Innovation and environmental policy: Clean vs. dirty technical change

We study a two sector endogenous growth model with environmental quality with two goods and two factors of production, one clean and one dirty. Technological change creates clean or dirty innovations. We compare the laissez-faire equilibrium and the social optimum and study first- and second-best policies. Optimal policy encourages research toward clean technologies. In a second-best world, we claim that a portfolio that includes a tax on the polluting good combined with optimal innovation subsidy policies is less costly than increasing the price of the polluting good alone. Moreover, a discriminating innovation subsidy policy is preferable to a non-discriminating one.

Web Processing Services for Forestry and Environmental Applications

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Observations on the dipteran fauna in a focus of dermal leishmaniasis in the State of Minas Gerais: II. The distribution of families in relation to their medical, veterinary and economic importance

Miniature light traps used to collect Phlebotominae in a focus of dermal leishmaniasis in the eastern part of the State of Minas Gerais, Brazil. Over a period of seven months, the other Diptera captured in 179 light trap samples were identified to family level. The traps were placed in eight localities which constituted three different biotopes: three woodland aresas, cultivated land, and a peridomestic site. A comparison is made between the totals of Dipeterans collected in each biotope, the total numbers of families collected in each biotope and the estimated indices of diversity. Dendograms representing the degrees of association between families of Diptera in different biotopes are presented. Some families of Diptera are uniformly distributed throughout the study area; a few families seem to have become adapted to areas where human activity has induced the greatest ecological changes. The impact between Dipterans and human well-being is discussed. The availabel evidence indicates that transmission of dermal leishmaniasis does not occur in areas where sand flies can be captured in greatest densities.

Species and Strain-specific Typing of Cryptosporidium Parasites in Clinical and Environmental Samples

Cryptosporidiosis has recently attracted attention as an emerging waterborne and foodborne disease as well as an opportunistic infection in HIV infected individuals. The lack of genetic information, however, has resulted in confusion in the taxonomy of Cryptosporidium parasites and in the development of molecular tools for the identification and typing of oocysts in environmental samples. Phylogenetic analysis of the small subunit ribosomal RNA (SSU rRNA) gene has shown that the genus Cryptosporidium is comprised of several distinct species. Our data show the presence of at least four species: C. parvum, C. muris, C. baileyi and C. serpentis (C. meleagridis, C. nasorum and C. felis were not studied). Within each species, there is some sequence variation. Thus, various genotypes (genotype 1, genotype 2, guinea pig genotype, monkey genotype and koala genotype, etc.) of C. parvum differ from each other in six regions of the SSU rRNA gene. Information on polymorphism in Cryptosporidium parasites has been used in the development of species and strain-specific diagnostic tools. Use of these tools in the characterization of oocysts various samples indicates that C. parvum genotype 1 is the strain responsible for most human Cryptosporidium infections. In contrast, genotype 2 is probably the major source for environmental contamination of environment, and has been found in most oysters examined from Chesapeake Bay that serve as biologic monitors of surface water. Parasites of Cryptosporidium species other than C. parvum have not been detected in HIV+ individuals, indicating that the disease in humans is caused only by C. parvum.

NanoImpactNet - Stakeholders' knowledge needs for research on the health and environmental impact of nanomaterials

NanoImpactNet (NIN) is a multidisciplinary European Commission funded network on the environmental, health and safety (EHS) impact of nanomaterials. The 24 founding scientific institutes are leading European research groups active in the fields of nanosafety, nanorisk assessment and nanotoxicology. This 4−year project is the new focal point for information exchange within the research community. Contact with other stakeholders is vital and their needs are being surveyed. NIN is communicating with 100s of stakeholders: businesses; internet platforms; industry associations; regulators; policy makers; national ministries; international agencies; standard−setting bodies and NGOs concerned by labour rights, EHS or animal welfare. To improve this communication, internet research, a questionnaire distributed via partners and targeted phone calls were used to identify stakeholders' interests and needs. Knowledge gaps and the necessity for further data mentioned by representatives of all stakeholder groups in the targeted phone calls concerned: potential toxic and safety hazards of nanomaterials throughout their lifecycles; fate and persistence of nanoparticles in humans, animals and the environment; risks associated to nanoparticle exposure; participation in the preparation of nomenclature, standards, methodologies, protocols and benchmarks; development of best practice guidelines; voluntary schemes on responsibility; databases of materials, research topics and themes. Findings show that stakeholders and NIN researchers share very similar knowledge needs, and that open communication and free movement of knowledge will benefit both researchers and industry. Consequently NIN will encourage stakeholders to be active members. These survey findings will be used to improve NIN's communication tools to further build on interdisciplinary relationships towards a healthy future with nanotechnology.

NanoImpactNet - building a multi-stakeholder dialogue on the health and environmental impact of nanomaterials

NanoImpactNet (NIN) is a multidisciplinary European Commission funded network on the environmental, health and safety (EHS) impact of nanomaterials. The 24 founding scientific institutes are leading European research groups active in the fields of nanosafety, nanorisk assessment and nanotoxicology. This 4-year project is the new focal point for information exchange within the research community. Contact with other stakeholders is vital and their needs are being surveyed. NIN is communicating with 100s of stakeholders: businesses; internet platforms; industry associations; regulators; policy makers; national ministries; international agencies; standard-setting bodies and NGOs concerned by labour rights, EHS or animal welfare. To improve this communication, internet research, a questionnaire distributed via partners and targeted phone calls were used to identify stakeholders' interests and needs. Knowledge gaps and the necessity for further data mentioned by representatives of all stakeholder groups in the targeted phone calls concerned: • the potential toxic and safety hazards of nanomaterials throughout their lifecycles; • the fate and persistence of nanoparticles in humans, animals and the environment; • the associated risks of nanoparticle exposure; • greater participation in: the preparation of nomenclature, standards, methodologies, protocols and benchmarks; • the development of best practice guidelines; • voluntary schemes on responsibility; • databases of materials, research topics and themes, but also of expertise. These findings suggested that stakeholders and NIN researchers share very similar knowledge needs, and that open communication and free movement of knowledge will benefit both researchers and industry. Subsequently a workshop was organised by NIN focused on building a sustainable multi-stakeholder dialogue. Specific questions were asked to different stakeholder groups to encourage discussions and open communication. 1. What information do stakeholders need from researchers and why? The discussions about this question confirmed the needs identified in the targeted phone calls. 2. How to communicate information? While it was agreed that reporting should be enhanced, commercial confidentiality and economic competition were identified as major obstacles. It was recognised that expertise was needed in the areas of commercial law and economics for a wellinformed treatment of this communication issue. 3. Can engineered nanomaterials be used safely? The idea that nanomaterials are probably safe because some of them have been produced 'for a long time', was questioned, since many materials in common use have been proved to be unsafe. The question of safety is also about whether the public has confidence. New legislation like REACH could help with this issue. Hazards do not materialise if exposure can be avoided or at least significantly reduced. Thus, there is a need for information on what can be regarded as acceptable levels of exposure. Finally, it was noted that there is no such thing as a perfectly safe material but only boundaries. At this moment we do not know where these boundaries lie. The matter of labelling of products containing nanomaterials was raised, as in the public mind safety and labelling are connected. This may need to be addressed since the issue of nanomaterials in food, drink and food packaging may be the first safety issue to attract public and media attention, and this may have an impact on 'nanotechnology as a whole. 4. Do we need more or other regulation? Any decision making process should accommodate the changing level of uncertainty. To address the uncertainties, adaptations of frameworks such as REACH may be indicated for nanomaterials. Regulation is often needed even if voluntary measures are welcome because it mitigates the effects of competition between industries. Data cannot be collected on voluntary bases for example. NIN will continue with an active stakeholder dialogue to further build on interdisciplinary relationships towards a healthy future with nanotechnology.

Exploring the association between Trypanosoma cruzi infection in rural communities and environmental changes in the southern Gran Chaco

The association between land use and land cover changes between 1979-2004 in a 2.26-million-hectare area south of the Gran Chaco region and Trypanosoma cruzi infection in rural communities was analysed. The extent of cultural land, open and closed forests and shrubland up to 3,000 m around rural communities in the north, northwest and west of the province of Córdoba was estimated using Landsat satellite imagery. The T. cruzi prevalence was estimated with a cross-sectional serological survey conducted in the rural communities. The land cover showed the same patterns in the 1979, 1999 and 2004 satellite imagery in both the northwest and west regions, with shrinking regions of cultured land and expanding closed forests away from the community. The closed forests and agricultural land coverage in the north region showed the same trend as in the northwest and west regions in 1979 but not in 1999 or 2004. In the latter two years, the coverage remote from the communities was either constant or changed in opposite ways from that of the northwest and west regions. The changes in closed forests and cultured vegetation alone did not have a significant, direct relationship with the occurrence of rural communities with at least one person infected by T. cruzi. This study suggests that the overall decrease in the prevalence of T. cruzi is a consequence of a combined effect of vector control activities and changes in land use and land cover.

Individual, family and environmental factors associated with pediatric excess weight in Spain: a cross-sectional study.

BACKGROUND There is a growing worldwide trend of obesity in children. Identifying the causes and modifiable factors associated with child obesity is important in order to design effective public health strategies.Our objective was to provide empirical evidence of the association that some individual and environmental factors may have with child excess weight. METHOD A cross-sectional study was performed using multi-stage probability sampling of 978 Spanish children aged between 8 and 17 years, with objectively measured height and weight, along with other individual, family and neighborhood variables. Crude and adjusted odds ratios were calculated. RESULTS In 2012, 4 in 10 children were either overweight or obese with a higher prevalence amongst males and in the 8-12 year age group. Child obesity was associated negatively with the socio-economic status of the adult responsible for the child's diet, OR 0.78 (CI95% 0.59-1.00), girls OR 0.75 (CI95% 0.57-0.99), older age of the child (0.41; CI95% 0.31-0.55), daily breakfast (OR 0.59; p = 0.028) and half an hour or more of physical activity every day. No association was found for neighborhood variables relating to perceived neighborhood quality and safety. CONCLUSION This study identifies potential modifiable factors such as physical activity, daily breakfast and caregiver education as areas for public health policies. To be successful, an intervention should take into account both individual and family factors when designing prevention strategies to combat the worldwide epidemic of child excess weight.

Agriculture, technological change and environmental sustainability: Looking for a win-win water policy strategy

This paper analyses the effects that technological changes in agriculture would have on environmental, social and economic indicators. Specifically, our study is focused on two alternative technological improvements: the modernization of water transportation systems versus the increase in the total factor productivity of agriculture. Using a computable general equilibrium model for the Catalan economy, our results suggest that a water policy that leads to greater economic efficiency is not necessarily optimal if we consider social or environmental criteria. Moreover, improving environmental sustainability depends less on the type of technological change than on the institutional framework in which technological change occurs. Keywords: agricultural technological changes, computable general equilibrium model, economic impact, water policy

Economic and environmental effects of the CO2 taxation: an input-output analysis for Spain

CO2 emissions induced by human activities are the major cause of climate change; hence, strong environmental policy that limits the growing dependence on fossil fuel is indispensable. Tradable permits and environmental taxes are the usual tools used in CO2 reduction strategies. Such economic tools provide incentives to polluting industries to reduce their emissions through market signals. The aim of this work is to investigate the direct and indirect effects of an environmental tax on Spanish products and services. We apply an environmentally extended input-output (EIO) model to identify CO2 emission intensities of products and services and, accordingly, we estimate the tax proportional to these intensities. The short-term price effects are analyzed using an input-output price model. The effect of tax introduction on consumption prices and its influence on consumers’ welfare are determined. We also quantify the environmental impacts of such taxation in terms of the reduction in CO2 emissions. The results, based on the Spanish economy for the year 2007, show that sectors with relatively poor environmental profile are subjected to high environmental tax rates. And consequently, applying a CO2 tax on these sectors, increases production prices and induces a slight increase in consumer price index and a decrease in private welfare. The revenue from the tax could be used to counter balance the negative effects on social welfare and also to stimulate the increase of renewable energy shares in the most impacting sectors. Finally, our analysis highlights that the environmental and economic goals cannot be met at the same time with the environmental taxation and this shows the necessity of finding other (complementary or alternative) measures to ensure both the economic and ecological efficiencies. Keywords: CO2 emissions; environmental tax; input-output model, effects of environmental taxation.

Fishery-induced selection on an Alpine whitefish: quantifying genetic and environmental effects on individual growth rate

Size-selective fishing, environmental changes and reproductive strategies are expected to affect life-history traits such as the individual growth rate. The relative contribution of these factors is not clear, particularly whether size-selective fishing can have a substantial impact on the genetics and hence on the evolution of individual growth rates in wild populations. We analysed a 25-year monitoring survey of an isolated population of the Alpine whitefish Coregonus palaea. We determined the selection differentials on growth rate, the actual change of growth rate over time and indicators of reproductive strategies that may potentially change over time. The selection differential can be reliably estimated in our study population because almost all the fish are harvested within their first years of life, i.e. few fish escape fishing mortality. We found a marked decline in average adult growth rate over the 25 years and a significant selection differential for adult growth, but no evidence for any linear change in reproductive strategies over time. Assuming that the heritability of growth in this whitefish corresponds to what was found in other salmonids, about a third of the observed decline in growth rate would be linked to fishery-induced evolution. Size-selective fishing seems to affect substantially the genetics of individual growth in our study population.

Protective and Aggravating Effects of Nlrp3 Inflammasome Activation in IBD Models: Influence of Genetic and Environmental Factors.

Background: Inflammatory bowel disease (IBD) is characterized by chronic intestinal inflammation due to dysregulation of the mucosal immune system. The cytokines IL-1β and IL-18 appear early in intestinal inflammation and their pro-forms are processed via the caspase-1-activating multiprotein complex, the Nlrp3 inflammasome. Previously, we reported that the uptake of dextran sodium sulfate (DSS) by macrophages activates the Nlrp3 inflammasome and that Nlrp3(-/-) mice are protected in the acute DSS colitis model. Of note, other groups have reported opposing effects in regards to DSS susceptibility in Nlrp3(-/-) mice. Recently, mice lacking inflammasomes were found to develop a distinct intestinal microflora. Methods: To reconcile the contradicting observations, we investigated the role of Nlrp3 deficiency in two different IBD models: acute DSS colitis and TNBS (2,4,6-trinitrobenzene sulfonic acid)-induced colitis. In addition, we investigated the impact of the intestinal flora on disease severity by performing cohousing experiments of wild-type and Nlrp3(-/-) mice, as well as by antibiotic treatment. Results: Nlrp3(-/-) mice treated with either DSS or TNBS exhibited attenuated colitis and lower mortality. This protective effect correlated with an increased frequency of CD103+ lamina propria dendritic cells expressing a tolerogenic phenotype in Nlrp3(-/-) mice in steady state conditions. Interestingly, after cohousing, Nlrp3(-/-) mice were as susceptible as wild-type mice, indicating that transmission of endogenous bacterial flora between the two mouse strains might increase susceptibility of Nlrp3(-/-) mice towards DSS-induced colitis. Accordingly, treatment with antibiotics almost completely prevented colitis in the DSS model. Conclusions: The composition of the intestinal microflora significantly influences disease severity in IBD models comparing wild-type and Nlrp3(-/-) mice. This observation may - at least in part - explain contradictory results concerning the role of the inflammasome in different labs. Further studies are required to define the role of the Nlrp3 inflammasome in noninflamed mucosa under steady state conditions and in IBD.

Genetic and environmental effects on the covariation betweencolour polymorphism and a life history trait

Variation in coloration with a strong underlying genetic basis is frequently found within animal populations but little is known about its function. Covariation between colour polymorphism and life-history traits can arise because morphs perform differently among environments or because they possess alternative alleles coding for key life-history traits. To test these two hypotheses, we studied a population of tawny owls Strix aluco, a bird displaying red, brown and grey morphs. We assessed the colour morph of breeding females, swapped eggs or hatchlings between pairs of nests, and examined how body condition in 3-week-old nestlings covaries with coloration of foster and genetic mothers. Redder foster and genetic mothers produced young in better condition. Because in two other years we observed that greyish females produced offspring in better condition than those of red females, the present study suggests that colour polymorphism signals genetic and phenotypic adaptations to cope with a fluctuating environment.

Plant species richness and environmental heterogeneity in a mountain landscape: effects of variability and spatial configuration

The loss of biodiversity has become a matter of urgent concern and a better understanding of local drivers is crucial for conservation. Although environmental heterogeneity is recognized as an important determinant of biodiversity, this has rarely been tested using field data at management scale. We propose and provide evidence for the simple hypothesis that local species diversity is related to spatial environmental heterogeneity. Species partition the environment into habitats. Biodiversity is therefore expected to be influenced by two aspects of spatial heterogeneity: 1) the variability of environmental conditions, which will affect the number of types of habitat, and 2) the spatial configuration of habitats, which will affect the rates of ecological processes, such as dispersal or competition. Earlier, simulation experiments predicted that both aspects of heterogeneity will influence plant species richness at a particular site. For the first time, these predictions were tested for plant communities using field data, which we collected in a wooded pasture in the Swiss Jura mountains using a four-level hierarchical sampling design. Richness generally increased with increasing environmental variability and "roughness" (i.e. decreasing spatial aggregation). Effects occurred at all scales, but the nature of the effect changed with scale, suggesting a change in the underlying mechanisms, which will need to be taken into account if scaling up to larger landscapes. Although we found significant effects of environmental heterogeneity, other factors such as history could also be important determinants. If a relationship between environmental heterogeneity and species richness can be shown to be general, recently available high-resolution environmental data can be used to complement the assessment of patterns of local richness and improve the prediction of the effects of land use change based on mean site conditions or land use history.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.