Establishment of a New Urban Solid Waste Management Programs in Mazandaran Province, North of Iran

This study reports residents’ preferences to establish a new urban solid waste management programs results from a double-bounded dichotomous choice contingent valuation method and choice experiment in Mazandaran province, north of Iran. In order to analysis the residents’ preferences, a dichotomous hypothetical market and a choice sets with different attributes and options were used For estimation of two mentioned methods, the normal logit and conditional logit were applied. In addition, an empirical comparison of the welfare measures derived from the doublebounded DC-CVM and CE is conducted. The main results show that there is no significant difference between the values derived from the two methods. The mean of WTP to establish a new solid waste management programs in CV and CE were estimated 2.45 and 2.61 US$, respectively, per a person per a month. Also the estimated marginal WTP for all attributes in CE was 8.1 US$ per a month. The results suggest that both double-bounded DC-CVM and CE can be successfully stablished for improvement environmental level quality in Mazandaran province. This paper could provide the basis for further development of other new programs on sustainable urban management of solid waste in Mazandaran province.Keywords: Dichotomous choice, Willingness to pay, Solid waste management, Mazandaran province, Ira

Land change detection and effective factors on forest land use changes: application of land change modeler and multiple linear regression

Reducing forest covered areas and changing it to pasture, agricultural, urban and rural areas is performed every year and this causes great damages in natural resources in a wide range. In order to identify the effective factors on reducing the forest cover area, multiple regression was used from 1995 to 2015 in Mazandaran forests. A Multiple regressions can link the decline in forest cover (dependent variable) and its effective factors (independent variable) are well explained. In this study, Landsat TM data of 1995 and Landsat ETM+ data of 2015 were analyzed and classified in order to investigate the changes in the forest area. The images were classified in two classes of forest and non-forest areas and also forest map with spatial variables of physiography and human were analyzed by regression equation. Detection satellite images showed that during the studied period there was found a reduction of forest areas up to approximately 257331 ha. The results of regression analysis indicated that the linear combination of income per capita, rain and temperature with determined coefficient 0.4 as independent variables were capable of estimating the reduction of forest area. The results of this study can be used as an efficient tool to manage and improve forests regarding physiographical and human characteristics.Keywords: Land change Modeler, Multiple linear regression, remote sensing, Mazandaran forest

How much is the use values of forest ecosystem services? Case study: north forests of Iran

Forests play a significant role for human’s well-being. Economists’ attention is mostly drawn on the market value of forest products. The trend, however, is changing as non-market values of forests are increasingly appreciated and measured. Recently, the ecosystem value of forest has been studied by natural resource economists and its role on human welfare is ensured. This paper indicates that the annual use value of the ecosystem services such as water conservation, soil protection, carbon fixation, nutrient cycling, water purification, air pollution absorption and recreation provided by forests is not only worth millions of dollars, but also in per hectare terms much more than hitherto known. This value for the Mazandaran forest reserve (MFR) ranged US$14.2–14.8 million or about US$ 6676.9–6785.6 per ha. If these are accounted for, then governments and societies faced with the development versus conservation dilemma can create more understanding decisions and policies that will assist conserve forests and the ecosystem services they provide, and thereby promulgate human well-being and sustainable development. Realization about these significant intangible benefits will assist in more informed decisions and policies that will help conserve forest ecosystems and the services they provide as well as promote human well-being and sustainable development

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Economic analysis of land use changes in forests and rangelands: Developing conservation strategies

Forests and rangelands are economically and environmentally important due to the production of goods and ecosystem services, and any changes in their nature requires a comprehensive evaluation and analysis. The objectives of this study include: a) conducting a cost-benefit analysis of land use change in the forests and rangelands of the Caspian vegetative area with regard to the environmental costs along with other costs and benefits, b) estimating the environmental damage of land use change, and c) developing appropriate strategies for the conservation of the forests and rangelands. The value of costs and benefits of the change was calculated from 2005 to 2016 using the average annual inflation rate up to 2035. The results of the evaluation model show that among the 6 scenarios of land use change, 4 scenarios are completely economically non-viable and two scenarios (forest and rangeland change to residential) are viable with regard to environmental considerations. Moreover, during this period, the change of forest and rangeland for other uses were respectively 43,732 and 33,466 ha. Accordingly, considering the average annual value of hectares of forest and rangeland in terms of producing goods and ecological services, the loss incurred was about 334 and 160 million USD. © 2019 Elsevier Lt

Mitosis domain generalization in histopathology images - The MIDOG challenge

The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F(1) score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task

Mitosis domain generalization in histopathology images — The MIDOG challenge

The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task

A multi-organ nucleus segmentation challenge

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics