Assessing the Influence of Fashion Clothing Advertising on Women's Consumer Behaviour in Finland; a case study of H&M

This study will provide a framework for analysing the current advertising and marketing patterns in women’s consumer behaviour in Finland. Swedish clothing retailer Hennes&Mauritz (H&M) was chosen as a case study since it is considered to be well-known in Finland; in 2010 average sales were astonishingly around 243million euros (H&M 2012). As this research is considered to be a broad topic, this study will focus on women in Finland aged 16-35 and above. This study will use various research methods such as case study, interview and a survey to analyse the dissertation topic. The researcher is confident that using these methods, the study will successfully contribute to the current literature on ‘’ Assessing the influence of fashion clothing advertising on women’s consumer behaviour in Finland; case study of H&M

Remote sensing-based operational modeling of fuel ignitability in Hyrcanian mixed forest, Iran

To date, the efficiency and effectiveness of early warning systems of satellite imagery for preventing and mitigating wildfire remain a challenging issue. The heat of pre-ignition (Qig) can be an index of fire likelihood, which is further enhanced with remotely sensed data, active fire data, and fuels information for operational application of satellite imagery in fire early warning systems. Qig is a prerequisite for forest fires by the side of ignition sources and weather. This study analyzed the effect of Qig variation on fire occurrences to develop a remote sensing-based initial fire likelihood index for identifying areas that have a high probability of fire. In this study, Qig of Rothermel’s fire spread model daily data is retrieved at 1 km pixels from MODIS data. MODIS active fire products were used to interpret the Qig of fuels for 10 days before the days of fire occurrences in November 2010 to determine the pre-fire conditions. A formula for converting Qig into an initial fire likelihood index (IFLI) was then used by binary logistic regression method. Analyses show that there was a positive association between suggested IFLI and fire occurrences during the study period with a fair diagnostic accuracy of 92%, and 80% for dead and live fuels, respectively. Mann–kendall test suggested that there are significant trends in the fuel moisture content time-series for both live and dead fuels. Further analysis using the Hosmer–Lemeshow test represents that the models showed an acceptable fit. The suggested IFLI is an effective tool for fire management decision-making whenever a near real-time fire likelihood is required

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%–12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%–33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations

Modelling static fire hazard in a semi-arid region using frequency analysis

Various fire hazard rating systems have been used by many countries at strategic and tactical levels for fire prevention and fire safety programs. Assigning subjective weight to parameters that cause fire hazard has been widely used to model wildland fire hazard. However, these methods are sensitive to experts' judgements because they are independent of any statistical approaches. Therefore, in the present study, we propose a wildland fire hazard method based on frequency analysis (i.e. a probability distribution model) to identify the locations of fire hazard in north-eastern Iran, which has frequent fire. The proposed methodology uses factors that do not change or change very slowly over time to identify static fire hazard areas, such as vegetation moisture, slope, aspect, elevation, distance from roads and proximity to settlements, as essential parameters. Several probability distributions are assigned to each factor to show the possibility of fire using non-linear regressions. The results show that approximately 86% of MODerate-resolution Imaging Spectroradiometer (MODIS) hot spot data are located truly in the high fire hazard areas as identified in the present study and the most significant contributing factor to fire in Golestan Province, Iran, is elevation. The present study also reveals that approximately 14% of the total study area (∼20368km2) has a fire hazard of 66%, which can be considered very high. Therefore, this area - located mostly in the central, west and north-east regions of Golestan Province - should be considered for an effective conservation strategy of wildland fire

Assessing fire hazard potential and its main drivers in Mazandaran province, Iran: a data-driven approach

Fires are a major disturbance to forest ecosystems and socioeconomic activities in Mazandaran province, northern Iran, particularly in the Hyrcanian forest sub-region. Mapping the spatial distribution of fire hazard levels and the most important influencing factors is crucial to enhance fire management strategies. In this research, MODIS hotspots were used to represent fire events covering Mazandaran Province over the period 2000-2016. We applied the ecological niche theory through the maximum entropy (MaxEnt) method to estimate fire hazard potential and the association with different anthropogenic and biophysical conditions, by applying different modeling approaches (heuristic, permutation, and jackknife metrics). Our results show that higher fire likelihood is related to density of settlements, distance to roads up to 3 km and to land cover types associated with agricultural activities, indicating a strong influence of human activities in fire occurrence in the region. To decrease fire hazard, prevention activities related to population awareness and the adjustment of farming practices need to be considered.info:eu-repo/semantics/publishedVersio

Measurement and modeling of particulate matter concentrations: Applying spatial analysis and regression techniques to assess air quality

This paper presented the levels of PM2.5 and PM10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM2.5 and PM10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Universal Kriging (UK) were used to investigate the status of air pollution in the city. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed to compare the four models. The results showed that the PM2.5 concentrations in the stations were between 10 and 500 μg/m3. Furthermore, the PM10 concentrations for all of 48 stations ranged from 20 to 1500 μg/m3. The concentrations obtained for the period of nine months were greater than the standard limits. There was difference in the values of MAPE, RMSE, MBE, and MAE. The results indicated that the MAPE in IDW method was lower than other methods: (41.05 for PM2.5 and 25.89 for PM10). The best interpolation method for the particulate matter (PM2.5 and PM10) seemed to be IDW method. • The PM10 and PM2.5 concentration measurements were performed in the period of warm and risky in terms of particulate matter at 2016. • Concentrations of PM2.5 and PM10 were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000. • Interpolation is used to convert data from observation points to continuous fields to compare spatial patterns sampled by these measurements with spatial patterns of other spatial entities

Applying Surface Fractal Analysis (SFA) in analysis of surface anomalies and its relation with changes in morphotectonic zones in the margin of the High Zagros Belt (HZB)

Fractal geometry is a method for describing a self-similar or a self-affine property in complex landforms and explanation of surface complexities and roughness. In the present study, the surface fractal dimensions (SFDs) were investigated by a cellular model by covering the divider method. Results indicated that geological and geomorphological processes change the character of the fractal dimension of the landforms. Changes in lithologic boundaries and faults influence changes in the fractal dimension and their mode of influence vary according to the topographic characters such as frequency, amplitude, and  types of formations. In lithologic units with hard limestone formations, the fractal dimension is low, while in alluvial formations, the fractal dimension increases. The drainage network density and tributaries margins affect the fractal dimension. Moreover, homogeneity of the lithologic units decreases the fractal dimension. In this study, the lowest fractal dimension is associated with the integrated units of Mesozoic orbitolina limestones on the border of the two structural zones of Sanandaj-Sirjan and High Zagros belt. However, friable and sensitive to erosion formations of the quaternary increase the fractal dimension. The succession of the hard and friable layers is effective on the local scale on the fractal dimension. Furthermore, mountains have lower fractal dimensions than lowlands. Generally, there is an inverse relationship between the fractal dimension and elevation and this relationship there is about the roughness index in the basin. The results illustrated that changes in the surface fractal dimension were dependent on a set of lithologic, tectonic, and geomorphologic factors. Also in complex topographic zones investigation of changes in the fractal dimension can be a useful and effective instrument for detecting and surveying of the surface anomalies