scholarly journals A Hybrid Genetic Programming Method in Optimization and Forecasting: A Case Study of the Broadband Penetration in OECD Countries

2012 ◽  
Vol 2012 ◽  
pp. 1-32 ◽  
Author(s):  
Konstantinos Salpasaranis ◽  
Vasilios Stylianakis

The introduction of a hybrid genetic programming method (hGP) in fitting and forecasting of the broadband penetration data is proposed. The hGP uses some well-known diffusion models, such as those of Gompertz, Logistic, and Bass, in the initial population of the solutions in order to accelerate the algorithm. The produced solutions models of the hGP are used in fitting and forecasting the adoption of broadband penetration. We investigate the fitting performance of the hGP, and we use the hGP to forecast the broadband penetration in OECD (Organisation for Economic Co-operation and Development) countries. The results of the optimized diffusion models are compared to those of the hGP-generated models. The comparison indicates that the hGP manages to generate solutions with high-performance statistical indicators. The hGP cooperates with the existing diffusion models, thus allowing multiple approaches to forecasting. The modified algorithm is implemented in the Python programming language, which is fast in execution time, compact, and user friendly.

2016 ◽  
Vol 21 (1) ◽  
pp. 47-55
Author(s):  
Konstantinos Salpasaranis ◽  
Vasilios Stylianakis

Abstract This paper presents the implementation of a modified Genetic Programming (GP) method in forecasting fixed broadband telecommunications penetration percentage in Organisation for Economic Co-operation and Development (OECD) countries. The specific GP method combines the use of known diffusion models for technological forecasting purposes, such as Logistic, Gompertz and Bass and the GP. The combination method produces both time dependant and causal models with high performance statistical indicators. Also, multiple approaches to forecasting can be implemented, mainly with no big datasets.


2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Konstantinos Salpasaranis ◽  
Vasilios Stylianakis ◽  
Stavros Kotsopoulos

This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers’ population. The method constitutes an expansion of the hybrid Genetic Programming (hGP) method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and LogInLog. In addition, the aforementioned functions and models expand the function set of hGP. The application of the method in combination with macroeconomic indicators such as Gross Domestic Product per Capita (GDPpC) and Consumer Prices Index (CPI) leads to the creation of forecasting models and scenarios for medium- and long-term level of predictability. The forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. The implementation of the modified-hGP in the datasets of mobile subscribers in the Organisation for Economic Cooperation and Development (OECD) countries shows very satisfactory forecasting performance.


2016 ◽  
Vol 167 (5) ◽  
pp. 294-301
Author(s):  
Leo Bont

Optimal layout of a forest road network The road network is the backbone of forest management. When creating or redesigning a forest road network, one important question is how to shape the layout, this means to fix the spatial arrangement and the dimensioning standard of the roads. We consider two kinds of layout problems. First, new forest road network in an area without any such development yet, and second, redesign of existing road network for actual requirements. For each problem situation, we will present a method that allows to detect automatically the optimal road and harvesting layout. The method aims to identify a road network that concurrently minimizes the harvesting cost, the road network cost (construction and maintenance) and the hauling cost over the entire life cycle. Ecological issues can be considered as well. The method will be presented and discussed with the help of two case studies. The main benefit of the application of optimization tools consists in an objective-based planning, which allows to check and compare different scenarios and objectives within a short time. The responses coming from the case study regions were highly positive: practitioners suggest to make those methods a standard practice and to further develop the prototype to a user-friendly expert software.


2019 ◽  
pp. 123-130

The scientific research works concerning the field of mechanical engineering such as, manufacturing machine slate, soil tillage, sowing and harvesting based on the requirements for the implementation of agrotechnical measures for the cultivation of plants in its transportation, through the development of mastering new types of high-performance and energy-saving machines in manufacturing machine slate, creation of multifunctional machines, allowing simultaneous soil cultivation, by means of several planting operations, integration of agricultural machine designs are taken into account in manufacturing of the local universal tractor designed basing on high ergonomic indicators. For this reason, this article explores the use of case studies in teaching agricultural terminology by means analyzing the researches in machine building. Case study method was firstly used in 1870 in Harvard University of Law School in the United States. Also in the article, we give the examples of agricultural machine-building terms, teaching terminology and case methods, case study process and case studies method itself. The research works in the field of mechanical engineering and the use of case studies in teaching terminology have also been analyzed. In addition, the requirements for the development of case study tasks are given in their practical didactic nature. We also give case study models that allow us analyzing and evaluating students' activities.


2020 ◽  
Vol 15 ◽  
Author(s):  
Akshatha Prasanna ◽  
Vidya Niranjan

Background: Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology have facilitated an accelerated development in clinical microbiology and our understanding of pathogens. Objective: For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification. Method: The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple command-line tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample. Results: The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value. Conclusion: Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.


Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3716
Author(s):  
Francesco Causone ◽  
Rossano Scoccia ◽  
Martina Pelle ◽  
Paola Colombo ◽  
Mario Motta ◽  
...  

Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yehe Liu ◽  
Andrew M. Rollins ◽  
Richard M. Levenson ◽  
Farzad Fereidouni ◽  
Michael W. Jenkins

AbstractSmartphone microscopes can be useful tools for a broad range of imaging applications. This manuscript demonstrates the first practical implementation of Microscopy with Ultraviolet Surface Excitation (MUSE) in a compact smartphone microscope called Pocket MUSE, resulting in a remarkably effective design. Fabricated with parts from consumer electronics that are readily available at low cost, the small optical module attaches directly over the rear lens in a smartphone. It enables high-quality multichannel fluorescence microscopy with submicron resolution over a 10× equivalent field of view. In addition to the novel optical configuration, Pocket MUSE is compatible with a series of simple, portable, and user-friendly sample preparation strategies that can be directly implemented for various microscopy applications for point-of-care diagnostics, at-home health monitoring, plant biology, STEM education, environmental studies, etc.


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