Xystrakis F, Koutsias N (2013). Differences of fire activity and their underlying factors among vegetation formations in Greece. iForest 6: 132-140.
Abstract: Climate and weather play an important role in shaping fire activity patterns by controlling fuel productivity and fire spread, respectively. Additionally, climate is a key factor controlling primary productivity while different climate zones are expected to support different vegetation formations, that on their turn, include different fuel types. The use, therefore, of an underlying phytogeographical framework would provide more comprehensive outputs in exploring fire activity patterns at national scales, instead of using administrative units that could include various types of non-ecological divisions. Within this concept, we examined the recent fire activity of the main forest formations occurring in Greece and we explored their relationships with various weather parameters and indexes. Correlations of number of fires with weather variables, especially among the vegetation formations that summarize most of the human influence were lax, yet burned area showed significant correlations mainly with fire season precipitation totals. The precipitation related variables generally outperformed the temperature-related variables in correlating with both number of fires and burned area. Climatic indexes, which embody the combined effects of precipitation and air temperature, reflected improved correlations, but not to the extent that could allow for the promotion of their use in further modeling.
Fire is an important disturbance factor in many terrestrial ecosystems that affects biodiversity and ecosystem functions (, , ) and structures plant communities (, ). The effects of fire may be severe, depending on fire frequency and intensity, the regeneration dynamics of vegetation and other local site characteristics like topography (, ). How important are fires in shaping vegetation patterns is proved by the increase in fire frequency, area burned and fire severity and extension of fire season observed in many areas of the world (, , ). This is important especially for the Mediterranean region, which is considered as one of the most fire-prone regions of the world with a remarkably high burned area/ fire ignition index ().
Climate and weather play an important role in shaping fire activity patterns, the first by controlling fuel production and the second by affecting fire spread (). Among the weather variables that were found to correlate with number of fires and burned area surface, the amount of summer or fire-season rainfall (, ), annual precipitation () and several parameters derived from air temperature () are the most important. Aggregation of the above parameters over time (e.g., totals for precipitation or averages for air temperature) often helps in noise reduction when different seasons are considered. For example, it has been shown that total precipitation over spring may have a positive correlation with burned area, while fire-season precipitation may show a negative correlation (). Moreover, it has been observed that weather indexes combining the effect of simple weather parameters are, conditionally, better correlated with fire activity than simple weather parameters (, , ).
It was also argued that the aggregation of data on fire activity over various fuel types and climates could mask the relationship between climate, fuel type and fire activity (). Differences in forest types among distinct vegetation formations are also likely to stem from differences in climatic conditions or fire activities (, ). Since fire activity, at large scale, depends on resource gradients of e.g., primary productivity (), different climatic and vegetation zones are expected to reveal differences in fire activity (). Similarly, different potential natural vegetation units at national scale may reflect different fire-related characteristics (). Vegetation types or formations directly depend on abiotic factors controlling primary productivity, as for example climate, topography and soil characteristics (, ). Thus, classification of the study area examined on the basis of vegetation types may help exploring fire activity in a more comprehensive way instead of considering administrative units, including various types of non-ecological divisions ().
Understanding and quantifying the components that shape fire regimes and influence fire activity may provide the basis for predicting possible future changes in fire activity (), especially when an increase in fire risk is expected, at least in the Mediterranean region (). Weather parameters linked to various components of fire regime (especially number of recorded fires and burned area) have been studied worldwide, as for instance in north America (, , ), in western Mediterranean basin (, , , , ). In Greece, except for some studies at landscape () or regional () scale, only Dimitrakopoulos et al. () and Koutsias et al. () have explored changes in fire regime and their correlation with the Standardized Precipitation Index (SPI) and various weather parameters, respectively.
In this study, we explored trends and differences in fire activity (fire number, area burned and fire ignition index) for the period 1985-2004 among different vegetation formations occurring in Greece (). Fire ignition index (FII) is defined as the ratio between area burned and number of fires (). Correlations between weather variables/indexes and fire activity were estimated taking also into consideration possible lagged effects. The results may provide the basis for the identification of important weather variables that correlate with fire activity as a basis to build up future prediction models.
Meteorological data from 57 weather stations, scattered throughout Greece (Fig. 1), for the period between 1960 and 2004 were obtained from the Hellenic National Meteorological Service (HNMS). The meteorological observations from the weather stations included monthly values of mean, mean maximum and mean minimum air temperature (°C) and monthly precipitation totals (mm). Furthermore, monthly precipitation totals were aggregated in: fire-season (July-September); annual; spring (March-May); and winter-spring (January-May) totals. Annual and fire-season mean air temperature were also calculated. These variables were additionally used to estimate several other indexes that could potentially describe climatic or weather features combining temperature and precipitation patterns. These indexes included: (i) Giacobbes's indice d'arido- humidité (G); (ii) Emberger's indice pluviothérmique (); (iii) the area of the Walter & Lieth climatic diagram for the period when precipitation curve undercuts the respective curve of air temperature (arid season - ); (iv) the annual and fire-season moisture deficit, estimated by applying the monthly soil water balance model as described by Xu (), using the most suitable potential evapotranspiration equations for each region as described in Kostinakis et al. () and Xystrakis & Matzarakis ().
Fig. 1 - Potential distribution of forest formations and locations of the weather stations (black triangles) in Greece. Coordinate reference system: GGRS'87 (epsg id:2100).
Fire data were represented by the annual number of fires and the total annual burned area observations for the period 1985-2004, and were obtained from the Greek Forest Service. Data included the geographic coordinates of the ignition points, therefore, could describe fire events within a spatially explicit framework.
Based on their geographic coordinates, fire data were assigned to different vegetation formations occurring in Greece (Fig. 1 - ). Briefly, these formations are described in terms of their dominant/ abundant woody taxa and their distribution over Greece (). A rough correspondence of some of these formations to the distribution of the major types of Mediterranean ecosystem is reported in Arianoutsou (). The altitudinal distribution of the aforementioned vegetation formations on the basis of 10 000 samples randomly distributed over the area covered by each formation is presented in Fig. 2. The total number of points within each formation was proportional its total area. Elevation values were obtained by the 7.5 arc-seconds Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) freely available from the United States Geological Survey (USGS - ⇒ http://eros.usgs⇒ . gov/).
Fig. 2 - Elevation distribution of vegetation formations in Greece. (Ol.Cer.): Oleo-Ceratonion; (Qu.il.): Quercion ilicis; (Os.Car.): Ostryo-Carpinion; (thermophilous): thermophilous deciduous oaks formation; (oro-Med): oro-Mediterranean formation.
The Oleo-Ceratonion is the warmest-driest subtype (eu-Mediterranean) of the formation of evergreen-broadleaved shrubs. This subtype includes mainly shrublands dominated by Pistacia lentiscus, Olea europaea var. sylvestris and Ceratonia siliqua or forests made up with Pinus halepensis and P. brutia with shrublayer of the aforementioned broadleaved-evergreen taxa. Phrygana (garrigues) are also included within this formation. It is mainly distributed over the coastal areas of the southern part of the country, including coastal Crete, south Attica, south Peloponnese and Aegean islands. It occurs at low altitudes, up to 700 m. a.s.l. (Fig. 2).
The Quercion ilicis is the "humid" subtype of the formation of evergreen broadleaved shrubs. This includes mainly Quercus ilex, Arbutus unedo shrublands or forests of P. halepensis and P. brutia with shrublayer of the aforementioned evergreen broadleaved taxa. It is distributed in the lowland part of northern Greece or inland Crete, N. Attica, eastern Peloponnese and widely in the humid western Greece. It is generally distributed at higher elevation than Oleo-Ceratonion (Fig. 2).
The Ostryo-Carpinion is the cooler subtype (sub-Mediterranean) of the formation of evergreen broadleaved shrubs. This includes mainly Quercus coccifera, Phillyrea latifolia, Acer sempervirens dominated shrubs or forests of P. halepensis and P. brutia with shrublayer of the aforementioned evergreen broadleaved taxa. It is argued that Oleo-Ceratonion is a degraded stage of the formation of thermophilous deciduous oaks (). It is distributed in the inland-lowland part of central and northern Greece, including Thessalia and Macedonia planes and a considerably large part of the central Peloponnese. Its elevation distribution is very similar to that of Quercion ilicis (Fig. 2), but Ostryo-Carpinion occurs at the lower altitudes in northern Greece, thus it is climatically differentiated, as also shown in Fig. 3.
Fig. 3 - Violin plots of mean annual air temperature (left), total annual precipitation (center) and fire-season precipitation (right) of the four vegetation formations considered in this study. Different letters indicate significant differences between vegetation types according to the paired Mann-Whitney test. For vegetation types' labels, see Fig. 2.
The thermophilous deciduous oaks formation includes most of the deciduous broadleaved forests (Quercus frainetto, Q. Cerris, Q. petraea, Q. robur subsp. robur and Castanea sativa), occurring mainly in the mountainous zone between 300 and 800 m a.s.l. (Fig. 2).
Finally, the oro-Mediterranean formation includes the high-altitude forests composed by various conifers including Pinus heldreichii, P. sylvestris, P. nigra s.l., Abies x borisii-regis in northern Greece and Abies cephalonica and Cupressus sempervirens forests in southern Greece. It is apparent that this formation includes various ecological types over a north-to-south gradient in Greece. It includes ecosystems not subjected to frequent fires, though indications of an alteration of their fire regime, due to the combined effects of climate change and fuel accumulation, have been reported (). Unfortunately, in the present study only two meteorological stations were avai-lable for this formation, and therefore it was excluded from further analysis.
A data set consisting of the annual number of fires and the annual total burned area for the period 1985-2004 for each vegetation formation was achieved. Similarly, weather data were aggregated based on vegetation formations, and seasonal or annual averages for the period 1985-2004 for each vegetation type were obtained.
