text:
\section{Introduction}
\label{sec:intro}
Knowledge of crop-specific planted area, conditions, and expected yields is critical for informing agricultural markets and decision making as well as ensuring food security globally. Official reports by agricultural ministries, such as the USDA's Crop Progress and Condition Reports, are widely followed by market analysts and traders to inform decision-making around agricultural markets and futures. Thus, uncertainty in public information including official reports for major food-producing countries results in uncertainty and volatility in agricultural markets and prices \cite{Lehecka2014}. This uncertainty can arise when they are shocks to the system, e.g., record flooding or the COVID-19 pandemic in the 2019 and 2020 growing seasons respectively. Market tensions and perceptions of increased uncertainty about agricultural production caused by policy decisions and reduced access to information influence market prices and decisions that can disrupt market stability and food security.

Earth observations (EO) data provide a valuable complementary source of information about global crop progress and conditions, enabling timely analysis at field scales throughout the growing season to supplement traditional data collection and reporting efforts. Crop type masks that provide classifications for crop-specific land cover are critical inputs for agricultural monitoring quantities including estimates of acreage, production, condition, and yield. Prior work on crop type classification has achieved promising results using time series information to differentiate spectrally-similar crop types. However, models trained to distinguish crop types based on temporal patterns may not generalize well to inputs that have shifted in the time domain, for example due to delayed growth or planting as a result of extreme weather events or climate change.

An example of this is the 2019 growing season in the midwestern United States. In spring 2019, heavy snowfall and rains caused unprecedented flooding across the midwestern states and forced farmers to plant crops 1-2 months later than normal \cite{Irwin2019}. While such extreme weather events are historically rare, they are becoming more frequent and exacerbate uncertainty and confusion for markets and decision-makers. Thus, for crop type classification models to be used operationally, it is important that they 1) be resilient to shifting growing seasons, and 2) provide predictions within the growing season when uncertainty around planting and production is highest.

In this paper, we present a method for crop type classification that uses time series inputs at key growth stages detected at the pixel level to mitigate the effects of domain shift caused by shifts in planting timelines. Additionally, this method enables in-season predictions of crop type at each detected growth stage. We demonstrated the performance of this method for mapping corn and soybean crops in a study region of the midwestern US in northern Illinois, and showed that our growth stage-normalized approach gives better classification accuracy than using fixed time series inputs. While this study is limited to crop type classification, our approach could be used for distinguishing other land cover types that exhibit unique phenological signatures, such as other plant or tree species, where time series classification approaches may be affected by seasonal shifts or climate change.

\section{Related work}
\label{sec:related_work}
Most prior work on crop type classification incorporates spectral and temporal information in the input to a machine learning classifier. Random forests and decision trees are widely used for crop type classification due to their good performance in many case studies and their interpretability (e.g., \cite{Hariharan2018,Neetu2019,Palchowdhuri2018,Saini2018,Shelestov2019,Shukla2018,Song2017,Sonobe2017_Landsat,Sonobe2017_Sentinel,Sonobe2018,Viskovic2019,MingWang2019,Wang2019}), though the input features extracted from the satellite observations vary. For example, Song et al. \cite{Song2017} used bagged decision trees to classify time series statistics extracted from optical Landsat spectral bands, whereas Wang et al. \cite{Wang2019} used the coefficients of harmonic regression as input features for a random forest classifier. Kernel methods including support vector machines (SVMs) and kernel-based extreme learning machines \cite{Pal2013} have also shown good performance for crop type classification (e.g., \cite{Feng2019,Neetu2019,Saini2018,Sonobe2017_Landsat,Sonobe2017_Sentinel,Wagstaff2006,MingWang2019})---for example, Feng et al. \cite{Feng2019} extracted more than 100 features including spectral, textural, and phenological properties of optical observations for SVM classification.

One limitation of traditional machine learning methods such as random forests and SVMs is that they require input features to be extracted from the original data to achieve good performance, which may be sub-optimal for discriminating between target classes and often requires domain expertise. To address these limitations, recent work (e.g., \cite{Brandt2019,Cai2018,Chakrabarti2019,Garnot2019,Ji2018,Kussul2017,Russwurm2017,Zhong2019}) has demonstrated improved classification performance using neural network-based approaches which automatically learn useful features for discriminating between target classes using a standard optimization procedure \cite{LeCun2015}. Zhong et al. \cite{Zhong2019} showed that a convolutional neural network (CNN) with 1D convolutions outperformed a long short term memory (LSTM) network \cite{Hochreiter1997} for classifying time series inputs of only one spectral index (enhanced vegetation index, or EVI). Cai et al. \cite{Cai2018} showed that a deep neural network had better performance when information from multiple spectral bands and indices were included in the time series inputs. Kussul et al. \cite{Kussul2017} demonstrated better performance using an ensemble of 2D CNNs compared to 1D CNNs using time series from multiple optical bands and synthetic aperture radar (SAR) observations. Ru{\ss}wurm et al. \cite{Russwurm2017} showed that an LSTM gave better performance than a 2D-CNN for multispectral time series inputs that also included a neighborhood of pixels around the predicted pixel. Similarly, Garnot et al. \cite{Garnot2019} found that temporal features extracted in recurrent layers were more important than spatial features extracted in 2D convolutional layers for parcel-level crop type classification, but that hybrid methods combining recurrent and convolutional layers had the best performance. Brandt \cite{Brandt2019} showed that a network with capsule and attention layers in addition to CNN and LSTM layers performed better for crop type classification than a 2D CNN, LSTM, and combined CNN-LSTM. Following the success of 3D CNNs for modeling spatio-temporal relationships in videos, Ji et al. \cite{Ji2018} showed that 3D CNNs sometimes improved performance over other methods including SVMs and 2D CNNs. Together, these studies suggest that better crop type classification performance can be achieved with deep learning methods that combine both recurrent and convolutional layers to model patterns in the spectral, spatial, and temporal dimensions of remote sensing observations.

Many prior crop type classification studies have not assessed model generalization to future years that may exhibit growing season timelines that differ from the years used in training. Additionally, most prior studies rely on observations spanning the complete growing season, and thus do not provide within-season predictions of crop types. These limitations are barriers to operational adoption of crop type classification techniques. In this study, we present an approach for within-season crop type classification that is robust to shifts in growing season timelines by dynamically selecting input observations based on crop phenology. To assess the performance of this approach in future years that exhibit domain shift with respect to the training data, we evaluated our method for the 2019 growing season in the midwestern US in which planting was delayed by as much as 1-2 months.

\section{Study area and reference data}
\label{sec:dataset}

We focused our study on a region in northern Illinois that covers approximately 12,056 km$^2$ including Knox and Stark counties as well as most of Peoria, Henry, and Bureau counties. We chose this region because northern Illinois was one of the regions most affected by extreme weather and floods in early 2019, and Illinois is the second-largest producer of corn in the United States \cite{USDA_Production}. Corn and soybeans are the dominant crops grown in this region, while forests, grassland, and urban development cover most area not covered by corn and soybean fields.
\begin{figure}[t]
\begin{center}
   \includegraphics[width=\linewidth]{study_area.pdf}
\end{center}
   \caption{Inset: study area in northern Illinois covering 12,056 km$^2$ including Knox, Stark, Peoria, Henry, and Bureau counties (tile 16TBL in HLS reference system).}
\label{fig:study_area}
\end{figure}

We used satellite observations from the Harmonized Landsat-8 and Sentinel-2 (HLS) and Sentinel-1 datasets. HLS adjusts Sentinel-2 surface reflectance products to have the same bandpasses and spatial resolution (30 m/pixel) as Landsat-8. This allows the Landsat-8 and Sentinel-2 satellites to be treated as a virtual constellation with 30 m/pixel spatial resolution and 2-3 days revisit time \cite{Claverie2018}. Sentinel-1 acquires C-band synthetic aperture radar (SAR) observations every 6 days at the equator. We used observations acquired between 2017-2019 since the second Sentinel-2 satellite was not launched until 2017. For Sentinel-1, we used VV and VH polarisation bands (speckle-filtered and normalized by incidence angle). For HLS, we used the blue, green, red, near infrared (NIR), and short-wave infrared (SWIR) bands (Landsat-8 bands B01-B06 and Sentinel-2 bands B02, 03, 04, 08A, 11, and 12) as well as two spectral indices: normalized difference water index (NDWI) and land surface water index (LSWI). We computed NDWI and LSWI using the following equations:
\begin{equation}
    \text{NDWI} = \frac{\text{GREEN}-\text{SWIR1}}{\text{GREEN}+\text{SWIR1}}
\end{equation}
\begin{equation}
    \text{LSWI} = \frac{\text{NIR}-\text{SWIR1}}{\text{NIR}+\text{SWIR1}}
\end{equation}
We used NDWI and LSWI instead of more commonly used indices---e.g., normalized difference vegetation index (NDVI) or green chlorophyll vegetation index (GCVI)---because water content inside the leaf has been shown to an important factor for discriminating between corn and soybeans \cite{Cai2018,Feng2019}, which is captured by the SWIR bands included in the calculations for NDWI and LSWI. In our experiments we found that classification performance decreased when NDVI and GCVI were included in the input. We used the quality assessment (QA) layer provided in the HLS dataset to remove pixels with clouds and cloud shadows (bit numbers 0-3). The HLS tiles also contain no-data pixels (NaNs) in regions not covered by the satellite track for a particular date. To fill in no-data pixels and mitigate outliers in the time series data, we smoothed the time series for each pixel and each band using the Savitzky-Golay method \cite{Chen2004}.

We used the Cropland Data Layer (CDL) produced by the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) \cite{Boryan2011} as a surrogate for ground truth data, as has been done in prior studies (e.g., \cite{Chakrabarti2019,Wang2019}). According to the CDL, approximately 60\% of our study area is covered by corn or soybean fields, and most fields follow a corn-soybean rotation. Labels sourced from the CDL cannot be considered ground truth since they are predicted using a combination of ground truth labels and a decision tree model. However, the CDL typically has high accuracy for corn and soybeans in midwestern states \cite{Boryan2013}, with user's and producer's accuracies between 90-98\% for corn and soybeans in Illinois estimated for CDL 2017-2019 \cite{CDL_Metadata}. Lark et al. \cite{Lark2017} recommends post-processing steps to reduce the number of errors in the CDL labels, including aggregating classes and filtering out within-field speckle and other heterogeneity. We aggregated all classes except corn and soybeans into an ``other'' class. We applied a $3\times3$ homogeneity filter to identify non-homogeneous regions (caused by within-field speckle) to be masked out during training.

\begin{figure*}
  \includegraphics[width=\textwidth]{domain_shift.pdf}
  \caption{True color Harmonized Landsat and Sentinel-2 (HLS) image on DOY 154 (June 3) in 2017, 2018, and 2019 over study area subset (inset in Figure \ref{fig:maps}) (top). Different levels of vegetation at the same time of year indicate different planting timelines. Histograms of NDVI for pixels classified as corn, soybean, or other in the CDL show the distribution in 2019 is shifted significantly lower than 2017-2018 for corn and soybeans, but mostly consistent for other.}
  \label{fig:domain_shift}
\end{figure*}

\section{Approach}
\label{sec:approach}
Time series observations of crops from satellite images may have different temporal patterns from year to year depending on the planting timeline followed each year, which may change due to extreme weather or other climate factors. While methods that use the time series from the full growing season have been shown to have good performance for discriminating crop types, these methods will likely have poor performance for seasons in which planting timelines are different than in the training seasons (i.e., domain shift). Furthermore, most methods do not enable predictions of crop types within the growing season, when uncertainty around crop production is most uncertain. We propose a new approach for crop type classification that addresses domain shift by normalizing inputs by crop growth stage. In each pixel, we automatically select observations at key growth stages rather than using observations from the same dates for all pixels. These key observations are then fed to a deep neural network that combines recurrent and convolutional layers to capture spectral, spatial, and temporal relationships in the input observations.

\subsection{Growth Stage Normalization}
While specific growth stages vary between crop types, all crops generally contain three key growth stages: greenup, during which crops emerge from the ground and undergo early vegetation; peak growth, during which crops have reached their maximum growth and leaf area index (LAI), and begin to ripen; and senescence, during which crops are drying down before they are harvested. For many crops including corn and soybeans, these growth stages are apparent in the time series of vegetation indices like NDVI (e.g., \cite{Becker2019,Wardlow2006}), though regional planting timelines may vary from year to year due to weather or other climate-related factors that influence soil conditions or temperature \cite{Hatfield2015}. For this reason, it is important that observations are compared at the same growth stage, which may not be at the same time \cite{Skakun2019}. Observations of the same crop on the same day but in different years will not be comparable if the planting timelines each year were different, resulting in domain shift. Figure \ref{fig:domain_shift} shows the distribution of NDVI values in corn, soybean, and other pixels as well as true color images on the same day of year (DOY) in 2017, 2018, and 2019. In 2019, the distribution of NDVI values at the same DOY is significantly lower for both corn and soybeans due to planting delays caused by extreme flooding, while the other class distribution showed only minor differences (likely due to minor crops that were also delayed).

Since observations at the same growth stage should be comparable regardless of planting timeline, our approach is to distinguish crop types using satellite observations acquired at key growth stages rather than using fixed dates or the full growing season. For each pixel in our dataset, we computed the approximate date of greenup, peak growth, and senescence using the NDVI time series. We defined the greenup DOY to be the day after which the slope of the NDVI time series curve is highest (i.e., increasing fastest) within the first half of the time series. We defined the DOY of senescence to be the day (following the greenup DOY) after which the slope of the NDVI time series curve is the lowest (i.e., the most negative, thus decreasing fastest). We defined the DOY of peak growth to be the day at which NDVI reaches its maximum value between the greenup and senescence DOYs. Figure \ref{fig:architecture} shows an example NDVI time series with greenup, peak growth, and senescence DOYs.

\begin{figure}[t]
\begin{center}
   \includegraphics[width=0.8\linewidth]{architecture.pdf}
\end{center}
   \caption{Crop type classification approach. Spectral and spatial information from DOYs selected based on key growth stages (greenup, peak growth, and senescence) are input to neural network with CNN and LSTM branches.}
\label{fig:architecture}
\end{figure}

\subsection{CNN-LSTM Classifier}
Prior studies detailed in Section \ref{sec:related_work} suggest that neural network architectures that use both convolutional and recurrent layers to capture spatial and temporal patterns in data (respectively), in addition to using observations from multiple spectral bands, should have the best performance for crop type classification compared to alternate methods. Our model architecture (shown in Figure \ref{fig:architecture}) consists of two branches: a 2D CNN branch to capture spatial and/or textural patterns, and an LSTM branch to capture temporal patterns. The CNN and LSTM branches are joined by a dense layer before the final softmax prediction layer. Since the number of growing days is a distinguishing factor between corn and soybeans, we included a third input to the dense layer joining the CNN and LSTM branches that is the scalar number of days elapsed between the senescence DOY and greenup DOY, i.e.: $\delta=\text{DOY}_{\text{senescence}}-\text{DOY}_{\text{greenup}}$.

\textbf{Recurrent branch.} The first layer in the recurrent branch is a 1D convolution layer (8 filters, $1\times3$ kernels) followed by four LSTM layers with 16 units in all layers. All LSTM layers are densely connected, meaning the output of all previous LSTM layers is included in the input in addition to the output of the prior layer. Each LSTM layer is followed by a batch normalization \cite{Ioffe2015} layer and dropout \cite{Srivastava2014} layer (randomly dropping 30\% of units). We used the rectified linear unit (ReLU) activation function for all layers. The input to the LSTM branch consists of 6 HLS spectral reflectance values, 2 HLS spectral index values, and 2 SAR polarisation values (see Section \ref{sec:dataset} and Figure \ref{fig:architecture}) for each pixel acquired on the greenup, peak, and senescence DOYs---i.e., $\bm{x}_i^{\text{LSTM}} \in \mathbb{R}^{M\times D}$ where $M=10$ is the number of channels and $D=3$ is the number of timesteps.

\textbf{Convolutional branch.} The convolutional branch consists of two 2D convolution layers, each having 64 filters and $3\times3$ kernels with 1-pixel strides. We found that fewer or more than two convolutional layers did not improve performance for the validation dataset. We used ReLU activation for both layers, and each layer is followed by a batch normalization layer (dropout did not improve model generalization in this branch). We included a $2\times2$ max pooling layer after the two convolution layers. The input to the CNN branch consists of $5\times5$ image patches in which the central pixel is the pixel to be classified for all 10 channels and 3 timesteps. We also experimented with larger patch sizes (odd dimensions between $7\times7$-$13\times13$), but found this did not improve validation accuracy. The multispectral patches for each timestep are concatenated to produce a 3D input tensor: $\bm{x}_i^{\text{CNN}} \in \mathbb{R}^{k\times k\times MD}$ where $k=5$ is the size of the input patch, $M=10$ is the number of channels, and $D=3$ is the number of timesteps.

\textbf{Dense layer.} The output tensors of the CNN and LSTM branches are flattened and joined by a dense (fully-connected) layer with 64 units. This dense layer is followed by the final softmax output layer that gives posterior probabilities of inputs belonging to the \textit{corn}, \textit{soybean}, or \textit{other} output classes.

We implemented our CNN-LSTM architecture using the Keras deep learning library with the TensorFlow backend. The code for our model and experiments (detailed in Section \ref{sec:experiments}) is available at \url{https://github.com/nasaharvest/croptype-mapping-gsn}.

\begin{table*}
\caption{Performance metrics (overall accuracy (OA), per-class precision/user's accuracy (UA) and recall/producer's accuracy (PA)) for each crop type classification method using input observations from fixed and growth stage-normalized (GS-Norm) dates. \textbf{Bold} indicates best performance across all methods.}
\begin{center}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|}
\hline
\multicolumn{1}{|c|}{Measure} & \multicolumn{1}{|c|}{CNN-LSTM$_\delta$} & \multicolumn{2}{|c|}{CNN-LSTM} & \multicolumn{2}{|c|}{CNN} & \multicolumn{2}{|c|}{LSTM} & \multicolumn{2}{|c|}{Random Forest}\\
 & GS-Norm & GS-Norm & Fixed & GS-Norm & Fixed & GS-Norm & Fixed & GS-Norm & Fixed \\
\hline\hline

OA & \textbf{85.4} & 82.8 & 55.9 & 82.1 & 65.1 & 76.3 & 48.8 & 83.7 & 82.8 \\
UA/Precision & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } \\
\hspace{3mm}\textit{Corn} & 91.1 & \textbf{91.2} & 28.4 & 89.1 & 55.8 & 87.6 & 38.9 & 86.2 & 81.1 \\
\hspace{3mm}\textit{Soybean} & 83.2 & 86.7 & 34.6 & \textbf{91.2} & 44.8 & 86.8 & 90.8 & 89.2 & 82.1 \\
\hspace{3mm}\textit{Other} & 83.1 & 76.7 & 83.9 & 75.0 & 85.9 & 68.2 & \textbf{89.1} & 79.9 & 84.6 \\
PA/Recall & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } \\
\hspace{3mm}\textit{Corn} & 79.2 & 72.6 & 10.7 & 77.6 & 39.7 & 60.2 & \textbf{95.6} & 81.5 & 84.4 \\
\hspace{3mm}\textit{Soybean} & \textbf{81.0} & 77.7 & 57.8 & 67.6 & 58.2 & 68.2 & 2.6 & 70.2 & 74.7 \\
\hspace{3mm}\textit{Other} & 93.1 & 94.0 & 90.5 & 94.7 & 89.7 & \textbf{94.1} & 40.3 & 93.7 & 86.5 \\

\hline
\end{tabular}
\end{center}

\label{tab:e1_perf}
\end{table*}

\begin{figure*}[t]
\begin{center}
   \includegraphics[width=\linewidth]{maps.pdf}
\end{center}
   \caption{First column: Cropland Data Layer (CDL) for 2019 showing reference labels of \textit{corn}, \textit{soybean}, and \textit{other}. Remaining columns: predicted crop types for all four late-season classification models. Top row shows the full study area and bottom row shows subset of the full study area (outlined in black in CDL 2019 image) to show finer-scale differences between predicted maps and CDL.}
\label{fig:maps}
\end{figure*}

\section{Experiments}
\label{sec:experiments}

Our goal for all experiments in this study is to assess model generalization to observations acquired in future years that were not part of the training dataset, in order to assess the resilience of each method to time domain shifts. We used observations from 2017 and 2018 in the training and validation datasets and from 2019 in the test dataset, since 2017 and 2018 follow typical planting timelines for corn and soybeans in the US midwest while 2019 saw delayed planting (discussed in Section \ref{sec:intro}). We partitioned our study region (shown in Figure \ref{fig:study_area}) into quadrants and used pixels in the northwest, northeast, and southeast quadrants for training and the southwest quadrant for validation. In all experiments, we used the validation dataset to assess when learning plateaued and ensure the model did not overfit to the training dataset. We conducted three experiments to evaluate the performance for crop type classification in the beginning, middle, and end (before harvest) of the growing season.

\begin{table*}
\caption{Performance metrics (overall accuracy, per-class precision/user's accuracy (UA) and recall/producer's accuracy (PA)) for each crop type classification method for early-season and mid-season classification experiments. \textbf{Bold} indicates best performance across mid-season classification and early-season classification methods.}
\begin{center}
\begin{tabular}{|l|c|c|c|c|c|c|c|}
\hline
\multicolumn{1}{|c|}{Measure} & \multicolumn{1}{|c|}{CNN-LSTM$_\delta$} & \multicolumn{1}{|c|}{CNN-LSTM} & \multicolumn{2}{|c|}{CNN} & \multicolumn{1}{|c|}{LSTM} & \multicolumn{2}{|c|}{Random Forest}\\
 & Mid-Season & Mid-Season & Early-Season & Mid-Season & Mid-Season & Early-Season & Mid-Season \\
\hline\hline

Overall accuracy & 80.0 & 72.8 & 55.5 & \textbf{82.8} & 72.7 & \textbf{69.0} & 76.5 \\
Precision (UA) & \text{ } & \text{ } & \text{ } & \text{ }  & \text{ } & \text{ }  & \text{ }  \\
\hspace{3mm}\textit{Corn}  & \textbf{94.9} & 93.2 & \textbf{80.5} & 89.3 & 84.2 & 62.7 & 73.2 \\
\hspace{3mm}\textit{Soybean} & 82.6 & \textbf{91.2} & 51.0 & 83.6 & 89.8 & \textbf{63.1} & 87.6 \\
\hspace{3mm}\textit{Other} & 72.9 & 62.3 & 55.6 & \textbf{78.7} & 64.1 & \textbf{77.0} & 75.3 \\
Recall (PA) & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ } & \text{ }\\
\hspace{3mm}\textit{Corn}  & 63.3 & 54.8 & 10.5 & \textbf{75.0} & 53.7 & \textbf{75.6} & 76.0 \\
\hspace{3mm}\textit{Soybean}  & 75.7 & 55.9 & \textbf{49.7} & \textbf{77.8} & 60.0 & 41.1 & 52.5 \\
\hspace{3mm}\textit{Other}  & 95.8 & \textbf{97.5} & \textbf{94.6} & 92.0 & 95.5 & 81.1 & 91.7 \\

\hline
\end{tabular}
\end{center}

\label{tab:e2_perf}
\end{table*}

\subsection{Late-season classification}
In the late-season classification experiment, classifier inputs contain observations acquired at all three growth stages: greenup, peak growth, and senescence. The mean senescence DOY for 2017-2019 is $278 \pm 28$. This is around the time that crops are harvested in this region (September-November) and production estimates remain uncertain, thus our model predictions would enable estimates several months before the CDL is made available publicly by the USDA (typically February the following year). To assess the contribution of each component in our model, we compared the performance of our model (denoted CNN-LSTM$_\delta$) with the CNN branch only, the LSTM branch only, and the CNN-LSTM without the scalar $\delta$ input. To assess the benefit of normalizing inputs by growth stage rather than using fixed DOYs, we evaluated model performance using inputs from fixed DOYs (except CNN-LSTM$_\delta$ since $_\delta$ would be constant across all inputs). For the first date we used DOY 153, 149, and 152 for 2017, 2018, and 2019 respectively since greenup typically occurs around late May/early June; for the second date we used DOY 210, 209, and 211 since peak growth is typically reached around late July; and for the third date we used 274, 271, and 274 since senescence typically occurs around late September. Since random forests are widely used for crop type classification in prior work \cite{Orynbaikyzy2019}, we also compared our method to a random forest with 900 estimators (chosen based on highest accuracy from 5-fold cross validation for estimators between 100-1000 with increments of 100). The input to the random forest included the 6 optical bands, 2 SAR bands, 2 spectral index values, and $_\delta$. We implemented the random forest using Scikit-learn \cite{scikit-learn} in python.

We trained all models for this and subsequent experiments for 25 epochs (after which validation accuracy plateaued) using mini-batch sizes of 4096, cross entropy loss, and the Adam optimizer with default settings \cite{Kingma2014}. To select training samples, we randomly sampled 520,000 pixels from the available training pixels (i.e., those not masked out by the homogeneity filter) and 10,000 from the available validation pixels. To mitigate class imbalance, we subsampled majority classes to have the same number of samples as the minority class (96,459 training and 5,517 validation samples per class). We used the same random sample and seed for all experiments. We observed empirically that validation accuracy decreased when a substantially larger or smaller number of training examples was used. We report the performance metrics (overall accuracy as well as per-class user's accuracy/precision and producer's accuracy/recall) for the five models using both growth-stage normalized inputs and fixed DOYs in Table \ref{tab:e1_perf}. We used the Sieve tool in QGIS to reduce within-field speckle in the CDL labels before computing performance metrics. In Figure \ref{fig:maps}, we illustrate the qualitative performance in the predicted classification maps for our study area compared to the CDL reference labels.

Table \ref{tab:e1_perf} shows that the growth-stage normalized CNN-LSTM$_\delta$ model has the highest overall accuracy for the 2019 test data. Table \ref{tab:e1_perf} also shows that for all models, growth-stage normalized inputs give better performance than fixed DOYs. The CNN-LSTM architecture consists of two branches---an LSTM and 2D-CNN branch---with outputs joined by a dense (fully-connected) layer. The only difference between this and CNN-LSTM$_\delta$ is the addition of a third input to this dense layer, which is the scalar difference between the senescence and greenup DOYs, intended to capture the number of growing days. Table \ref{tab:e1_perf} shows that including $\delta$ increases accuracy by 2.6\%. The performance of the CNN branch-only model performs much better (by 5.8\%) than the LSTM branch-only model, which suggests that spatial information is more informative for discriminating crop types than temporal information in this experiment. While prior work found recurrent layers to have greater influence on performance than convolutional layers (e.g., \cite{Brandt2019,Garnot2019}), the difference is likely due to the shorter time series (3 timesteps) used in this study. The random forest had lower overall accuracy than the CNN-LSTM$_\delta$, but higher overall accuracy than the other neural network models. This is likely because the random forest includes the scalar $\delta$ input while the CNN-LSTM, CNN, and LSTM models do not, but does demonstrate that a random forest can achieve comparable performance to deep learning methods for this problem. Table \ref{tab:e1_perf} also shows that the difference between the random forest with growth-stage normalized and fixed DOY inputs is much lower than for the neural network models. This suggests that random forests may extrapolate better to changes in planting timelines than neural network approaches, since the random forest does not explicitly model temporal or spatial patterns that rely on the ordering of inputs.

\subsection{Early and mid-season classification}
Since all models in the late-season classification experiments described in the previous section performed better with growth stage-normalized inputs, we did not report results using fixed DOYs in the two remaining experiments for early-season and mid-season classification. We used the same experimental setup as for the late-season classification, but only included greenup and peak growth stage inputs in the mid-season classification and only greenup for the early-season classification experiments. For the CNN-LSTM$_\delta$ model, we computed $\delta$ as the difference between greenup and peak DOYs instead of greenup and senescence as in the late-season model. We only evaluated the CNN model and the random forest for early-season classification since there is no temporal information to be captured by recurrent layers. Table \ref{tab:e2_perf} gives the performance metrics for each model in the mid-season and early-season classification experiments. While the random forest results in Table \ref{tab:e1_perf} were achieved including $\delta$ in the model input, we found that the random forest had significantly higher accuracy for the mid-season classification experiment when $\delta$ was not included in the input (32.8\% with $\delta$, 76.5\% without). Thus the random forest results in Table \ref{tab:e2_perf} were achieved without the $\delta$ input.

As expected, the overall accuracy for all models decreased when the senescence DOY was excluded from the input in the mid-season classification experiment. This drop in accuracy is primarily due to low accuracy for the corn and soybean classes, as we observed that the recall for the other class remained high regardless of the number of timesteps. The CNN had the highest overall accuracy (82.8\%) for mid-season classification, emphasizing that recurrent layers are less effective given fewer temporal observations. The mean peak growth DOY in our experiments is $216 \pm 27$, so this classification performance could be achieved between July-September within the growing season.

For the early-season classification experiment, the overall accuracy is significantly lower when only the greenup timestep is included in the input to the CNN (27.3\% lower than in mid-season). This underscores the conclusion from prior studies and this study that crop type classification performance improves significantly when observations from multiple timesteps are used (even if only a few dates). The random forest accuracy is also lower in early-season than mid-season (7.5\% lower), but this difference is much lower than for the CNN. We also found that with only one timestep, the random forest performs better than the CNN. Thus, while the neural network approaches are more effective when additional growth stages are included later in the season, the random forest has the best performance given limited data in the beginning of the season. This could also be a result of the complexity of the neural network models, which is more suitable for the higher-dimension inputs in the later-season experiments. The mean greenup DOY in our experiments is $144 \pm 37$, so early-season predictions could be made using the random forest with 69.0\% accuracy between April-June near the start of the growing season.

\section{Discussion}

Most prior work on crop type classification has focused on post-season classification and have not addressed the effect of domain shift on model performance in future years not included in the training dataset. To be used operationally, models must be resilient to domain shifts that may occur in future seasons---e.g., due to delayed planting---as the global climate changes and extreme weather events become more frequent. Operational methods also can't rely on collecting more data each year to capture these shifts in training examples, e.g., due to the COVID pandemic or in countries where security is compromised. Rather than using observations from a fixed time period or set of dates, our approach normalizes inputs to be captured at the same key crop growth to ensure comparability between observations acquired in different years regardless of the planting timeline. Our experiments in Section \ref{sec:experiments} showed the best within-season classification accuracy of 85.4\% was achieved using our proposed CNN-LSTM$_\delta$ model with growth-stage normalized inputs late in the season prior to harvest (September-November), and that overall accuracy of 82.8\% could be achieved in mid-season (July-September) and 69\% at the beginning of the growing season (April-June) using the growth-stage normalized CNN and random forest respectively. In future work we will determine if this remains true when the models are applied to larger, more geographically diverse areas in the US Corn Belt states.

Our experiments confirmed prior findings that neural network architectures that used both recurrent and convolutional layers with multispectral, multi-temporal observations gave the best crop type classification performance (e.g., \cite{Brandt2019,Garnot2019}). Our late-season classification model included inputs from only three timesteps; in future work we will explore using more timesteps between the greenup and senescence DOYs to make better use of the recurrent layers. Although one of the main benefits of neural networks is that useful features can be automatically learned from data without the need for domain-specific feature engineering, we found that introducing some domain-specific information in the form of growth stage-normalized inputs enabled all models (including random forests) to be more robust to patterns in future (test) seasons not captured in the training data. Consistent with prior studies (e.g., \cite{Hariharan2018,Kenduiywo2017,Kussul2017,Orynbaikyzy2019,Shelestov2019,Sonobe2017_Sentinel}), we also found that including SAR inputs from Sentinel-1 in addition to optical inputs significantly improved classification accuracy; in future work, we will explore additional SAR parameters in addition to VV and VH polarization that can be extracted from SAR data. Finally, we plan to investigate aggregating pixel-level observations or predictions at the field-level, which has been shown to improve classification performance in prior work (e.g., \cite{Cai2018,Chakrabarti2019}).

\section{Conclusion}

Crop type classification is an important technique for providing information about planted area and enabling estimates of crop condition and forecasted yield, especially within the growing season when uncertainties around these quantities are highest. As the global climate changes and extreme weather events become more frequent, crop type classification methods (for both within-season and post-season classification) must be resilient to domain shifts (e.g., due to changes in planting timelines) that can adversely affect predictive performance. Models that provide reliable predictions without the need to collect new training data each season is especially important when ground surveys are difficult or impossible, e.g., due to conflict-related insecurity or travel restrictions imposed from the COVID-19 pandemic.

We proposed an approach for within-season crop type classification that uses remote sensing inputs normalized by crop growth stage to ensure compatibility across seasons even when domain shifts may occur. We used a neural network leveraging both convolutional and recurrent layers to predict whether pixels belong to the \textit{corn}, \textit{soybean}, or \textit{other} class. We evaluated this method for the 2019 growing season in the midwestern United States, during which planting was delayed by as much as 1-2 months due to extreme weather that caused record flooding. We showed the highest classification accuracy of 85.4\% could be achieved near harvest between September-November, 82.8\% accuracy mid-season between July-September, and 69.9\% between May-July in the beginning of the growing season. Our study was limited to a region spanning several counties in northern Illinois, and our next step is to scale our approach to all of the US Corn Belt states. We will assess operational use of this approach during the 2020 growing season to provide information to help mitigate uncertainty in agricultural markets caused by the COVID-19 pandemic.

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


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text:
Language Diversity in the US: ...
Language Diversity in the US: Most Multilingual States
By: Alison	On: September 14, 2016 In: Languages	Comments: 7
United States 2015 Census data on the most multilingual regions of the US reveals some interesting surprises as to where you can expect to find the most language diversity in the country. While some of the states that made the top ten list seem like givens — New York and California, for instance — others may take you by surprise.
1) California
California is king when it comes to bi- and multilingual residents. A whopping 45 percent of California's inhabitants speak a language other than English in the home.
California is well-known for its Hispanic population and, just as Spanish is the second most popular language in the US as a whole, it's also the second most-spoken language in the state. Other languages with a significant presence include Chinese, Korean and Vietnamese, as well as Arabic, Armenian and Tagalog.
2) Texas
Like California, many of Texas's bilingual residents use Spanish in the home. According to census numbers, 35 percent of the state's residents speak a language other than English at home, such as Chinese, German or Vietnamese.
3) New Mexico
New Mexico comes in third place in the rankings of bilingual citizens, with 34 percent of the state's population speaking another language. Unsurprisingly, most of those speak Spanish, but a fair number speak Navajo and/or other Native American languages.
4) New York
No surprise here! New York state has 31 percent of residents speaking a second language. This state is also home to NYC where you're likely to hear an enormous diversity of languages, whether it's Chinese, Italian, Russian or Yiddish — sometimes all of these within the same city block!
5) New Jersey
New Jersey rounds out the top five in our list of states with the most language diversity with 31 percent of the state's residents speaking a second language other than English. Due to its proximity to New York, a fair amount of immigrants have settled in the Garden State, contributing to populations speaking Chinese, Gujarati, Portuguese, Spanish — and of course, Italian.
Nevada is home to more than Las Vegas glitz and glam, and dry desert landscapes. This western US state is also home to a diverse number of languages, with 30 percent of the population being at least bilingual. Chinese, German and Tagalog add to the predominant second language of the Southwest: Spanish.
7) Florida
"Bienvenido a Miami" is a good way to welcome visitors to Florida's notorious party city, which is also well-known for its Spanish-speaking population. In the state as a whole, 29 percent of the population speaks a second language, including French, German and Italian.
8) Arizona
Arizona comes in at number eight as of 2015 Census data. The state claims a bilingual population of 27 percent, most of them speaking Spanish and, like New Mexico, Navajo and other Native American tongues.
It might not be the first state that comes to mind when you think of language diversity in the US, but Hawaii still makes it into the top ten with 26 percent of the Aloha State's residents claiming to be bilingual. Japanese is quite prominent on the islands, as are Chinese, Korean and Tagalog — along with Hawaiian, the state's second official language.
10. Illinois & Massachusetts (tie)
With 23 percent of their populations speaking a foreign language at home, both Illinois and Massachusetts take the final spot in our top ten list.
In Illinois, you're likely to hear residents speaking Chinese, German, Spanish and, if you're in Chicago especially, Polish. In Massachusetts, Spanish, Haitian Creole, Chinese, Portuguese, Vietnamese and French predominate.
Surprised by Multilingual States?
Were you surprised by the states that made the top 10? Did you expect any of the other 39 states to offer more language diversity than the ones shown here? Share your thoughts with us in the comment section below!
And if you're a business looking to localize your content for greater engagement, contact Accredited Language today and we'll help you reach a wider audience.
Free Localization Quote
Regional Languages of Spain Require Localization
Why Restaurants Need Effective Translation
Joseph Greenberg
This doesn't answer DIVERSITY of languages. Spanish plus English isn't diversity….
Do not large percentages of persons in Maine, New Hampshire and Louisiana speak French as well as English?
You're right – there are a number of French speakers in Maine, New Hampshire and Louisiana. For this article, we were looking specifically at the ten states which had the largest number of multilingual speakers or speakers of a language other than English. Since only about 5% of speakers in Maine and Louisiana, and only about 3% in New Hampshire speak French, they did not make the top ten.
Hi Chelsea:
I thought that I had read somewhere that a much higher percentage of persons than you indicate in Maine, New Hampshire, Louisiana and maybe even Vermont speak French and that those states were officially bilingual. Surprised to see those percentages so low. So, only Hawaii is officially bilingual? Thanks.
Finn Brady
Which states have the most languages spoken in them? California seems highly multilingual, but what about Alaska? Does not Alaska have more than 20 official languages?
Awesome2009
I can awnser that, turns out that California ain't the king,Washington is due to quite some african languages
California and Texas speaking Spanish.. it's more like Spanglish .. I know because I lived and witnessed first hand

redpajama_set_name: RedPajamaCommonCrawl


This check list will help you know what points will be considered during an inspection and develop an action plan to ensure you get the best rating you reasonably can when your business is visited.
We have a food safety management system, such as Safer Food Better Business, to ensure the production of safe food.
If you answered 'no' to any question, you need to show you are taking action to correct this failing.
It would be sensible to write down what you're not doing and what action you will be taking to correct this, who will be doing it and when you expect it to be finished.
That way, if an inspector calls before you have done the work, you can show you understood something was wrong and you are taking action to correct it. This will give the inspector confidence that you are a responsible business owner trying to get things right when you become aware they are wrong.
This check list is based on the structure of the Safer Food Better Business pack. It is not comprehensive and there are likely to be additional things in the pack relevant to your business.


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text:
package org.apache.cassandra.db.memtable;

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.concurrent.Callable;

import com.google.common.base.Throwables;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import org.apache.cassandra.db.ColumnFamilyStore;
import org.apache.cassandra.db.Directories;
import org.apache.cassandra.db.DiskBoundaries;
import org.apache.cassandra.db.PartitionPosition;
import org.apache.cassandra.db.SerializationHeader;
import org.apache.cassandra.db.SystemKeyspace;
import org.apache.cassandra.db.commitlog.IntervalSet;
import org.apache.cassandra.db.lifecycle.LifecycleTransaction;
import org.apache.cassandra.db.partitions.Partition;
import org.apache.cassandra.db.rows.UnfilteredRowIterator;
import org.apache.cassandra.io.sstable.Descriptor;
import org.apache.cassandra.io.sstable.SSTableMultiWriter;
import org.apache.cassandra.io.sstable.format.SSTableFormat;
import org.apache.cassandra.io.sstable.metadata.MetadataCollector;
import org.apache.cassandra.metrics.TableMetrics;
import org.apache.cassandra.service.ActiveRepairService;
import org.apache.cassandra.utils.FBUtilities;

public class Flushing
{
    private static final Logger logger = LoggerFactory.getLogger(Flushing.class);

    private Flushing() // prevent instantiation
    {
    }

    public static List<FlushRunnable> flushRunnables(ColumnFamilyStore cfs,
                                                     Memtable memtable,
                                                     LifecycleTransaction txn)
    {
        DiskBoundaries diskBoundaries = cfs.getDiskBoundaries();
        List<PartitionPosition> boundaries = diskBoundaries.positions;
        List<Directories.DataDirectory> locations = diskBoundaries.directories;
        if (boundaries == null)
        {
            FlushRunnable runnable = flushRunnable(cfs, memtable, null, null, txn, null);
            return Collections.singletonList(runnable);
        }

        List<FlushRunnable> runnables = new ArrayList<>(boundaries.size());
        PartitionPosition rangeStart = boundaries.get(0).getPartitioner().getMinimumToken().minKeyBound();
        try
        {
            for (int i = 0; i < boundaries.size(); i++)
            {
                PartitionPosition t = boundaries.get(i);
                FlushRunnable runnable = flushRunnable(cfs, memtable, rangeStart, t, txn, locations.get(i));

                runnables.add(runnable);
                rangeStart = t;
            }
            return runnables;
        }
        catch (Throwable e)
        {
            throw Throwables.propagate(abortRunnables(runnables, e));
        }
    }

    @SuppressWarnings("resource")   // writer owned by runnable, to be closed or aborted by its caller
    static FlushRunnable flushRunnable(ColumnFamilyStore cfs,
                                       Memtable memtable,
                                       PartitionPosition from,
                                       PartitionPosition to,
                                       LifecycleTransaction txn,
                                       Directories.DataDirectory flushLocation)
    {
        Memtable.FlushablePartitionSet<?> flushSet = memtable.getFlushSet(from, to);
        SSTableFormat.Type formatType = SSTableFormat.Type.current();
        long estimatedSize = formatType.info.getWriterFactory().estimateSize(flushSet);

        Descriptor descriptor = flushLocation == null
                                ? cfs.newSSTableDescriptor(cfs.getDirectories().getWriteableLocationAsFile(estimatedSize), formatType)
                                : cfs.newSSTableDescriptor(cfs.getDirectories().getLocationForDisk(flushLocation), formatType);

        SSTableMultiWriter writer = createFlushWriter(cfs,
                                                      flushSet,
                                                      txn,
                                                      descriptor,
                                                      flushSet.partitionCount());

        return new FlushRunnable(flushSet, writer, cfs.metric, true);
    }

    public static Throwable abortRunnables(List<FlushRunnable> runnables, Throwable t)
    {
        if (runnables != null)
            for (FlushRunnable runnable : runnables)
                t = runnable.writer.abort(t);
        return t;
    }

    public static class FlushRunnable implements Callable<SSTableMultiWriter>
    {
        private final Memtable.FlushablePartitionSet<?> toFlush;

        private final SSTableMultiWriter writer;
        private final TableMetrics metrics;
        private final boolean isBatchLogTable;
        private final boolean logCompletion;

        public FlushRunnable(Memtable.FlushablePartitionSet<?> flushSet,
                             SSTableMultiWriter writer,
                             TableMetrics metrics,
                             boolean logCompletion)
        {
            this.toFlush = flushSet;
            this.writer = writer;
            this.metrics = metrics;
            this.isBatchLogTable = toFlush.metadata() == SystemKeyspace.Batches;
            this.logCompletion = logCompletion;
        }

        private void writeSortedContents()
        {
            logger.info("Writing {}, flushed range = [{}, {})", toFlush.memtable(), toFlush.from(), toFlush.to());

            // (we can't clear out the map as-we-go to free up memory,
            //  since the memtable is being used for queries in the "pending flush" category)
            for (Partition partition : toFlush)
            {
                // Each batchlog partition is a separate entry in the log. And for an entry, we only do 2
                // operations: 1) we insert the entry and 2) we delete it. Further, BL data is strictly local,
                // we don't need to preserve tombstones for repair. So if both operation are in this
                // memtable (which will almost always be the case if there is no ongoing failure), we can
                // just skip the entry (CASSANDRA-4667).
                if (isBatchLogTable && !partition.partitionLevelDeletion().isLive() && partition.hasRows())
                    continue;

                if (!partition.isEmpty())
                {
                    try (UnfilteredRowIterator iter = partition.unfilteredIterator())
                    {
                        writer.append(iter);
                    }
                }
            }

            if (logCompletion)
            {
                long bytesFlushed = writer.getFilePointer();
                logger.info("Completed flushing {} ({}) for commitlog position {}",
                            writer.getFilename(),
                            FBUtilities.prettyPrintMemory(bytesFlushed),
                            toFlush.memtable().getCommitLogUpperBound());
                // Update the metrics
                metrics.bytesFlushed.inc(bytesFlushed);
            }
        }

        @Override
        public SSTableMultiWriter call()
        {
            writeSortedContents();
            return writer;
            // We don't close the writer on error as the caller aborts all runnables if one happens.
        }

        @Override
        public String toString()
        {
            return "Flush " + toFlush.metadata().keyspace + '.' + toFlush.metadata().name;
        }
    }

    public static SSTableMultiWriter createFlushWriter(ColumnFamilyStore cfs,
                                                       Memtable.FlushablePartitionSet<?> flushSet,
                                                       LifecycleTransaction txn,
                                                       Descriptor descriptor,
                                                       long partitionCount)
    {
        MetadataCollector sstableMetadataCollector = new MetadataCollector(flushSet.metadata().comparator)
                                                     .commitLogIntervals(new IntervalSet<>(flushSet.commitLogLowerBound(),
                                                                                flushSet.commitLogUpperBound()));

        return cfs.createSSTableMultiWriter(descriptor,
                                            partitionCount,
                                            ActiveRepairService.UNREPAIRED_SSTABLE,
                                            ActiveRepairService.NO_PENDING_REPAIR,
                                            false,
                                            sstableMetadataCollector,
                                            new SerializationHeader(true,
                                                                    flushSet.metadata(),
                                                                    flushSet.columns(),
                                                                    flushSet.encodingStats()),
                                            txn);
    }
}

redpajama_set_name: RedPajamaGithub


text:
Fu l'unica figlia sopravvissuta di Federico Augusto II, Granduca di Oldenburg dalla sua prima moglie la Principessa Elisabetta Anna di Prussia.
Sofia Carlotta ("Lotte") è meglio nota per il suo matrimonio infelice e ben pubblicizzato con il Principe Eitel Friedrich di Prussia, il secondo dei figli maschi di Guglielmo II di Germania. Il matrimonio portò successivamente al divorzio; Sofia Carlotta si sarebbe risposata dopo pochi anni con Harald van Hedemann, un ex ufficiale di polizia.

Famiglia e infanzia
Sofia Carlotta (o "Lotte") nacque il 2 febbraio 1879 nella residenza dinastica del Castello di Oldenburg ad Oldenburg in Germania. Era la maggiore dei figli di Federico Augusto, l'allora Granduca Ereditario di Oldenburg e della sua prima moglie Elisabetta Anna di Prussia. Sofia Carlotta ebbe una sorella minore chiamata Margherita, ma morì giovane. Fu chiamata in onore di Sofia Carlotta di Hannover, la moglie del primo re prussiano Federico I.

La madre di Sofia Carlotta morì nel 1895, e suo padre si risposò l'anno successivo con la Duchessa Elisabetta Alessandrina di Meclemburgo-Schwerin. Questa seconda unione diede a Sofia Carlotta quattro fratellastri e sorellastre, tra cui Nicola, Granduca Ereditario di Oldenburgo e la Duchessa Altburg, in seguito Principessa Ereditaria di Waldeck e Pyrmont. Tutti insieme risiedevano nella nuova costruzione dell'Elisabeth-Anna-Palais (chiamato in onore di sua madre). Questo secondo matrimonio di suo padre, rese la vita a casa di Sofia Carlotta infelice, ed ella fu lieta di fuggire una volta che un matrimonio adeguato le fu offerto. Suo padre successe come Granduca di Oldenburg nel 1900.

Accuse della stampa
C'era sempre stato un timore che Sofia Carlotta avesse ereditato la salute cagionevole della madre, così i soggiorni nelle varie città termali furono una componente comune della sua infanzia. Nel 1904, un giornalista tedesco fu condannato a un mese di prigione per aver alluso al rapporto tra Sofia Carlotta e l'aiutante militare di suo padre. Egli aveva scritto un articolo sul suo ritorno da un lungo soggiorno in Riviera "per la sua salute". Egli alluse alla sua "malattia", come la stessa che affliggeva due principesse di Meclemburgo-Strelitz, sulla cui moralità vi erano stati un sacco di pettegolezzi. Poiché questi soggiorni prolungati erano un aspetto comune della sua infanzia, voci del genere furono senza fondamento.

Primi incontri con il Principe Eitel
Nel giugno 1905, Sofia Carlotta incontrò per la prima volta il Principe Eitel Friedrich di Prussia alle nozze di suo fratello il Principe della Corona Guglielmo con la Duchessa Cecilia di Meclemburgo-Schwerin. Il Principe Eitel e il Principe della Corona Guglielmo erano entrambi figli di Guglielmo II di Germania ed Augusta Vittoria di Schleswig-Holstein. La madre di Eitel desiderava in maniera particolare quell'unione, poiché voleva che suo figlio sposasse un membro del Casato di Oldenburg, una famiglia considerata tranquilla, inoffensiva, e adatta ad un principe. Gli Oldenburg erano anche un'antica famiglia con forti legami sia con l'Imperatrice che con l'Imperatore (in quanto la madre di Sofia Carlotta era una principessa prussiana). La sua famiglia aveva anche altri forti legami con gli Hohenzollern, la madre di Sofia Carlotta era stato una grande intima della Duchessa di Sassonia-Meiningen, la sorella dell'Imperatore. La Duchessa era anche la madrina di Sofia Carlotta, e Sofia Carlotta era lei stessa una favorita dell'Imperatore. La sua vicinanza alla corte prussiana era così pronunciata nel fatto che essa aveva anche suscitato alcune prime voci che avrebbe sposato invece il principe Guglielmo. A Guglielmo era stato invece concesso di scegliere, e aveva deciso la Duchessa Cecilia.

Nel settembre 1905, Eitel si recò a Lensahn, una residenza degli Oldenburg, per diverse settimane. Divennero ben presto fidanzati. La forte pressione da parte di Eitel (nonostante la sua presunta riluttanza), insieme con il desiderio di Sofia Carlotta a lasciare la sua casa, potrebbe essere stata il vero motivo del loro fidanzamento. C'erano state molte voci che trapelavano sulle azioni di Eitel come soldato, sia sul campo che fuori, così i suoi genitori erano senza dubbio ansiosi di vederlo ammogliato ad una sposa apparentemente rispettabile e virtuosa.

Matrimonio

Il 27 febbraio 1906, Sofia Carlotta sposò il Principe Eitel a Berlino. Il matrimonio cadde in occasione delle nozze d'argento dell'Imperatore e dell'Imperatrice, che amplificò considerevolmente l'evento. Le nozze avevano 1,500 invitati, che comprendeva molti membri delle famiglie reali della Germania. Sofia Carlotta indossava un abito lungo tre metri e mezzo realizzato di seta bianco perla e ricamato con rose d'argento. Il matrimonio ebbe tre cerimonie - la firma del contratto di matrimonio ai sensi dello statuto del Casato di Hohenzollern il primo giorno, la somministrazione dei giuramenti di diritto civile al secondo, e infine i riti religiosi nella cappella del castello quello stesso giorno. Ella fu accolta calorosamente a Berlino.

Ebbero un matrimonio infelice. Nonostante il suo caloroso benvenuto a Berlino, Sofia Carlotta non riuscì a crearsi delle amicizie in quella città. Eitel era anche costantemente infedele. Una fonte afferma che quando si rese conto che tipo di persona che ella aveva sposato, Sofia Carlotta "si ritirò in una specie di riserva altera, da cui lei non emerse". Raramente si videro durante il suo periodo trascorso a combattere durante la prima guerra mondiale. Fu un periodo di solitudine per Sofia Carlotta, ae lei risiedeva principalmente nel Castello del Belvedere a Berlino, dove passava il suo tempo principalmente leggendo, dipingendo, e socializzando con un piccolo numero di amici.

Titoli e trattamento
2 febbraio 1879 –  27 febbraio 1906: Sua Altezza Duchessa Sofia Carlotta di Oldenburg
27 febbraio 1906 – 20 ottobre 1926: Sua Altezza Reale Principessa Sofia Carlotta di Prussia
20 ottobre 1926 - 29 marzo 1964: Sua Altezza Duchessa Sofia Carlotta di Oldenburg

Ascendenza

Note

Bibliografia

Altri progetti

Collegamenti esterni

Sofia Carlotta

redpajama_set_name: RedPajamaWikipedia


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In 2018 another dream comes true for Cougar, Flow EV Records signed a label DJ contract with him. Cougar now serves his rousing and impulsive sounds in clubs and on dance floors around the world as part of the Flow family. The future looks bright!


text:
Q: Simple circuit with transistor using Raspberry Pi I'm trying to use a Raspberry Pi to control a set of 4 LEDs. I'd like to control the LEDs in software by using a GPIO pin. Here is the schematic I've come up with... (please ignore the wire in the top right corner of the diagram, the transistor should make or break the circuit)

I'll be using a 9V battery to power 4 LEDs in series. Each LED has a voltage drop of 2V and a current rating of 20mA. Therefore my resistor should have the value of 50 Ohms (1V / 20 x 10^-3A). The diagram has an incorrect resistor value shown.
My problem is that I'm not sure of what will happen when I connect the Pi's pin to the base of the transistor. Will it mean that the pin becomes connected to the 9V circuit and will draw 20mA after I set the pin to high? Or does the pins voltage have its own influence? (the pin operates at 3.3V).
There is probably a better way to do this but I'm trying to learn about transistors so I don't want to remove the transistor.

A: This is how you should connect the NPN transistor:

R_GPIO must limit base current to about ten times LED current divided by transistor gain. Assuming a gain of 150 at the specified collector current:
$$R_{GPIO} = {{3.3 V - 0.7 V} \over {10 \cdot {20 mA \over 150}}} \approx 2 k$$

A: A correct way to do this is:

simulate this circuit – Schematic created using CircuitLab
In an NPN transistor, the voltage between emitter and base will never be more than about 0.7 volts.  In your circuit, if a High from the Pi is 3.3 volts, that would leave the emitter at 2.6 volts - not enough to turn on the LEDs.
My way, the collector/emitter voltage should be about 0.3 volts when the Pi output is High.  The base resistor limits the current drawn from the Pi output when it is High.

A: This circuit would not work, even if the collector and emitter were not shorted.  Assuming around a 2V voltage drop across each diode when they're on, the emitter would thus be sitting at about 8V -- even if the resistor were shorted out.  The base needs to be about 0.7V higher than then the emitter for the transistor to stay on  -- about 8.7V.  The Pi has 3.3V outputs, and thus isn't big enough to keep the transistor turned on

