MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.Comment: 16 page

Pavement maintenance optimization strategies for national road network in Indonesia applying genetic algorithm

"6th International Workshop on Performance, Protection & Strengthening of Structures under Extreme Loading, PROTECT2017, 11-12 December 2017, Guangzhou (Canton), China"In Indonesia, the national road has an important role to increase the region's economy, the road has the function to preserve interprovincial or inter-provincial and regencies/cities. Road network in Indonesia has a significant length, of approximately 516,239 kilometers, where the majority presents lack of information related to monitoring data and evaluation. As a consequence, road maintenance is not appropriated. The objective of this paper is to describe the development of a Genetic Algorithm (GA) based on multi objectives programming of pavement and to investigate the optimal maintenance strategy options applied as function of road surface distress conditions. This is supported by database of an Integrated Road Management System (IRMS) and taking into account of both road network condition and agency costs. The optimization strategies provided by the developed soft computing tool can help solving agency problems; minimizing costs and maximizing road services.The Authors are grateful to the Lembaga Pengelola Dana Pendidikan (LPDP) for the financial support provided to this thesis through the grant financed by the Ministry of Finance Republic of Indonesia.info:eu-repo/semantics/publishedVersio

Multiresidue Method for the Simultaneous Analysis of Antibiotics and Mycotoxins in Feeds by Ultra-High Performance Liquid Chromatography Coupled to Tandem Mass Spectrometry

Residues in animal feeds and foods of animal origin have been important safety issue concerning both human and animal health. A multiresidue method for determination of eight mycotoxins and ten antibiotics was developed and validated in animal feeds by using QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction followed by UHPLC-MS/MS. Optimisation of UHPLC-MS/MS parameters was performed to achieve good separation and resolution. The method was validated according to the European Commission Decision 2002/657/EC. Matrix matched calibration curves showed good r2 (≥0.995) values, and limit of quantification (LOQ) values varied between 1.2 and 5.2 mg kg1. Average recoveries ranged from 60 to 102% with relative standard deviations of 2.2 and 15.6% for all type of feed samples except for tetracyclines, lincomycin, tylosin, ochratoxin A, and fumonisin (B1 and B2)

Multi-objective optimization of confidence-based localization in large-scale underwater robotic swarms.

Localization in large-scale underwater swarm robotic systems has increasingly attracted research and industry communities’ attention. An optimized confidence-based localization algorithm is proposed for improving localization coverage and accuracy by promoting robots with high confidence of location estimates to references for their neighboring robots. Confidence update rules based on Bayes filters are proposed based on localization methods’ error characteristics where expected localization error is generated based on measurements such as operational depth and traveled distance. Parameters of the proposed algorithm are then optimized using the Evolutionary Multi-objective Optimization algorithm NSGA-II for localization error and trilateration utilization minimization while maximizing localization confidence and Ultra-Short Base Line utilization. Simulation studies show that a wide localization coverage can be achieved using a single Ultra-Short Base Line system and localization mean error can be reduced by over 45% when algorithm’s parameters are optimized in an underwater swarm of 100 robots

Multi-objective genetic algorithm applied to spectroscopic ellipsometry of organic-inorganic hybrid planar waveguides

The applicably of multi-objective optimization to ellipsometric data analysis is presented and a method to handle complex ellipsometric problems such as multi sample or multi angle analysis using multi-objective optimization is described. The performance of a multi-objective genetic algorithm (MOGA) is tested against a single objective common genetic algorithm (CGA). The procedure is applied to the characterization (refractive index and thickness) of planar waveguides intended for the production of optical components prepared sol-gel derived organic-inorganic hybrids, so-called di-ureasils, modified with zirconium tetrapropoxide, Zr(OPr(n))(4) deposited on silica on silicon substrates. The results show that for the same initial conditions, MOGA performs better than the CGA, showing a higher success rate in the task of finding the best final solution. (C) 2010 Optical Society of AmericaFCTFEDERPTDC/CTM/72093/2006SFRH/BD/41943/2007MP070

İzmir‐Ankara suture as a Triassic to Cretaceous plate boundary – data from central Anatolia

The İzmir‐Ankara suture represents part of the boundary between Laurasia and Gondwana along which a wide Tethyan ocean was subducted. In northwest Turkey, it is associated with distinct oceanic subduction‐accretion complexes of Late Triassic, Jurassic and Late Cretaceous ages. The Late Triassic and Jurassic accretion complexes consist predominantly of basalt with lesser amounts of shale, limestone, chert, Permian (274 Ma zircon U‐Pb age) metagabbro and serpentinite, which have undergone greenschist facies metamorphism. Ar‐Ar muscovite ages from the phyllites range from 210 Ma down to 145 Ma with a broad southward younging. The Late Cretaceous subduction‐accretion complex, the ophiolitic mélange, consists of basalt, radiolarian chert, shale and minor amounts of recrystallized limestone, serpentinite and greywacke, showing various degrees of blueschist facies metamorphism and penetrative deformation. Ar‐Ar phengite ages from two blueschist metabasites are ca. 80 Ma (Campanian). The ophiolitic mélange includes large Jurassic peridotite‐gabbro bodies with plagiogranites with ca. 180 Ma U‐Pb zircon ages. Geochronological and geological data show that Permian to Cretaceous oceanic lithosphere was subducted north under the Pontides from the Late Triassic to the Late Cretaceous. This period was characterized generally by subduction‐accretion, except in the Early Cretaceous, when subduction‐erosion took place. In the Sakarya segment all the subduction accretion complexes, as well as the adjacent continental sequences, are unconformably overlain by Lower Eocene red beds. This, along with the stratigraphy of the Sakarya Zone indicate that the hard collision between the Sakarya Zone and the Anatolide‐Tauride Block took place in Paleocene

Brief communication: Lessons learned and experiences gained from building up a global survey on societal resilience to changing droughts

This paper describes the process of creating a global survey of experts to evaluate drought resilience indicators. The lessons learned include five main points: (1) the heterogeneity in the conceptual background should be minimized before the construction of the survey; (2) large numbers of indicators decrease the engagement of respondents through the survey, and ways to apportion indicators whilst maintaining reliability should be considered; (3) it is necessary to design the survey to balance response rate and accuracy; (4) the survey questions should have clear statements with a logical and flowing structure; and (5) reaching experts with different domain experience and representing different regions is difficult but crucial to minimize biased results.</p