Basic descriptive statistics, including the differences among vegetation formations in the distribution of selected variables (fire- and weather-related variables), were obtained from violin plots (), drawn using the VIOPLOT package of the software R (). Paired Mann-Whitney test was applied to test for differences in means of selected variables among the different vegetation formations.
Time series of fire activity in all vegetation formations were plotted alongside. To test for monotonic trends in time series of number of fires, area burned and FII, the non-parametric Mann Kendall trend test () was applied in the time series of all vegetation formations.
Correlation analysis was performed to describe the relationships between the annual number of fires and burned area in each vegetation type and the various weather variables aggregated on annual to seasonal basis. Correlation coefficients were also calculated between the fire characteristics of the year t and weather variables for the previous three years [lag(t-1) up to lag(t-3)], since the correlation between lagged precipitation totals and burned area was often observed (, ). To assess the significance of lagged correlations, time series of both weather and fire variables, often affected by autocorrelation, were "pre-whitened" by fitting them to a 5th order autoregressive model (AR5), thus reducing them to "white noise" (). Highly skewed variables, as e.g., fire-season precipitation, were log transformed and variables showing significant trends were detrended by means of ordinary least square regression against time (). All analyses were performed using the R statistical package ().
Significant differences in climatic features and fire characteristics among the four vegetation types were observed. Ostryo-Carpinion was characterized by the lowest annual number of fires, followed by oro-Mediterranean conifers. Quercion ilicis clearly showed the greatest number of fires; the thermophilous deciduous oaks formation also showed a high number of fires, though their median was lower than the median obtained for Quercion ilicis (Fig. 4). As for burned area, no significant differences among vegetation types were observed, except for the oro-Mediterranean conifers formation which clearly had the lowest burned area, as also shown from the paired-Mann-Whitney test (Fig. 4). Finally, patterns of fire ignition index (FII) reported in Fig. 4 indicate the presence of two main groups in Greece: (i) the Oleo-Ceratonion, Quercion ilicis and Ostryo-Carpinion formations, characterized by high values of FII; and (ii) the thermophilous deciduous oaks and oro-Mediterranean conifers, with considerably lower values of FII. To summarize, although a large number of fires was observed for the thermophilous oaks formation, their size was rather small, resulting in low values of FII; in contrast, Ostryo-carpinion was characterized by a small number of remarkably large fires that results in high values of FII.
Fig. 4 - Violin plots of number of fires (left), total burned area (center) and fire ignition index (right) of the four vegetation formations considered in this study. Different letters indicate significant differences between vegetation types according to the paired Mann-Whitney test. For vegetation types' labels, see Fig. 2.
Differences among the vegetation formations were also observed in annual mean air temperature patterns. A clear gradient form Oleo-Ceratonion (annual mean air temperature: ~ 18 °C) towards the thermophilous oaks (temperatures: ~ 14 °C) was apparent (Fig. 3). Differences in total annual rainfall were not so pronounced, though Quercion ilicis was characterized by conspicuous high values of total annual precipitation, in relation to the high annual rainfall of several weather stations from western Greece. Analogously, a clear gradient of fire-season precipitation was also obvious, with the lowest values observed in Oleo-Ceratonion and the highest in thermophilous oaks class. To summarize, Oleo-Ceratonion is characterized by high temperatures and low fire-season precipitation values, i.e., favorable conditions for fire initiation and spread; in contrast, thermophilous oaks are characterized by lower air temperature values and high values of fire-season precipitation hampering fire ignition and spread.
Correlation analysis aimed at the detection of weather variables related to fire characteristics in all vegetation formations. Weather variables significantly correlated with fire characteristics (number of fires and burned area) are presented in Tab. 1.
Tab. 1 - Spearman's lag 0 correlation coefficients of number of fires and burned area observed in the vegetation formations with selected weather variables. (N): number of fires; (A): area burned; (*): significant correlation with p < 0.05; (**): significant correlation with p < 0.01; (Tmax): maximum air temperature; (prec): precipitation; (W & L): the area of the Walter & Lieth climatic diagram for the period when precipitation curve undercuts the respective curve of air temperature.
Fire-season precipitation, fire-season Giaccobes' index and fire-season moisture deficit showed significant correlations with burned area in all the vegetation types analyzed (Tab. 1). Moreover, in all vegetation formations the burned area was significantly correlated with at least one weather variable or index, while the same did not hold for number of fires. Moreover, correlations coefficients between fire- and weather-related variables were greater in the "thermophilous deciduous oaks" formation. Interestingly, weather variables aggregated over the fire season largely exceeded all other variables as for correlation coefficients. This suggests a straightforward effect of fire-season weather on fire occurrences. Finally, rainfall-related variables generally showed larger correlation coefficients than temperature-related variables.
Time series of recent fire history for the Oleo-Ceratonion formation are presented in Fig. 5. Mann-Kendall trend test revealed a positive - though not significant (p=0.09) - trend for the number of fires, but significant negative trends for the burned area (p=0.04) and FII (p=0.01).
Fig. 5 - Time series of recent fire history (number of fires - left, burned area - center, and fire ignition index - right) for the vegetation formations considered in this study.
The number of fires did not correlate with any of the examined weather variables; in contrast, burned area was significantly negatively correlated with lag 0 fire-season precipitation, fire-season Giaccobe's index and fire season moisture deficit (Tab. 1). Correlation analysis of lagged variables revealed a general trend of positive though not significant cross-correlations between burned area and (i) winter-spring and (ii) fire-season precipitation (Fig. 6). Furthermore, there is a series of positive cross-correlations between area burned and total annual precipitation lag 1 to lag 4.
Fig. 6 - Lagged cross-correlations between burned area and annual, fire-season and spring precipitation for the vegetation formations studied. (CCF): cross-correlation function.
Man-Kendall trend test revealed that all variables show significant trends during the period 1985-2004 (Fig. 5). The trend was positive for the parameter number of fires (p=0.027), and negative for area burned (p=0.029) and FII (p=0.004). The highest number of fires was recorded in the year 1992, while the largest burned area was observed in 1988. Similarly to Oleo-Ceratonion, the number of fires did not show any significant lag 0 correlations with weather variables (Tab. 1). In contrast, burned area was significantly correlated with most of the variables considered and had the highest (in absolute values) correlation coefficient with the arid-period area of the Walter and Lieth diagram. In Quercion ilicis, similarly to Oleo-Ceratonion, a significant correlation between area burned and lag 0 fire-season precipitation was observed, and a positive lag 3 cross correlation with winter-spring precipitation (Fig. 6). It is also interesting to observe the lag 1 positive, though not significant, correlation with spring precipitation (Fig. 6).
The recent fire history of Ostryo-Carpinion is presented in Fig. 5. The Mann-Kendall trend test revealed that all variables have decreasing trends during the period 1985-2004, though the variable number of fires was not significant (p=0.516). In contrast, trends for burned area (p=0.047) and FII (p=0.040) were both significant. The highest number of fires was observed during the year 1994, while the highest burned area was recorded in the year 2000.
In contrast with vegetation formations previously examined, the number of fires of Ostryo-Carpinion showed significant correlations with various weather variables (Tab. 1). The highest correlation coefficient was observed with fire-season Giaccobe's index. Similarly, burned area was also correlated with several weather-related variables. Among temperature-related variables, a significant positive correlation with fire-season absolute maximum air temperature was observed. Among precipitation-related variables, fire-season Giaccobe's index had the highest significant negative correlation.
Correlation analysis of lagged variables revealed significant correlation coefficients between: (i) burned area and lag 0 fire-season precipitation; (ii) lag 0 cross-correlation and annual precipitation (Fig. 6). Finally, a significant positive correlation between burned area and lag 2 total annual precipitation was detected, as well as a series of positive correlations (though not significant) with fire-season precipitation up to lag 3.
Mann-Kendall trend test revealed that both the positive trend for number of fires and the negative trend for burned area (Fig. 5) were not significant (p=0.19 and p=0.455, respectively); in contrast, the observed trend for FII was significant (p=0.018) over the period 1985-2004. The highest number of fires was observed during the year 2001, while the largest burned area was recorded in 1988. Similarly to Ostryo-Carpinion, number of fires was significantly correlated with a number of air temperature- and precipitation-related variables: the highest correlation value was observed for the variable fire-season moisture deficit (Tab. 1). The same variable was also found to have the highest correlation coefficient (in absolute values) with the burned area.
In this forest formation, the lag 0 correlations with all examined weather variables were significantly negative (Fig. 6). There are also peaks of high, positive cross-correlations (though not significant) between burned area and lag 2 annual precipitation, and a peak of high, negative (though not significant) cross correlation with lag 1 annual precipitation.
Based on the results obtained in this study, the number of fires in recent years showed an increasing trend for all the forest formations analyzed, with the exception of Ostryo-Carpinion. In contrast, both burned area and fire ignition index (FII) consistently show a decreasing trend over the period 1985-2004. Therefore, a general picture of more forest fires affecting smaller areas can be drawn, suggesting an improvement in the effectiveness of fire suppression activities. Nevertheless, a large variability was observed in the above trends: years with extreme values of both number of fires and burned area are quite recurrent, and could have a pronounced effect in the trend analysis. In Greece, observations over the period 1961-1997 showed increasing trends for fire and weather characteristics (). Similar increasing trends have been detected in other Mediterranean regions over the last decades (, ). Furthermore, the number of fires is distinctly increasing in some regions over the last two decades, while the same does not hold for the extension of burned areas (, ). However, it has to be pointed out that large periods of observations are required for the detection of significant trends over time, in order to ensure that the observed changes are not due to interannual weather variability.
Differences in fire activity (number of fires, burned area and FII) recorded among different vegetation formations feed the ongoing debate concerning the relative importance of fuel and weather in fire occurrence, especially for large fires (). The Oleo-Ceratonion formation, that mostly includes fuel-limited ecosystems, had significantly higher values of forest ignition index in comparison with the thermophilous deciduous oaks and the oro-Mediterranean conifers formations, that mostly include climate-limited ecosystems (Fig. 4). Similar considerations are supported by the output of the correlation analysis carried out (Tab. 1). The correlation between the number of fires and weather variables for the Oleo-Ceratonion and Quercion ilicis vegetation types were systematically weaker than that of Ostryo-Carpinion and thermophilous deciduous oaks. The stronger association of number of fires with the latter formations suggests that their fire history may be rather climate-controlled. Similar considerations can be argued on the basis of the analysis of the correlation coefficients between burned area and weather variables, although the differences are not so pronounced.
The lack of a direct influence of weather variables on fire ignition (number of fires) in the vegetation types Oleo-Ceratonion and Quercion ilicis also allow to hypothesize the influence of exogenous drivers in fire initiation. These formations are common in the driest part of the country and include more fire resistant (or resilient) vegetation types than the other formations. Similarly to our results, Carvalho et al. () failed to detect significant regressions between fire characteristics and weather variables in a Portuguese districts characterized by fire resistant vegetation. It is also worth to notice that Oleo-Ceratonion and Quercion ilicis vegetation types include the most human-impacted ecosystems in Greece (e.g., urbanized areas and cultivated lands). Intense human impact is traditionally related with fire ignition () and could alter the relationships between fire regimes and potential natural vegetation (). Indeed, arsons and burn of dry biomass in cultivated land in Greece account for the large majority of fires with known ignition cause (about 49% of total number of fires over the period 1982-2006 - ). In these areas, meteorological conditions are not the main driver of ignition for the majority of recorded fires, though weather conditions still affect the fire spread (burned area). Extreme weather conditions may counterbalance fuel limitations for fire spread (). This is in accordance with the findings by Dimitrakopoulos et al. (), who report that any fire can become large (>1000 ha) when particular meteorological conditions do occur.
In contrast, the thermophilous deciduous oaks formation suffer lower human impacts, being common at higher altitudes where land abandonment since long time led to fuel accumulation and vegetation thickening (, ). The large amount of fuel accumulated, as a consequence of the optimal climatic conditions for plant growth, may explain the large number of fires observed for this vegetation type. On the other hand, the wet fire season, as inferred by the high fire-season precipitation total (Fig. 4), may limit fire spread. These formations typically have increased fuel load requiring severe drought conditions to become flammable. Pausas & Fernandez-Munoz () argue that fire regime is a counterbalance between fuel availability and weather conditions. Fuel availability in this vegetation type increases as forest recolonization of abandoned agricultural lands comes along, partially explaining the upward trends of fires and the poor correlation observed with weather variables. This also can indirectly explain the differences observed in weather controlling fire ignition and spread in fuel-limited vs. climate-limited vegetation formations in Greece, similarly to Pausas & Fernandez-Munoz (). Nevertheless, in the case of extreme dry fire-season weather, fires in less fire-prone vegetation types () may spread uncontrollably and burn large areas to a comparable extent with other vegetation types including more fire-prone ecosystems (e.g., year 1988 - Fig. 5). A similar weather-controlled fire activity was observed for other vegetation formations characterized by climatically- controlled high fuel production, as the dry pine forests in the southwestern USA () or the mesic Sitka spruce forests in northwestern USA (). Similarly, Mouillot et al. () found that shrubs and grasslands were more probe to re-burning than forest vegetation types in Corsica. It can be argued that the relative importance of fuel in controlling fire spread diminishes in the case of extreme fire weather ().
Among all the examined weather variables, those aggregated over fire season constantly showed higher correlation coefficients with area burned in all vegetation formations. In contrast, variables aggregated over the whole year showed considerably lower correlations. The above pattern may be due to the masking effect of contrasting season conditions in the observed fire activity ().
Maximum air temperature and several moisture-related variables were found to be significantly correlated with area burned in Canadian peatlands (). Similarly, maximum air temperature outperformed precipitation-related variables in an investigation carried out in the Iberian peninsula (), while the mean air temperature only marginally correlated with area burned. Consistently with our findings, mean air temperature was poorly correlated with extreme fire risk in Mediterranean regions (). The above evidence suggests a contradictory effect air temperature-related variables in summarizing the burned area variation. Indeed, in our study temperature-related variables slightly underperformed when compared to the precipitation-related variables.
Negative correlation between lag 0 spring precipitation and area burned is consistent with the results reported by Gedalof et al. (), who observed that an increase in the burned area may be favored when at least two consecutive dry fire seasons do occur, determining fuel desiccation and thus increasing its flammability. Similarly, winter and early spring precipitation, affecting fuel accumulation and increasing soil water content, was not clearly related to the burned area in a study carried out in Portugal, due to the conflicting mechanisms of the aforementioned processes ().
In this study, we found one significant lagged correlation between area burned and annual precipitation for the Ostryo-Carpinion formation and some peaks in lag 2 annual and fire-season precipitation for the Oleo-Ceratonion and the thermophillous deciduous oaks formations. These findings are in accordance with those of Pausas (), Westerling et al. () and Koutsias et al. () who identified some significant effects of lagged weather parameters in the burned area. This "preconditioning" was also clearly observed in grass- and scrubland-dominated ecoprovinces in the western USA ().
In this study drought-related indexes combining the effects of air temperature and rainfall (like the arid period area in the Walter & Lieth diagrams or the Emberger's indice pluviothérmique) did only slightly improve fire-related variable predictions obtained using simple weather variables. The use of simple weather variables is easier to interpret providing straight forward results, and therefore it should be considered as more appropriate for the development of explanatory models of fire activity.
Some likely important factors may be missing in the results presented here. In human-impacted ecosystems, the relationships between fire and vegetation are expected to be less pronounced (). Landscape structure (), proximity to settlements (), or several other socio-economic factors (, ) are expected to influence fire ignition and spread. Such parameters have to be included in further analysis in order to explain larger part of the total variation.
The limited number of available weather stations also restricts the adoption of a more detailed classification scheme that would additionally include structural characteristics of vegetation and land-use/land cover (LULC) classes. For example, in each of the formations considered in this study, a variety of LULC types exists, including grasslands, scrublands and forests. Each of these LULC units is certainly expected to reflect different fire regimes, due to differences in fuel availability and structural characteristics (). Such a classification (LULC classes within forest formations) would result in complex spatial patterns requiring a denser meteorological station network in order to adequately represent the climatic and inter-annual weather variations of each class.
Efforts to include additional explanatory variables have been put forward and further analyses are expected to address the description of fire characteristics within a framework that will include vegetation, socioeconomic and landscape variability. Nevertheless, the importance of weather can not be neglected since it has been proven to account for a large part of variation in both number of fires and burned area in the Mediterranean basin () and Greece (). Studies discussing the differences in fire regime among different ecoregions are considerably abundant in north America (, ), while are rather scarce in Europe (). Our approach therefore, provides the basis for the better understanding of the relation between forest fires and weather and, in general, between fires and site productivity, since its relation to climate, topography and soil conditions (, ).
This work has been funded by the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement nº 243888 (FUME project). We acknowledge the Hellenic National Meteorological Service and the Hellenic Forest Service for providing meteorological and forest fire data, respectively. The authors would like to thank M. Gouvas for his valuable insights with respect to the forest formations. We are also grateful to two anonymous reviewers for their valuable comments on an earlier version of the manuscript.
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Koutsias N, Arianoutsou M, Kallimanis AS, Mallinis G, Halley JM, Dimopoulos P (2012). Where did the fires burn in Peloponnisos, Greece the summer of 2007? Evidence for a synergy of fuel and weather. Agricultural and Forest Meteorology 156 (1): 41-53.
Koutsias N, Karteris M (2003). Classification analyses of vegetation for delineating forest fire fuel complexes in a Mediterranean test site using satellite remote sensing and GIS. International Journal of Remote Sensing 24 (15): 3093-3104.
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Littell JS, McKenzie D, Peterson DL, Westerling AL (2009). Climate and wildfire area burned in western US ecoprovinces, 1916-2003. Ecological Applications 19 (4): 1003-1021.
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Vasilakos C, Kalabokidis K, Hatzopoulos J, Matsinos I (2009). Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Natural Hazards 50 (1): 125-143.
Vázquez A, Moreno JM (1993). Sensitivity of fire occurrence to meteorological variables in mediterranean and atlantic areas of Spain. Landscape and Urban Planning 24 (1-4): 129-142.
Vázquez A, Pérez B, Fernández-González F, Moreno JM (2002). Recent fire regime characteristics and potential natural vegetation relationships in Spain. Journal of Vegetation Science 13 (5): 663-676.
Vega-García C, Chuvieco E (2006). Applying local measures of spatial heterogeneity to Landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes. Landscape Ecology 21 (4): 595-605.
Viedma O (2008). The influence of topography and fire in controlling landscape composition and structure in Sierra de Gredos (Central Spain). Landscape Ecology 23 (6): 657-672.
Viedma O, Angeler DG, Moreno JM (2009). Landscape structural features control fire size in a Mediterranean forested area of central Spain. International Journal of Wildland Fire 18 (5): 575-583.
Viegas DX, Viegas MT (1994). A relationship between rainfall and burned area for Portugal. International Journal of Wildland Fire 4 (1): 11-16.
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Xystrakis F, Matzarakis A (2011). Evaluation of 13 empirical reference potential evapotranspiration equations on the island of Crete in southern Greece. Journal of Irrigation and Drainage Engineering 137 (4): 211-222.
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text:
Q: UIDatePicker with single row In my application i need to integrate UIDatePicker view with  single row (only one row needs to display, that's a selection row.  UIDatePicker selection functionality is same like scrolling date up and down. i dont want to change that. ). like this

is it possible in Native UIDatePicker? if yes give me some idea. if not suggest any custom Date picker(that should not give any problem while move to app store). Guide me to fix this issue.

A: You can do this by simply connecting to your designer for few images like
1. Background
2. Separator

All you have to do is create interface design like this and parse current date in required format with NSDateFormatter and put it as required.
Hope this helps.

A: Don't Do That.
UIDatePicker displays a UIPickerView and Unfortunately, size of UIPickerView cannot be changed.
My answer : Even If you found any Hack ,pleas don't use that code, As It will go against Human Interface Guidelines (by making Touch Targets too small).

redpajama_set_name: RedPajamaStackExchange


text:
Published on Scottish Local History Forum (https://slhf.org)
Home > Torrance Heritage and History Group
Torrance Heritage and History Group
The aim of Torrance Heritage and History Group is to develop an interest in local history for all people with an interest in Torrance. The Group works to improve the promotion and preservation of historical documents, artefacts and oral histories relating to Torrance.
The Group meets on the last Monday of the month from September to May/June at the Caldwell Halls, 16 Campsie Road, Torrance G64 4BN at 7.30 pm
Caldwell Halls, 16 Campsie Road, Torrance, G64 4BN
programme_2015-16.doc [1]
Alison Graham
Maureen Copeland
Scottish Local History Forum, Box 103, 12 South Bridge, Edinburgh EH1 1DD | Scottish Charity No. SCO 15850 Founded 1983 | Scottish Local History Forum © 2013
Source URL: https://slhf.org/member-organisation/torrance-heritage-and-history-group
[1] https://slhf.org/sites/default/files/documents/programme_2015-16.doc
[2] mailto:torranceheritageandhistory@gmail.com

redpajama_set_name: RedPajamaCommonCrawl


Find a way to divide your email list according to what people love to read. Some people love general articles while others love to read niche-specific content.
efore people open your email, they will establish if they can trust the source and then they will look at the subject line. Nothing happens at all unless your cold email is opened and hence the importance of the subject line.
Below we have highlighted a few tricks you can use to creating an amazing subject line.
Subject lines that arouse curiosity e.g. "7 Off-the-Beaten-Path Beach Destinations in the Caribbean".
Subject lines that give the reader direct benefit e.g. "Three Secrets about Saving Money That Your Travel Agent Does Not Want You to Know". You can also use "how to's" e.g. "How to Visit Antarctica on a Budget".
Always send the email with a subject line that captures the prevailing season. Outdated subject lines are immediately marked as spam or deleted.
Try using the name of an easily recognized influencer on your subject line.
This makes the readers curious about what is in the email. "…" Usually means read on.
Example:"Check Out Our Newest Cabins in the Royal Caribbean Cruise Ship". Everybody is always looking for new holiday ideas.
The universal feeling of belonging is stronger among travelers than in other niches.
Example: "You Are Not Alone: We Will Have Your Back during the Entire Barrier Reef Exploration".
It works very well when you are communicating something informal. It disarms your reader because it sounds like an email from a friend.
Statistics show that people love watching videos, not reading through volumes of text. Make sure your video does not redirect your viewers to other pages.
If you have been sending newsletters to your subscribers, then it means there is a relationship there. Just make sure you do not over promise and under-deliver.
This phrase is a curiosity invoker. People will want to know what "this" thing is.
People are more likely to open an email that has a subject line with a name of somebody they are familiar with. They can immediately establish that you are trustworthy.
If you know a client personally, you can Google their latest achievement and include it in the subject line.
Make your emails personal. People know you are in business and have to sell but show your human touch also.
Always give value to your subscribers. Don't fill up their inbox with useless emails.
Use active voice. Write the email like you are writing it to a personal friend. Since all your subscribers share the common interest of travelling, write the email like you are writing to one person.
Don't try too hard to spark your readers' attention. You come off as somebody that's not genuine.
Segment your list. Find a way to divide your email list according to what people love to read. Some people love general articles while others love to read niche-specific content. In travel, you can have segments such as Safari, Cruising, Winter Vacations, Beach, Weddings, Business Travel, and Budget Travel etc.
Which are those small things that you ignore but are responsible for losing your Travel Customers?


text:
#ifndef DSP_DEFS_H_
#define DSP_DEFS_H_
#include "dspdids.h"
/*---------------------------------------------------------------------------*/
#define dsp_download_reserve_space(fp, length)
/*****************************************************************************/
/*
 * OS file access abstraction layer
 *
 * I/O functions returns -1 on error, 0 on EOF
 */
struct _OsFileHandle_;
typedef long (*OsFileIo)(struct _OsFileHandle_ *handle,
			 void *buffer,
			 long size);
typedef long (*OsFileSeek)(struct _OsFileHandle_ *handle,
			   long position,
			   int mode);
typedef long (*OsCardLoad)(struct _OsFileHandle_    *handle,
			   long length,
			   void **addr);
typedef struct _OsFileHandle_
{ void       *sysFileDesc;
	unsigned long sysFileSize;
	OsFileIo      sysFileRead;
	OsFileSeek    sysFileSeek;
	void       *sysLoadDesc;
	OsCardLoad    sysCardLoad;
} OsFileHandle;
extern OsFileHandle *OsOpenFile(char *path_name);
extern void          OsCloseFile(OsFileHandle *fp);
/*****************************************************************************/
#define DSP_TELINDUS_FILE "dspdload.bin"
/* special DSP file for BRI cards for Qsig and CornetN because of missing memory */
#define DSP_QSIG_TELINDUS_FILE "dspdqsig.bin"
#define DSP_MDM_TELINDUS_FILE "dspdvmdm.bin"
#define DSP_FAX_TELINDUS_FILE "dspdvfax.bin"
#define DSP_DIRECTORY_ENTRIES 64
#define DSP_MEMORY_TYPE_EXTERNAL_DM         0
#define DSP_MEMORY_TYPE_EXTERNAL_PM         1
#define DSP_MEMORY_TYPE_INTERNAL_DM         2
#define DSP_MEMORY_TYPE_INTERNAL_PM         3
#define DSP_DOWNLOAD_FLAG_BOOTABLE          0x0001
#define DSP_DOWNLOAD_FLAG_2181              0x0002
#define DSP_DOWNLOAD_FLAG_TIMECRITICAL      0x0004
#define DSP_DOWNLOAD_FLAG_COMPAND           0x0008
#define DSP_MEMORY_BLOCK_COUNT              16
#define DSP_SEGMENT_PM_FLAG                 0x0001
#define DSP_SEGMENT_SHARED_FLAG             0x0002
#define DSP_SEGMENT_EXTERNAL_DM             DSP_MEMORY_TYPE_EXTERNAL_DM
#define DSP_SEGMENT_EXTERNAL_PM             DSP_MEMORY_TYPE_EXTERNAL_PM
#define DSP_SEGMENT_INTERNAL_DM             DSP_MEMORY_TYPE_INTERNAL_DM
#define DSP_SEGMENT_INTERNAL_PM             DSP_MEMORY_TYPE_INTERNAL_PM
#define DSP_SEGMENT_FIRST_RELOCATABLE       4
#define DSP_DATA_BLOCK_PM_FLAG              0x0001
#define DSP_DATA_BLOCK_DWORD_FLAG           0x0002
#define DSP_DATA_BLOCK_RESOLVE_FLAG         0x0004
#define DSP_RELOC_NONE                      0x00
#define DSP_RELOC_SEGMENT_MASK              0x3f
#define DSP_RELOC_TYPE_MASK                 0xc0
#define DSP_RELOC_TYPE_0                    0x00  /* relocation of address in DM word / high part of PM word */
#define DSP_RELOC_TYPE_1                    0x40  /* relocation of address in low part of PM data word */
#define DSP_RELOC_TYPE_2                    0x80  /* relocation of address in standard command */
#define DSP_RELOC_TYPE_3                    0xc0  /* relocation of address in call/jump on flag in */
#define DSP_COMBIFILE_FORMAT_IDENTIFICATION_SIZE 48
#define DSP_COMBIFILE_FORMAT_VERSION_BCD    0x0100
#define DSP_FILE_FORMAT_IDENTIFICATION_SIZE 48
#define DSP_FILE_FORMAT_VERSION_BCD         0x0100
typedef struct tag_dsp_combifile_header
{
	char                  format_identification[DSP_COMBIFILE_FORMAT_IDENTIFICATION_SIZE];
	word                  format_version_bcd;
	word                  header_size;
	word                  combifile_description_size;
	word                  directory_entries;
	word                  directory_size;
	word                  download_count;
	word                  usage_mask_size;
} t_dsp_combifile_header;
typedef struct tag_dsp_combifile_directory_entry
{
	word                  card_type_number;
	word                  file_set_number;
} t_dsp_combifile_directory_entry;
typedef struct tag_dsp_file_header
{
	char                  format_identification[DSP_FILE_FORMAT_IDENTIFICATION_SIZE];
	word                  format_version_bcd;
	word                  download_id;
	word                  download_flags;
	word                  required_processing_power;
	word                  interface_channel_count;
	word                  header_size;
	word                  download_description_size;
	word                  memory_block_table_size;
	word                  memory_block_count;
	word                  segment_table_size;
	word                  segment_count;
	word                  symbol_table_size;
	word                  symbol_count;
	word                  total_data_size_dm;
	word                  data_block_count_dm;
	word                  total_data_size_pm;
	word                  data_block_count_pm;
} t_dsp_file_header;
typedef struct tag_dsp_memory_block_desc
{
	word                  alias_memory_block;
	word                  memory_type;
	word                  address;
	word                  size;             /* DSP words */
} t_dsp_memory_block_desc;
typedef struct tag_dsp_segment_desc
{
	word                  memory_block;
	word                  attributes;
	word                  base;
	word                  size;
	word                  alignment;        /* ==0 -> no other legal start address than base */
} t_dsp_segment_desc;
typedef struct tag_dsp_symbol_desc
{
	word                  symbol_id;
	word                  segment;
	word                  offset;
	word                  size;             /* DSP words */
} t_dsp_symbol_desc;
typedef struct tag_dsp_data_block_header
{
	word                  attributes;
	word                  segment;
	word                  offset;
	word                  size;             /* DSP words */
} t_dsp_data_block_header;
typedef struct tag_dsp_download_desc
{
	word                  download_id;
	word                  download_flags;
	word                  required_processing_power;
	word                  interface_channel_count;
	word                  excess_header_size;
	word                  memory_block_count;
	word                  segment_count;
	word                  symbol_count;
	word                  data_block_count_dm;
	word                  data_block_count_pm;
	byte *p_excess_header_data;
	char *p_download_description;
	t_dsp_memory_block_desc *p_memory_block_table;
	t_dsp_segment_desc *p_segment_table;
	t_dsp_symbol_desc *p_symbol_table;
	word *p_data_blocks_dm;
	word *p_data_blocks_pm;
} t_dsp_desc;
typedef struct tag_dsp_portable_download_desc /* be sure to keep native alignment for MAESTRA's */
{
	word                  download_id;
	word                  download_flags;
	word                  required_processing_power;
	word                  interface_channel_count;
	word                  excess_header_size;
	word                  memory_block_count;
	word                  segment_count;
	word                  symbol_count;
	word                  data_block_count_dm;
	word                  data_block_count_pm;
	dword                 p_excess_header_data;
	dword                 p_download_description;
	dword                 p_memory_block_table;
	dword                 p_segment_table;
	dword                 p_symbol_table;
	dword                 p_data_blocks_dm;
	dword                 p_data_blocks_pm;
} t_dsp_portable_desc;
#define DSP_DOWNLOAD_INDEX_KERNEL               0
#define DSP30TX_DOWNLOAD_INDEX_KERNEL           1
#define DSP30RX_DOWNLOAD_INDEX_KERNEL           2
#define DSP_MAX_DOWNLOAD_COUNT                  64
#define DSP_DOWNLOAD_MAX_SEGMENTS         16
#define DSP_UDATA_REQUEST_RECONFIGURE     0
/*
  parameters:
  <word> reconfigure delay (in 8kHz samples)
  <word> reconfigure code
  <byte> reconfigure hdlc preamble flags
*/
#define DSP_RECONFIGURE_TX_FLAG           0x8000
#define DSP_RECONFIGURE_SHORT_TRAIN_FLAG  0x4000
#define DSP_RECONFIGURE_ECHO_PROTECT_FLAG 0x2000
#define DSP_RECONFIGURE_HDLC_FLAG         0x1000
#define DSP_RECONFIGURE_SYNC_FLAG         0x0800
#define DSP_RECONFIGURE_PROTOCOL_MASK     0x00ff
#define DSP_RECONFIGURE_IDLE              0
#define DSP_RECONFIGURE_V25               1
#define DSP_RECONFIGURE_V21_CH2           2
#define DSP_RECONFIGURE_V27_2400          3
#define DSP_RECONFIGURE_V27_4800          4
#define DSP_RECONFIGURE_V29_7200          5
#define DSP_RECONFIGURE_V29_9600          6
#define DSP_RECONFIGURE_V33_12000         7
#define DSP_RECONFIGURE_V33_14400         8
#define DSP_RECONFIGURE_V17_7200          9
#define DSP_RECONFIGURE_V17_9600          10
#define DSP_RECONFIGURE_V17_12000         11
#define DSP_RECONFIGURE_V17_14400         12
/*
  data indications if transparent framer
  <byte> data 0
  <byte> data 1
  ...
  data indications if HDLC framer
  <byte> data 0
  <byte> data 1
  ...
  <byte> CRC 0
  <byte> CRC 1
  <byte> preamble flags
*/
#define DSP_UDATA_INDICATION_SYNC         0
/*
  returns:
  <word> time of sync (sampled from counter at 8kHz)
*/
#define DSP_UDATA_INDICATION_DCD_OFF      1
/*
  returns:
  <word> time of DCD off (sampled from counter at 8kHz)
*/
#define DSP_UDATA_INDICATION_DCD_ON       2
/*
  returns:
  <word> time of DCD on (sampled from counter at 8kHz)
  <byte> connected norm
  <word> connected options
  <dword> connected speed (bit/s)
*/
#define DSP_UDATA_INDICATION_CTS_OFF      3
/*
  returns:
  <word> time of CTS off (sampled from counter at 8kHz)
*/
#define DSP_UDATA_INDICATION_CTS_ON       4
/*
  returns:
  <word> time of CTS on (sampled from counter at 8kHz)
  <byte> connected norm
  <word> connected options
  <dword> connected speed (bit/s)
*/
#define DSP_CONNECTED_NORM_UNSPECIFIED      0
#define DSP_CONNECTED_NORM_V21              1
#define DSP_CONNECTED_NORM_V23              2
#define DSP_CONNECTED_NORM_V22              3
#define DSP_CONNECTED_NORM_V22_BIS          4
#define DSP_CONNECTED_NORM_V32_BIS          5
#define DSP_CONNECTED_NORM_V34              6
#define DSP_CONNECTED_NORM_V8               7
#define DSP_CONNECTED_NORM_BELL_212A        8
#define DSP_CONNECTED_NORM_BELL_103         9
#define DSP_CONNECTED_NORM_V29_LEASED_LINE  10
#define DSP_CONNECTED_NORM_V33_LEASED_LINE  11
#define DSP_CONNECTED_NORM_TFAST            12
#define DSP_CONNECTED_NORM_V21_CH2          13
#define DSP_CONNECTED_NORM_V27_TER          14
#define DSP_CONNECTED_NORM_V29              15
#define DSP_CONNECTED_NORM_V33              16
#define DSP_CONNECTED_NORM_V17              17
#define DSP_CONNECTED_OPTION_TRELLIS        0x0001
/*---------------------------------------------------------------------------*/
extern char *dsp_read_file(OsFileHandle *fp,
			   word card_type_number,
			   word *p_dsp_download_count,
			   t_dsp_desc *p_dsp_download_table,
			   t_dsp_portable_desc *p_dsp_portable_download_table);
/*---------------------------------------------------------------------------*/
#endif /* DSP_DEFS_H_ */

redpajama_set_name: RedPajamaGithub


text:
E-Invoicing/E-Billing
Enterprise Communication
Payment and Card
Casino Tech
Enterprise Startups
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Innovile
Innovile: Maximizing Network Efficiency and Improving Customer Experience
Follow Innovile on :
Erdal Koklu, CEO
Doing more with less is seldom easy. But leading-edge technologies such as artificial intelligence and data analytics help mobile operators do just that. These advanced technologies will continue to make stronger impact in the communications industry with the onset of 5G networks. The operators can now not only serve more users but also meet their growing demands. However, adopting inventive technologies and disruptive approaches presents its own set of challenges. With plentiful investments in technologies, operators find it daunting to manage the overly complex technology orchestration. This is where Innovile find its relevance. Innovile serves as a 'glue' between operators and new technologies. "We empower our clients—from small carriers with a million subscribers to leading carriers—to streamline their business, improve customer experience, boost efficiency, and unlock revenues," says Erdal Koklu, CEO of Innovile.
Innovile's solutions and services are tailored precisely to match a client's existing infrastructure. The company offers a configuration management solution that integrates multi-vendor and multi-technology components into a centralized platform to increase network efficiency and simplify its operations. The solution proactively helps reduce configuration errors across complex networks, which in turn, improves cost savings by 30 percent. Configuration errors in complex networks are a vital cause of customer complaints, and Innovile's solution helps clients enhance their network reliability and customer satisfaction to that end.
We empower our clients—from small carriers with a million subscribers to leading carriers—to streamline their business, improve customer experience, boost efficiency, and unlock revenues
The solution can be rapidly implemented in about 1-2 weeks, and the speedy delivery enables clients to achieve quick business results.
Moreover, as 5G continues to transform the mobile landscape, monitoring the performance of complex networks is becoming a growing concern. Innovile's Performance Management solution offers visibility into end-to-end network performance through simplified reporting mechanism, helping the administrators to tackle the problems in a timely manner and drive operational efficiency.
With exploding network traffic and mounting pressure on mobile operators' business case, self-organizing networks (SON) play a crucial role in optimizing performance. SON minimizes the cost of running a mobile network by eliminating manual configuration of network elements from deployment through dynamic optimization and troubleshooting post implementation. Innovile's SON solution allows clients to automate network management, allowing them to dedicate their resources to other activities and focus on building better customer experiences. With the solution, clients are improving their network capacity by more than 20 percent and increasing the network throughput by 15 percent. There has been a decline in the dropped call rates as well. Innovile has successfully deployed the world's largest SON solution for the leading Russian mobile operator, MegaFon. The client's network currently consists of around 800,000 cells and is projected to reach one million cells in the next five years. MegaFon's network is a complex combination of technologies from many vendors, including more than 20 different operational support systems (OSS). Currently, Innovile's SON is used to standardize operations and optimize the multi-technology, multi-vendor network across Russia, spanning 11 time zones and covering around 77 million subscribers.
Innovile's experience in the network optimization and automation space has allowed it to deliver flexible and modular solutions. Looking toward the future, the company aims to expand its presence in Europe, using its branch office in Barcelona as the base. Apart from Europe, Innovile has plans to enter the U.S. and APAC market by 2020; it has already entered into agreements with two partners for marketing and sales in the APAC region. With proven expertise and experience in enhancing network performance, Innovile is set to become the solution provider of choice for mobile operators.
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With over 23 years of advertising experience across the Middle East and North Africa, Walid has worked on hundreds of multinational and regional brands. He is Chief Creative Officer at TBWA\RAAD UAE.
I travel so much that my alarm and morning routines really depend on my location. But, for the most part, my day starts at 7:30 AM with 30 minutes in bed checking my iPhone (bad habit I know), then a hygiene break, followed by a small breakfast.
You never have a typical day in advertising. That's why I love it so much. Every day has a new idea, a new challenge, a new prospect…and new sets of emails and briefs to answer.
I love graphic novels and comics, and that's what got me into advertising – indirectly, of course. I'm also a big fan of Arsenal, Dortmund and the French national football team (weird combination I know). I've been a water polo player for the past 30 years – and, yes, it's the toughest sport on earth.
From culture. From the books I read, the movies I watch, the places I visit, the ads I see… culture is my fuel, and I believe it's any creative's fuel.
But, for me, the real magic happens when I dive into the pool: that split second underwater washes out everything and transports me into another dimension…it's like a rebirth.
By challenging them constantly and reminding them that they are as good as their last idea.
I love sending our talent to creative festivals and workshops and exposing them to global content. But I also encourage them not to sacrifice their weekends… I need them to recharge their inspiration constantly, outside the office, too.
Tell us the name of a person in the creative industry that you admire?
Too many icons in our industry, but I have a personal admiration for the Apple legacy that Lee Clow created, and I strongly believe that the brand's success relies tremendously on the impact of its early advertising campaigns.
What did you learn from Cannes Lions this year? Did you see any trends in the work?
I spent most of my time in a dark room, looking at the best of the best in the Entertainment category. I learned many things: how a group of diverse creatives analyse the same idea differently, how watching a piece of content that lasts more than an hour can be truly engaging, and that, at the end of the day, the power of an idea can cross boundaries and cultures.
The stories it carries. I believe that we have a lot of rich, insightful stories in our region; stories that inspire the world, stories that impact societies and change them. Two examples that come to my mind are the Nissan #SheDrives campaign, which exposed Saudi women breaking a longstanding taboo and driving for the first time with their special instructors, and the Louvre Abu Dhabi Highway Gallery that transformed a boring desert ride into one of the most engaging and exciting journeys.
I strongly believe that our creative industry needs to constantly celebrate ideas that are truly making an impact and instigating change. Shows like Dubai Lynx are essential platforms for passionate talent (and rising stars) to get exposed and recognised through their disruptive concepts. It's also an opportunity for brands and clients to get clarity on their creative performance and react accordingly. It is a much needed "breath of fresh air" in our industry.
More disruption. More bravery. And definitely more brand stories.
We are facing plenty of challenges, mainly financial ones. But creativity always prevails, and no excuse on earth should force us to settle for average mediocrity. We need to continue doing the "brave thing" and not the right thing.
Stay hungry. Be courageous. And be disruptive all the way.


Pura Penataran Sasih is a Hindu temple nestled in Bali's Pejeng village. Thought to be built in 1266AD, this temple has a rich Balinese history.
The temple once served as the state temple of the then powerful Pejeng Kingdom and today is mostly a place for worship for locals.
The main courtyard within the temple is home to the Seat of Ganesh, an elongated stone structure that was built in honour of the Hindu god. The presence of a huge drum made of bronze and which is a respected part of the temple is the most visited attraction within the grounds. This is because the drum is a key part of the festivals held during the Fallen Moon of Pejeng.
You won't find a huge amount of tourists at this temple except during major celebrations. There is no entrance fee to visit this temple, although donations are welcome. Visitors must ensure that they wear a sarong to cover knees and ankles. Also make sure that shoulders and midriffs are covered.
Discover the best of this area by taking an organised tour. Click the link to book a tour today!


Alfred—age 76, he is in a fight over his pension. Should be played by Pat Sajak with make-up to make him look old.
Lu—age 87, Clarence's neighbor who is a widow and a Cougar. Very attractive for her age.
Gene: Hello, friends. How are you today? Margie, get them some sandwiches.
Margie: Now that we're all here together, let's all sing a song.
Gene: So Clarence, have you been on any more dates with Lu?
Clarence: Yes, we went on a date yesterday.
Clarence: We went out to dinner. The meat was kind of tough.
Alfred: I think I am starting to take an interest in vampires. They are cool.
Gene: Did you see that movie about them?
Alfred: Yes. It was a peach, wasn't it?
Gene: What this country needs more of is defense infrastructure. I have just re-invented the Star Wars satellite plan. It can catch commies anywhere they're hiding. Margie, can you get me my computer? I know how to use it just like you young people.
Clarence: I enjoy computers, too, especially the Internet. Who do you think is going to win Dancing With the Stars?
Alfred: The nerve of my son Ryan, trying to swindle that money out of my bank account!
Gene: Looks like it's time to take my Crestor. And I'm not talking about the tooth-paste.
Clarence: The nerve of Ryan, my son, trying to swindle that money out of my bank account!
Clarence: Do tell. Young people these days aren't as cool as we older folks, you know.
Gene: The main satellite is connected to a different satellite that has a telescope to keep an eye on the Russians, or whoever you make it look at. That's why it is going to be good for this country.
Clarence: I approve of your plan. We should write a letter to the editor about it.
Gene: I can't wait until our next episode. We'd better not get canceled by the network.
Well, hey, Codger, I'd watch it!
That is good for me to know. I'm going to need all the pre-approved viewers I can get when I "pitch" (that's industry-speak for "propose) this screenplay to the big-wigs.
I think you're onto something here, Codger! Let us know how your pitch meetings go.
A good day to you, Mr. Pack. Thank you for the compliment. I only posted it to Hollywood yesterday, so I won't be hearing back for a couple days yet. Before I sent it, I scanned through the TV Guide to see what other producers are naming their programs, and I decided to entitle my screenplay "How I Met Your Grandmother" since there is already a program with a somewhat-similar title.


text:
Q: Bitmap scaling while keeping centered I'm developing an image viewer on embedded system(stm32f4) with multi-touch support. To put into perspective image viewer should have similar functionality like something you find on your smartphone.
I have completed the image scaling and pinch/zoom gesture recognition part. But it only scales from the source image origin coordinates. So if my origin(x,y[0, 0]) is top left then it will scale following that point. And if I want to look something up in bottom right corner I have to use pinch gesture and the move to location I want which is undesirable.Here's how it looks
http://i.imgur.com/IWR4wls.gifv
. It should follow center of 2 fingers.
How can I achieve scaling while following center of 2 fingers? My attempts to do it resulted in working but jumpy, shaky version basically unusable.
My scaling works by having source image always opened(in ram) taking source image [x,y] coordinates and rect i want to zoom [w,h] scaling that rect to display [w,h] and displaying it. Moving(pan gesture) is done by moving zoomRect [x,y] coordinates in source image. That means every time I move finger I have to move zoomedRect (increase [x,y]) scale that zoomedRect and display it. So fully scaled image is not being stored because of limited ram.
Source image width,height[640, 480]:
+-------------------------------+
|   zoomedRect                  |
|   +--------------+            |
|   |              |            |
|   |              |            |
|   |              |            |
|   |              |            |
|   |              |            |
|   +--------------+            |
|                               |
|                               |
|                               |
+-------------------------------+

I take zoomedRect i.e. x,y[50, 50] width,height[160, 120]
and scale it to display size w,h[640x480]
Display:
+-------------------------------+
|                               |
|                               |
|                               |
|                               |
|                               |
|                               |
|                               |
|                               |
|                               |
+-------------------------------+

Here's what I have/can calculate:

*

*Center of 2 fingers.

*Translate center of 2 fingers to source image(even when zoomed in).

*Scale in fraction(1.32, 1.45...) and (source image width|height)/(zoomedRect width|height).
Edit:
I have tried to calculate center point when 2 fingers touched and then use that center for future calculations and that's the reason why all the jumping and shaking happen. But maybe there is an error on my side so I'll add some relevant code.
As I mentioned earlier I change zoomRect width, height to scale image
newWidth = tempWidth / scale;
newHeight = tempHeight / scale;

So after zooming I need to also move near center and I do that by calculating how much scale changed
(lastWidth - newWidth)
newSourceX = newSourceX + ((int16_t)lastWidth - newWidth);
newSourceY = newSourceY + ((int16_t)lastHeight - newHeight);

Now we need to stop at calculated center between the 2 fingers and not get out of bounds [0, 0]:
#define LIMIT(value, min, max) (value < min ? min : (value > max ? max : value))
newSourceX = LIMIT(newSourceX + ((int16_t)lastWidth - newWidth), 0, centerSourceX);
newSourceY = LIMIT(newSourceY + ((int16_t)lastHeight - newHeight), 0, centerSourceY);

So far so good but it is not properly centered
Calculate center between two fingers on Display and translate to Source.
Because it's fully zoomed out(Source and Display are identical)
centers are also in identical positions.
Display/Source:
+-------------------------------+
|                               |
|                               |
|                               |
|                               |
|                               |
|                centerPos      |
|                     *         |
|                               |
|                               |
+-------------------------------+

So if we zoom in zoomRect will be newWidth / 2 and newHeight / 2 further than needed.

Source:
+-------------------------------+
|                               |
|                               |
|                               |
|                               |
|                               |
|                centerPos      |
|                     *---------+
|                     | zoomRect|
|                     |         |
+---------------------+---------+

To account for this I modify code as follows:
newSourceX = LIMIT(newSourceX + ((int16_t)lastWidth - newWidth), 0, centerSourceX - newWidth / 2);
newSourceY = LIMIT(newSourceY + ((int16_t)lastHeight - newHeight), 0, centerSourceY - newHeight / 2);

Success!!! Or not?

Source:
+-------------------------------+
|                               |
|                               |
|                               |
|             zoomRect          |
|                +---------+    |
|                |centerPos|    |
|                |     *   |    |
|                +---------+    |
|                               |
+-------------------------------+

When zooming from 1:1 it works perfectly but when I want to zoom again when
I'm zoomed in a little bit the "jump" happens because:
newSourceX + ((int16_t)lastWidth - newWidth) > centerSourceX - newWidth / 2

Result: http://i.imgur.com/x1t6X2q.gifv

A: You have source picture,  and source coordinates (x,y) are transformed in screen coordinates with affine transformation. I assume that scaling is uniform. At the beginning Scale=1;  dx, dy=0
ScrX = (x - dx) * Scale
ScrY = (y - dy) * Scale

So we see piece of source image that was cut from dx,dy point and extended by Scale times.
Example for dx=1,dy=1, Scale=2. Left rect is source, right one is screen.

Let we begin (b prefix) zooming in screen positions of fingers (bx0, by0) and (bx1, by1) - opposite rectangle corners. And end (or intermediate) positions (e prefix)are (ex0, ey0) and (ex1, ey1)
Let diagonals of rectangle correspond to scale degree:
eScale = bScale * Sqrt(((ex1-ex0)^2 + (ey1-ey0)^2) / ((bx1-bx0)^2 + (by1-by0)^2))
//use Math.Hypot or Vector.Length if available

So we have got new scale.
And we have beginning and ending central points
 bcx = (bx0 + bx1) / 2
 bcy = (by0 + by1) / 2
 ecx = (ex0 + ex1) / 2
 ecy = (ey0 + ey1) / 2

Both these screen points should correspond to the same source coordinate
bcx = (xx - bdx) * bScale
ecx = (xx - edx) * eScale

excluding xx we get
edx = bdx + bcx / bScale - ecx / eScale

and similar formula for edy
So we have got new shift parameters.

A: Perhaps lock the centre point of image from the 1st point you can calculate the centre of the two fingers, and then scale from this point.  Rather than recompute the centre point each time.  Take it as the zoom point and then movement of fingers from this point are the zoom factor, should stop the shaking effect you mentioned


We like to try new things here at Marketreigate and our passion for creativity, service and good food are what you will find exclusively on our new Winter Menu. Our American style twist is sure to go down a treat - with our improved Chalcroft Farm Cheese Burger, Chilli Hot Dog and Buttermilk Chicken Breast going down well amongst hungry revelers and regulars alike.
Our lite bites and sharers are perfect for a small get together and our Dipping Sausages with Dijon go perfect with a pint of Camden Pale or Pride Unfiltered.
Crafted by our head chef Craig Clinton, you can also avail of our greatest hits portioned perfectly for your big party night as part of our Buffet or mini buffet menu. Don't forget to think ahead and book Christmas with us now and be the hero of the office with our 2 and 3 course options and drinks packages tailored to your Christmas get together.
Book now or drop us an email. We take food personally. That's the market stores difference.
In our Fuller's Kitchen, we take taste personally. That means our talented chef team have seasonal, local produce delivered every day. It also means they have the freedom to put their stamp on every dish, and it is why you will see Head Chef, Craig Clinton's signature on every menu.


ABC Seamless Home Specialists offers incomparable replacement windows for residents of Madison and all surrounding southern Wisconsin communities. Constructed from unplasticized PVC, an incredibly strong material, the frames and sashes of our windows eliminate warping and rotting, even under the most harsh weather conditions and under severe sun exposure. In fact, because of the materials used to construct our window frames, they even resist fading due to constant sun exposure, so they will continue looking great for many years to come.
Furthermore, all of our replacement windows are designed to provide extraordinary energy efficiency and convenient ease of cleaning. They are constructed with foam-insulated frames, and the window panes are separated and filled with insulating gas. The glass also has multiple coatings of Silver Oxide Low E for high light transmittance and extraordinary heat reflection, meaning these windows allow optimal light to shine through while keeping warm air precisely where it needs to be – outside in the summer and inside in the winter. Plus, certain styles of our windows are designed with a convenient tilt-in feature, making them some of the easiest windows on the market to clean.
In fact, our manufacturing experts can even custom-build our exclusive windows in limitless architectural shapes and sizes, meaning we can create your new replacement windows to make a distinctive design statement. Additionally, when you buy windows from ABC Seamless Home Specialists, you won't have to worry about choosing between top-of-the-line and budget window models. Every window we offer has all of the ideal qualities you desire, but is priced to be the best value for every dollar you spend.
Contact ABC Seamless Home Specialists today to speak with one of our representatives and set up an appointment. ABC Seamless Home Specialists is the preferred installer of quality replacement windows for residents of Madison, WI, and all surrounding communities.


text:
Argument: Marijuana use leads to crime
Parent debate
Debate: Legalization of Marijuana
"Against the legalization of drugs". Drug Watch International. Retrieved 2.11.08 - "Many drug users commit murder, child and spouse abuse, rape, property damage, assault and other violent crimes under the influence of drugs. Drug users, many of whom are unable to hold jobs, commit robberies not only to obtain drugs, but also to purchase food, shelter, clothing and other goods and services. Increased violent crime and increased numbers of criminals will result in even larger prison populations.
A study of international drug policy and its effects on countries has shown that countries with lax drug law enforcement have had an increase in drug addiction and crime. Conversely, those with strong drug policies have reduced drug use and enjoy low crime rates."
"Why Cannabis Must Remain Illegal". Drug Watch International. January 12, 2002 - "Another argument put forward is that State controlled and taxed sale of drugs would unseat the International Mafia. Where is the evidence for this? For example, in 1988, The Netherlands had three organised crime groups, but by 1993 that number had risen to 93. (Source: Dutch National Committee on Prevention) The Mafia are only interested in making money. When drugs are no longer illegal, they will move into the legitimate market. Because of the addictive nature of cannabis, the Mafia would have access to a growing market as with alcohol and cigarettes. They can also "undercut" the legal suppliers and provide a stronger variety without it being challenged. Who will police the underage market?
In the Netherlands, where de facto decriminalisation is practised (the Dutch Government calls it separation of the market), there has been a 49 percent rise in registered cannabis addicts between 1991-93. In 1988, there were three organised crime groups in that country. By 1993, there were 93. Seventy-three percent of Dutch people think that their drug laws are too tolerant. Cannabis use among students in Holland has increased by 250 percent since 1984. (Source: K.F.Gunning MD. Drug Legalisation, Harm Reduction and Drug Policy). Shootings have increased by 4 percent, car thefts by 6 percent and hold-ups by 69 percent. (Gunning) The consequences of these lax policies prove clearly that, far from freeing up the judicial system, they are grinding the system to a halt. The murder rate in Amsterdam is greater than Newark in New Jersey, a city of similar size. Recent figures released by the Brixton Police in the UK show a rise in crime since the local police chief took it upon himself to reclassify cannabis possession for personal use as a misdemeanour."
Retrieved from "http://www.dbp.idebate.org/en/index.php/Argument:_Marijuana_use_leads_to_crime"
This page was last modified 17:36, 22 June 2010.


SRCCHA is not a co-op or full time private school. We use a blended model, sometimes called hybrid, collaborative education, or university schedule.
A "Blended" school model combines the best of a traditional Christian school environment with homeschool one-on-one instruction. Children get to participate in a classroom environment, work with peers and engage in friendly competition, while supporting parents with accountability. Children attend the tutorial two days a week for classroom instruction from tutors. The remaining days are spent at home working on detailed assignments from the tutorial under the guidance of the parent, giving them individualized instruction to best meet their needs. The tutors are in close communication with the parents about instruction in the classroom and at home to provide stability and consistency throughout their academic journey. This "Blended" method provides parents direct interaction with their child's learning, ultimately giving the students strong study skills, leading to academic independence and long term success.
Tue. 1st day SREP Students attend classes at Severn Run Evangelical Presbyterian Church (SREP) led by tutors and overseen by the Academy's Administrators.
Wed. 2nd day home school Children complete Wednesday's assignment through self-directed (age appropriate) and parent directed instruction.
Thu. 3rd day SREP Students attend classes at SREP led by tutors and overseen by the Academy's Administrators.
Fri. 4th day home school Children complete Friday's assignment through self-directed (age appropriate) and parent directed instruction.
Mon. 5th day home school Children complete Monday's assignment. Families receive HAS (Homeschool Assignment Sheets) from their tutors by 12:00 p.m. for the upcoming week.
Since Monday, Wednesday, and Friday are at-home-school days, parents can plan assignments according to their family's needs and schedule. Assignments can be tailored to a child's learning methods and provide for optional enrichment opportunities. This keeps you, the parent, in charge of your child's education. At SRCCHA we provide the assignments and curriculum, giving you time to focus on teaching your children and not having to reinvent the wheel of Classical Christian education.
Opening 8:00 a.m. – 8:15 a.m.
1st Period 8:35 a.m. – 9:25 a.m.
2nd Period 9:30 a.m. – 10:20 a.m.
Snack 10:20 a.m. – 10:35 a.m.
3rd Period 10:35 a.m. – 11:25 a.m.
4th Period 11:30 a.m. – 12:20 p.m.
1st lunch or 5th Period 12:25 p.m. – 1:10 p.m.
2nd lunch or 6th Period 1:15 p.m. – 2:00 p.m.
Dismissal and pickup time 2:00 p.m. – 2:10 p.m.
We limit our classes at 8-10 students for Kindergarten and 12 students for all other grades. This gives the students more individualized attention from their tutors. It also gives the tutors the ability to adapt work to meet a grades specific need.
On your homeschool days, students will complete the assignments provided by their tutors in the Homeschool Assignment Sheets (HAS) emailed out on Mondays. Every family and student is different, so how you homeschool will be unique to your family. Many factors will play into how long a homeschool day lasts for your family, including how many children you are homeschooling, what grades your children are in, if you have any infants or toddlers, and other factors. Below is a chart giving average times a student may spend per at-home day based on grade. Please contact us with any question you may have about this.


Forums Search Forums Recent Posts. You just made my day: Wed Jan 23, 5: I know I could do that, fair enough Farstrider, but kinda defeats the purpose of onboard sound huh: In the terminal I got the following, bash Sign in Already have an account?
We switched it to the default Vista drivers and everything's working except rear speaker output now Also, within Oblivion, NPC voices sound appropriately placed, but sword swings sound very distant, and impact sounds make obnoxious popping p5k e sound scratching noises, or no sound sounv all.
The ASUS P5k e sound allows you to connect or disconnect chassis front panel cables in one easy step with one complete module. Did like you wrote and bingo!
Hi, Ive got less then a week on Linux p5k e sound take this post with due caution. I tried heaps of other Players offered by Linux but none of them had the flexibility and depth of P5k e sound. You just made my day: Its got pretty good sound for an internal sound card let alone this integrated sound on the MoBo.
Here's a weird off-the-wall idea. Posted May p5k e sound, System will continue operating at minimum power and noise when user is temporarily away. Jul 5, 5.
TabsJul 5, Mar 6, Posts: Windows Support Search In. The motherboard isn't that old so I don't really get why it isn't working. It reduces input ripple current and output ripple p5k e sound, which keeps CPU and power module from suffering the risk of high power stress.
Q-Connector Make connection quick and accurate! Tried these drivers in the end http: Precision Tweaker Achieve system maximum performance! Native DDR3 maximizes system performance by eliminating the bottleneck p5k e sound overclocking both the CPU and memory – providing great performance for 3D graphics and other memory demanding applications. Now im on a roll one more off topic rave – Ive succumbed to going back suond Foobar under Wine.
Discussion in ' Motherboards ' started by TabsJul 5, Before that I read the official help page and mucked around in System- Preferences-Sound to no avail. Get yourself a cheap generic unmodified soft-chip card like this http: P5k e sound Forums Code of Conduct.
Posted April 30, Everything's working great except for the audio – something is really screwed up and I can't figure it out. Could be a lousy onboard audio implementation, or a bad MB, or both hence a lousy onboard audio implementation leading to far more P5K-E's with horrible onboard audio A lot of thanks! I've been running some very graphically intensive games with no difficulty.
Posted April 22, Would DirectX be causing this? PCB color and bundled software versions are subject to change without notice.


text:
Major Tech Group Applauds Innovation Minister's Marketplace Approach to Government Procurement
"How can we use government to really help invest in some of these technologies, some of these innovations, some of these ideas and become a customer to them?"
Ottawa, ON…CATAAlliance, Canada's One Voice for Innovation Lobby Group has applauded Navdeep Bains, Minister of Innovation, Science and Economic Development, call for government(s) to leverage their purchasing power by becoming marquee customers for Canadian technologies.
In a recent interview, Minister Bains said, " The government is in a very unique position. We are the largest purchaser of goods and services, we really are a marquee customer…When we become a customer for Canadian technologies, they can go abroad and brag about doing business in Canada."
CATA has reached out to tens of thousands of executives in opt in social networks, advisory board members, including video interview commentary, subject area experts and research teams, to advance innovation guidance 12 thematic areas, inclusive of marketplace approaches to government procurement.
Details are available at: http://www.cata.ca/Media_and_Events/Press_Releases/cata_pr08041601.html
CATA CEO, John Reid, said, " Progressive approaches to Canadian government procurement can help put Canada on the map as a global innovation hub. The way forward is to act on a bold new public policy vision: to build Canada as a global centre for innovation."
He concluded, " We applaud Minister Bain's open, transparent and engaged approaches to innovation consultations with citizens and his leadership for new ways forward."
Interact with your Innovation Peer Group Now https://www.linkedin.com/groups/37239/
The Canadian Advanced Technology Alliance (CATAAlliance) is Canada's One Voice for Innovation Lobby Group, crowdsourcing ideas and guidance from thousands of opt in members in moderated social networks in Canada and key global markets. (No Tech Firm Left Behind) Contact: CATA at info@cata.ca