, durian, mango, cassava and citrus. Transcriptomics have helped to identify repertoires of host-translocated microbial effector proteins which counteract defenses and reprogram the host in support of infection. As such, these studies have helped in understanding how pathogens cause diseases. Despite the importance of P. palmivora diseases, genetic resources to allow for disease resistance breeding and identification of microbial effectors are scarce. RESULTS: We employed the model plant Nicotiana benthamiana to study the P. palmivora root infections at the cellular and molecular levels. Time-resolved dual transcriptomics revealed different pathogen and host transcriptome dynamics. De novo assembly of P. palmivora transcriptome and semi-automated prediction and annotation of the secretome enabled robust identification of conserved infection-promoting effectors. We show that one of them, REX3, suppresses plant secretion processes. In a survey for early transcriptionally activated plant genes we identified a N. benthamiana gene specifically induced at infected root tips that encodes a peptide with danger-associated molecular features. CONCLUSIONS: These results constitute a major advance in our understanding of P. palmivora diseases and establish extensive resources for P. palmivora pathogenomics, effector-aided resistance breeding and the generation of induced resistance to Phytophthora root infections. Furthermore, our approach to find infection-relevant secreted genes is transferable to other pathogen-host interactions and not restricted to plants.This work was supported by the Gatsby Charitable Foundation (RG62472), by the Royal Society (RG69135) and by the European Research Council (ERC-2014-STG, H2020, 637537)

Food waste treatment option selection through spherical fuzzy AHP

In line with the increase in consciousness on sustainability in today's global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions