Οικονομική της χαρτογράφησης των φυσικών πόρων - η περίπτωση του δασολογίου

One of the many challenges facing electronic 1 system architects is how to provide a cost estimate related to design decisions over the entire life-cycle and product line of the architecture. Various cost modeling techniques may be used to perform this estimation. However, the estimation is often done in an ad-hoc manner, based on specific design scenarios or business assumptions. This situation may yield an unfair comparison of architectural alternatives due to the limited scope of the evaluation. A preferred estimation method would involve rigorous cost modeling based on architectural design cost drivers similar to those used in the manufacturing (e.g. process-based technical cost modeling) or in the enterprise software domain (e.g. COCOMO). This paper describes an initial study of a cost model associated with automotive electronic system architecture. The model's intended use is to evaluate system cost drivers in response to various architectural decisions (e.g. choosing a communication bus topology or mapping a function to hardware). The primary cost driver categories explored are design and development, part fabrication, assembly and in-service costs. The preliminary version of this cost model focuses on describing the key influences on cost, but not the entire mathematical model. The paper presents the cost model with the help of influence diagrams and illustrates the use of the cost modeling methodology through an automotive case study – a steer-by-wire system. As future work, we propose to build a cost model and supporting methodology that accounts for architecture evolution to address the issue of evolving architecture requirements as well as when and where to employ new technology in the architecture.

The use of advanced composites has grown over the last two decades. These high strength, low weight materials can now be found in many structural applications, including bridges. However, the substitution of plastics for conventional materials, such as steel, is governed by engineering constraints and cost competitiveness. A brief discussion of the process variables and engineering constraints is presented in the first chapter. To analyze the latter and to identify cost centers in production more readily, a computerized cost model was developed. This model estimates the cost of producing components by the pultrusion process.;The technical cost modeling methodology employed involves dividing the total cost of the process into the individual cost elements that contribute to the total cost. Based upon theoretical considerations, engineering judgements, and statistically derived relationships, equations are developed to estimate the individual cost elements. Cost elements were divided into two categories: variable and fixed costs. The variable cost elements were raw materials, direct labor and utilities. The fixed cost elements were main machine, auxiliary equipment, tooling, tool setting operator, building, installation, maintenance, overhead and the capital recovery cost of invested capital.;The technical cost model developed can be used to estimate the cost of fabricating different components by pultrusion process. It can also be used for performing sensitivity and comparative cost analysis. An example of sensitivity analyses is presented for a sample bridge deck product which is a new application in the structural area and results in lighter decks (about 75% lighter) and longer service because of lower corrosion rates.

We directly observed a Cobb–Douglas symmetric plane using the index of surface openness, which is used in geography, and successfully identified it. Based on this observation, we measured the capital shares (capital elasticity) and labor shares (labor elasticity) and compared them with the results of multiple regression analysis used in economics. We confirmed consistent agreement in seven countries: Japan, Germany, France, Spain, Italy, the United Kingdom, and the Netherlands. Thus, we show that the Cobb–Douglas production function can be clearly captured in empirical data as a geometric entity with a quasi-inverse symmetry of variables. Based on the above discussion, we theoretically clarified why the Cobb–Douglas production function is better fit to empirical data in economics, because it uniquely derives the fact that their variables follow a power-law distribution.

This article presents an approach to integrate conceptual design and marketing by using a systems engineering approach. A methodology is applied that has successfully linked conceptual design with cost modeling to now also integrate marketing considerations. By combining technical product models and cost models with a market model in one integrated model, an added value is generated: Systems engineers can analyze entire business cases. The paper describes how the market model works, shows the interfaces to conceptual design and cost modeling, and discusses the integration into the overall design process. The full model is applied to the development of the European satellite navigation system “Galileo.” Development teams can easily change parameters in the design and analyze the effects of these changes on, for example, market revenues. To quantify differences among design alternatives, two approaches are considered: (a) a measure of effectiveness (market robustness) and (b) cash‐flow curves to model, simulate, and evaluate entire business cases. © 2002 Wiley Periodicals, Inc. Syst Eng 5: 315–326, 2002

Automated forest mapping is important to understand our forests that play a key role in ecological system. However, efforts towards forest mapping is impeded by difficulty to collect labeled forest images that show large intraclass variation. Recently unsupervised learning has shown promising capability when exploiting limited labeled data. Motivated by this, we propose a progressive unsupervised deep transfer learning method for forest mapping. The proposed method exploits a pre-trained model that is subsequently fine-tuned over the target forest domain. We propose two different fine-tuning mechanism, one works in a totally unsupervised setting by jointly learning the parameters of CNN and the k-means based cluster assignments of the resulting features and the other one works in a semi-supervised setting by exploiting the extracted knearest neighbor based pseudo labels. The proposed progressive scheme is evaluated on publicly available EuroSAT dataset using the relevant base model trained on BigEarthNet labels. The results show that the proposed method greatly improves the forest regions classification accuracy as compared to the unsupervised baseline, nearly approaching the supervised classification approach.

• Analysised the probable potential of MXenes is distinct areas. • Summarized the current MXene synthesis methods. • Covered the challenges in commercial scale MXene production. • A comprehensive feasibility analysis was performed on MXene synthesis. MXenes, a class of two-dimensional materials, exhibit exceptional electrical conductivity, catalytic activity, and rapid charge-discharge characteristics, making them highly promising for energy-related applications, such as supercapacitors, batteries, water splitting, and fuel cell catalysis. Despite extensive research on the physicochemical properties and laboratory-scale synthesis of MXenes, a critical gap remains in understanding the feasibility and economic viability of commercial-scale MXene production. This study addresses this gap by systematically evaluating precursor materials, synthesis routes, scalability constraints, and environmental impacts associated with the manufacturing of MXenes. Through a comparative analysis of synthesis parameters and process economics, critical factors influencing consistent large-scale production were identified. The results demonstrate that optimized acid etching and post-processing methods can achieve high yield and reproducible MXene synthesis, effectively addressing significant scalability challenges. Compared with previous laboratory-focused studies, these findings provide more substantial evidence that industrial-scale MXene production can be both technically feasible and economically viable. The accompanying economic assessment further indicates cost-effectiveness, while market analysis reveals increasing demand across the energy, environmental, and electronic sectors. By integrating insights from technical optimization, cost modeling, and market trends, this work establishes a practical framework for advancing the industrialization of MXene. Overall, the study bridges the gap between laboratory research and real-world application, offering actionable guidance for achieving scalable, sustainable, and commercially competitive MXene manufacturing.

Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers

In recent years, both single-melt plasma arc cold hearth melting (PAM) and electron beam cold hearth melting (EBM) followed by a vacuum arc remelting step have emerged as low-cost alternatives for producing single-melt titanium alloy slabs for critical, non-aerospace applications. Technical cost modeling was used to estimate unit cost, identify principal cost drivers, and perform sensitivity analyses for producing 25.4 mm thick Ti-6Al-4V plates for ballistic-shock resistant applications. The results showed that the principal cost drivers are raw material processing, melt processing, and plate rolling. Sensitivity analyses showed that percent revert and process yield could significantly influence or adversely impact the estimated unit cost. A comparison of the estimated unit cost for a worst case of double VAR (2XVAR) with a best case for either PAM- or EBM-processed 25.4 mm thick plate showed a potential cost savings of 57% or 58%, respectively. These results showed the utility of the technical cost modeling approach in enabling process selection and cost control.

<div class="htmlview paragraph">In order to determine the best way to evaluate materials selection from an economic standpoint, a discussion of conventional cost estimation is given versus a more precise technique, Technical Cost Modeling.</div> <div class="htmlview paragraph">Automotive body panels are used as an application for the costing techniques; conclusions about fabrication costs and parts consolidation are drawn with regard to these parts.</div>

<div class="htmlview paragraph">Aluminum and polymer composites have long been considered the materials of choice for achieving mass reduction in automotive structures. As consumer and government demand for mass reduction grows, the use of these materials, which have traditionally been more expensive than the incumbent steel, becomes more likely. In response to this growing challenge, the international steel community has joined forces to develop the Ultra Light Steel Auto Body (ULSAB). The resulting design saves mass and increases performance relative to current steel unibodies. Although mass savings are not as dramatic as those achieved by alternative materials, this design offers the potential to be accompanied by a manufacturing cost reduction.</div> <div class="htmlview paragraph">The projected manufacturing piece and investment cost for the ULSAB are investigated using technical cost modeling. The results presented here examine the elements that contribute to the cost, including treatments for stamping, hydroforming, assembly and purchased parts. Particular emphasis is placed on the procedure for laying out the design of the manufacturing and production facility based on the respective manufacturing processes, production rates, labor requirements, equipment and tooling costs, and so on.</div> <div class="htmlview paragraph">The results of this paper provide material suppliers and automakers with a framework for understanding the technical and economic advances that are possible with the implementation of the ULSAB technology approach. Through this understanding, decisions regarding investments of limited human and financial capital can be implemented.</div>

A methodological study for biotope and landscape mapping based on CIR aerial photographs

Scientific, technological, and economic data are investigated for 128 countries. A stepwise regression analysis is carried out on the data, using domestic patent counts as the dependent variable. The form of the regression equations is the Cobb-Douglas production function. The analysis shows that domestic patents (as indicator of national technological capacity, and treated here as the dependent variable) are closely related to GNP (a measure of national economic size), counts of scientific publications (an indicator of scientific capacity), and counts of national patents obtained in the U.S. (a measure of world class technological capacity). Together, these three independent variables account for more than 92 percent of the variance in the dependent variable.

The current demand for food has been met through the exploitation of natural reserves. Brazil has 26% of its extension occupied by agricultural uses, 62% of which are pastures. Degraded pastures have greater land use intensity than well-managed pastures, leading to greater degradation of the environment. Land use classification systems consider that pastures are well managed, a misconception for the Brazilian reality. Based on this approach, it was aimed to develop a methodology for mapping the intensity of land use by pasture via remote sensing. The method of mapping was developed and validated in basins with different soil and climatic characteristics. Three calibrations were performed based on NDVI values to ascertain the influence on the results, being evaluated from the field campaigns and the kappa and weighted kappa indices. The kappa and weighted kappa indices presented reasonable and moderate agreement, respectively. The results were considered as satisfactory for the three calibrations, evidencing that the degree of degradation of the pastures can be estimated in a simple way by remote sensing. The Limoeiro River Basin has around 46.9% of pastures, at least, heavily degraded and 96.6% with some degree of degradation, which contributes to degradation of the natural resources and reduction of livestock farming and economic potential of the basin.

The Soil Map of the Russian Federation, 1 : 2.5 M scale (1988) requires updating to include soil data that have been accumulated in the past decades, reflect real changes in the soil cover, including anthropogenic transformation, and ensure precise localization of soil objects and correspondence of the map to satellite data with the use of digital soil mapping technologies. The substantive-genetic classification system of Russian soils (2004, 2008) provides the conceptual basis for this updating. The conversion of soil information from the initial map of 1988 into the new classification system is being performed for each polygon of the digitized map. It is based on the analysis of a vast body of diverse information and includes both the search for analogues of the names of mapping units in the new classification system (renaming of the soils) and the correction of the composition of soils in the polygons: new natural soils, cultivated soils (agrosoils), and urban soils are added to the attribute database. The largest number of new natural soils has appeared in legend sections “Soils of tundra” and “Soils of taiga and broadleaved forests”. Anthropogenic soils (119 legend units) that are shown on the map for the first time, have their maximum representation (36 units) in the section “Soils of steppes”; it is close to the number of natural soils (37 units) in this zone. A considerable percent of anthropogenic soils (> 50% of the natural soils) is also typical of legend sections “Soils of broadleaved forests and forest-steppes,” “Soils of dry steppes and semideserts,” “Salt-affected and solonetzic soils”. The total number of natural and anthropogenic soil units (425) in the new legend is more than twice as large as the initial number of natural soil units in the base map (205). The results of the renaming and updating of soils for each soil polygon are fixed in a separate section of the attribute database to the map and will be used for generating the new map by the methods of digital soil mapping.

<strong class="journal-contentHeaderColor">Abstract.</strong> Young forest age mapping at a fine spatial resolution is important for increasing the accuracy of estimating land-atmosphere carbon fluxes and guiding forest management practices. In recent decades, China has actively conducted afforestation and forest protection projects, thereby, laying the foundation for the realization of carbon neutrality. However, very few studies have been conducted which map the ages of young forests for the whole of China at a fine spatial resolution. In this research, a continuous change detection and classification (CCDC)-based method suitable for large-scale forest age mapping is proposed, and used to estimate young forest ages across China in 2020 at a spatial resolution of 30 m. First, a 10 m spatial resolution land cover dataset (WorldCover2020) from the European Space Agency (ESA) was used to determine the forest cover areas in 2020. Then, the CCDC algorithm was used to identify stand-replacing disturbances to determine the stand age based on 436,967 Landsat tiles across China from 1990 to 2020. A validation sample set composed of multiple land use/land cover (LULC) products was used to calculate the overall accuracy (OA) of the 2020 young forest age (1&ndash;31 years) map of China, and the OA was 90.28 %. The reliability and applicability of the proposed CCDC-based forest age mapping method was validated by comparing the forest age map with Hansen&rsquo;s forest change dataset, Max Planck Institute for Biogeochemistry (MPI-BGC) 1 km global forest age datasets and field measurements. The CCDC-based method has strong application potential in real-time mapping of the age of young forests at the global scale. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services, and important guidance for government departments, such as the National Forestry and Grassland Administration and National Development and Reform Commission in China.