redpajama_set_name: RedPajamaStackExchange


Masoud Barzani was born in 1946 in the Mahabad Republic, the only modern Kurdish state that has ever existed. It lasted one year in northern Iran, declared under Soviet military occupation at the end of the Second World War. Months after the Soviets withdrew Iran crushed the nascent republic and executed its leaders in early 1947.
Though there has been no Kurdish state since, either in Iran, Iraq, Turkey or Syria, the dream of an independent Kurdistan was handed down to Barzani by his father, Mustafa Barzani, a leader of the influential tribe from the town of Barzan in the mountains of the Kurdish north of Iraq.
After the first Gulf War in 1991, President George H.W. Bush called on the Iraqi Kurds and the Shia in the south to rise up against Saddam Hussein. But he provided no support and the Kurds and Shia were slaughtered. In response the U.S. got a U.N. Security Council-approved "no fly zone" set up over the north and south of the country.
Under the protection of U.S. air cover, the Kurds forged a large measure of autonomy from Baghdad. But Barzani's tribe, centered in Erbil, had competition for Kurdish leadership from the Talabani tribe, in the second largest Kurdish Iraqi city, Sulemaniyah. Over three years, the two tribes fought a bitter civil war for control of the Kurdish region, with thousands killed until the Clinton White House brokered a ceasefire in 1998.
Barzani was made president of the entire Iraqi Kurdish region in a power-sharing agreement. The Clinton peace agreement however left each side with control over their own peshmerga military force and intelligence apparatus. These were never integrated even after Kurdish autonomy became formalized with Baghdad's recognition following the 2003 U.S. invasion of Iraq. (Jalal Talabani served as president of Iraq from 2005 to 2014, although primary governing authority rested with the prime minister who came from the Shiite majority).
Throughout their long history of yearning for sovereignty and subsequent suppression first by the British and then Baghdad, the Kurds have never achieved as much independence and stability as they've had in the post-invasion Kurdish Regional Government, with its capital in Erbil. Amid continued tension with Sulemaniyah, Barzani ruled the entire Kurdish region, marked by nepotism and widespread corruption, but also substantial foreign investment.
With his increased power, Barzani orchestrated a non-binding referendum for independence from Iraq in 2005 that received 98 percent support, but it was essentially ignored by Baghdad, as well as by Turkey and Iran, both with their own restive Kurdish populations. The referendum went nowhere and was seen as largely symbolic.
With extraordinary bravado, Barzani claimed that Erbil would be turned into a new Dubai. And his people bought it. Like Dubai, the shining new Erbil would be constructed on oil money, not just from the considerable oil within his territory, but eventually from the environs of the disputed city of Kirkuk.
Kirkuk has never been a majority Kurdish city, undermining Barzani's claim to it. The city has a complicated demographic history. The al-Tikriti family was the main Arab family in Kirkuk in the Seventeenth Century. A Kurdish tribe made it their capital in the Eighteenth Century. Turkmen have been present since the Eleventh Century, and became the majority as the Ottomans moved in more Turkmen in the early Twentieth Century.
By the Treaty of Ankara, registered with the League of Nations in 1926, Kirkuk became a part of the Kingdom of Iraq. Until the 1930s, Kirkuk was still largely a Turkmen town, but after oil was discovered there was an influx of Arab and Kurdish workers. According to the 1957 census, the last one taken, Kirkuk city was 37.6 percent Iraqi Turkmen, 33.2 percent Kurdish, with Arabs and Assyrians making up less than 23 percent.
A short-lived 1970 autonomy agreement with the Kurds was ended in 1974 when a new law excluded Kurdish enclaves from oil-rich areas and the city's boundaries were redrawn to create an Arab majority. From 1991 – the time of the first Gulf War – to the 2003 U.S. invasion, Saddam expelled about 500,000 Kurds from Kirkuk and surrounding towns, according to Human Rights Watch. Arab families were settled in their place. But after the 2003 invasion, thousands of displaced Kurds moved back into Kirkuk.
The new Iraqi constitution following Saddam's downfall called for a referendum for the city to determine whether it wanted to belong to Baghdad or Erbil. Though scheduled for 2007, it was never held. Baghdad did not want the Kurdish refugees who had come to the city to vote.
In 2014 when ISIS attacked Kirkuk, the Iraqi army, as it had in Mosul, retreated. Peshmerga forces fought off ISIS and took the city, an Iraqi army base and nearby oil installations as their prize.
ISIS also attacked Erbil in the summer of 2014. It was repelled by the peshmerga with the help of Iranian money and soldiers, as well as U.S. air strikes. It was that air campaign that launched President Barack Obama's war on ISIS.
The long fight against the terrorist group helped drive down world oil prices and depleted Erbil's coffers — what was left of it after corrupt officials took their cut. Foreign nationals fled the city as it was under ISIS siege. The dream of the new Dubai ended in a city with at least a third of its buildings skeletons of uncompleted construction lining the thoroughfares. Government workers went months without pay.
Without a successful referendum to run on and under pressure from ISIS, Barzani called off 2015 presidential elections. When his elected term then expired, he stayed on as president anyway. This led to a boycott of Parliament by the opposition, which shut down the legislative body for two years.
With ISIS repelled from Erbil and Kirkuk under his control, Barzani called for an independence referendum in January 2016, hoping to shore up his legitimacy. Realizing that the defeat of ISIS in Mosul was a priority, he wisely postponed the vote after foolishly calling for it.
The defeat of ISIS in Mosul this summer allowed Barzani to revive his independence referendum. He brazenly extended it to disputed areas, especially to Kirkuk. Every country in the world that mattered, including the United States, opposed the vote and pressured Barzani to call it off.
Factions of the Talabani clan in Sulemaniyah also were opposed. Only Israel supported it for its own interests of breaking up an Arab country and getting a foothold to launch operations over the border in Iran. Baghdad, Ankara and Tehran coordinated their response, threatening military action and an economic blockade of landlocked Iraqi Kurdistan, totally dependent on Turkish imports and illegal oil sales, not under control of Baghdad, to Turkey.
Despite the pressure Barzani sought to fulfill his father's and his dream of independence and held the vote on Sept. 25. As expected, it won in a landslide with 93 percent support. But there was another reason for Barzani to hold the vote. As he had clearly planned to do in January 2016 when he first called for the referendum to bolster his legitimacy, Barzani attempted to use the overwhelming referendum support to get himself elected on Nov. 1 in the Kurdish presidential and parliamentary elections and end his two-year illegitimate hold on the presidency.
Barzani made it clear that he would not declare independence after the binding referendum but instead sought one to two years of negotiations with Baghdad leading to independence. While Baghdad, Ankara and Tehran ignored the 2007 referendum this time they seemed to overreact to it. While rejecting negotiations, and with no country but Israel supporting it, Baghdad and its allies could have just ignored the referendum as it would go nowhere. It was symbolic and intended to give Barzani an electoral boost.
But Iraqi Prime Minister Haider al-Abadi is also facing elections next year. A weak response to the referendum could have cost him. A strong response was also an opportunity for Abadi to crush Barzani's electoral hopes. Instead of bolstering his position, Barzani handed Abadi another crucial opportunity, a gift on a gilded platter: the chance to use the referendum as a pretext to settle the Kirkuk issue in Baghdad's favor.
The city was occupied by 6,000 peshmerga fighters. However, as the Iraqi army and Iranian-backed Shia militia moved on Kirkuk on the night of Oct. 15, thousands of Kurds fled the city fearing a bloodbath.
But Baghdad took back its army base, its oil installations and control of the city with hardly a shot fired. It emerged that Abadi had forged a deal with the peshmerga to stand down. But not Barzani's peshmerga. Instead, the peshmerga of Talabani's Sulemaniyah, which had never been integrated with Barzani's forces. Arabs and Turkmen celebrated wildly in the city as they tore down the Kurdish flag.
Faced with this outcome, Barzani first called off the Nov. 1 elections, which he could now not count on to win. And on Wednesday he went a step further, completing his humiliation of losing Kirkuk, and perhaps ending his reign: he gave in to Baghdad's demands to cancel the referendum, offering to "freeze" the results while pleading for new negotiations with Baghdad, setting back his hopes of independence for at least a generation.
← What Did Hillary Clinton Know?
Undeniably it's a double whammy approach by Abadi government to pursue a bloody militia led war against the Kurds in Iraq under the premise of the 'constitution'. While other parts of Iraq have consistently breached constitution rule, but without any reaction from Abadi. The other spooky part is that Abadi, instead of using Iraqi army, he is actually given the lead to the Iranian backed militias to occupy Kurdish territories. Only in the town of Tuz Khurmatu near Kirkuk, Kurds have fled in thousands, their homes were burnt down and many were killed by the militias. https://www.amnesty.org.uk/press-releases/iraq-kurdish-homes-targeted-wave-attacks-government-backed-militias It is true though, that the Kurdish region in Iraq is driven by two clan families, Barzani and Talabani, while corruption and nepotism is rife, the Kurdish economy suffers from $20b debits, mainly missing oil funds. The two powerful clans run the region without any accountability or monitoring, even the local parliament has been suspended by Barzani over two years ago, when lawmakers raised his lapsed presidential terms. What is vital now is that the two clan rulers must resign, and let the ball rolling for the resumption of the Kurdish Parliament and forming a transitional government, mainly from technocrats and planners, to negotiate terms with Baghdad, based on the constitution.
How come the UN will force European countries to break up but not Muslim?
Question: Let's start from the recent developments; recently, the Secretary of State Rex Tillerson called on Iraqis to disband the Iran-backed militias. The Iraqi prime minister rejected that. Do you intend to pursue this matter and how?
McMaster: Well, we have to support the government of Iraq, we have to support Prime Minister [Haider al-] Abadi, who's done, I think, a tremendous job under very difficult conditions. But, as everybody knows, the Iranians have done a very good job, also, of infiltrating and subverting Iraqi state institutions and functions, as well as creating these militias that lay outside of the Iraqi government's control. And, I think, what they intend to do is use them opportunistically to advance Iranian interests. You see that in reaction to the Kurdish referendum, for example, and, so, what really needs to happen is all of the drivers of this terrible fitna, this terrible sectarian violence, have to be addressed and that has to be removing all causes of that kind of violence.
I appreciate Joe Lauria's reporting on Kurdistan. I don't understand why he isn't reporting the Russian government's longstanding advocacy of Kurdish national rights in the form of urging that the countries with large Kurdish populations adopt federal, constitutional forms which grant autonomous rights for Kurds and other national minorities. Russia's proposal for Syria is being widely discused there and it has no doubt positively influenced the actions of the Syrian government in the military campaign against right-wing jihadi forces.
But Baghdad took back its army base, its oil installations and control of the city with hardly a shot fired. It emerged that Abadi had forged a deal with the peshmerga to stand down.
It would be interesting to learn whether the "Talabani" Kurds are brighter, were bought off, or both. Baghdad (or Iran) may have learned from the way somebody bribed key Iraqi officers and officials to disappear when ISIS moved in their direction. Naturally this left the men on the ground without either leadership or supplies.
Obama's war on ISIS? NONSENSE! ISIS was a very useful tool for Obama and his neocons, and it got a hell of a lot more help from the US than it did unfriendly bullets or bombs. Notice that Erbil/Arbil got actual US assistance. Mosul didn't, and neither did Baghdad. That was the pattern – ISIS got lightly spanked only when it went off script.
In my opinion Israel and the US hoped Baghdad would fall to ISIS. This would have drastically crimped the slight recovery Iraq had been making since the Invasion For Israel Bush the Dumber made on that nation. The US made very sure the Iraqis had no effective way of fighting ISIS – besides the sabotage of the military probably financed by Saudi Arabia, the Iraqis had no air force worth mentioning.
Only when Baghdad did manage to survive were the first 4 severely limited F-16s permitted to go to Iraq. Probably this was to forestall even more more support from Iran and Russia. Even now Iraq has only 13 F-16 aircraft. By way of contrast the Netherlands has 61 of the planes, and Jordan 46 of them. Oh yes, Obama was after Iraq's continued demolition, just like his predecessor, the Codpiece Commander. Probably Israel will build a statue to the both of them someday.
Speaking of the cesspool nation, Israel is really desperate with its Kurdish project, enough to request permission from the Russians for an air corridor to the place.
Assistance? Maybe, but just as likely they want to evacuate important Israelis without risking the increasingly risky overland routes. Nobody is exactly great buddies of the Kurds these days, and piles of bribes just might not be enough.
The mil-bloggers I read have been suggesting for 3-4 years that the Kurds were being downright stupid in their cozying up to Israel and the US, apparently unaware that they were as disposable as toilet wipe products to both nations.
That would be an interesting debate, the extent to which the US and Israel supported Isis and AlQ to destabilize Iraq, having accidentally created a Shiite-majority state allied with Iran.
It is very sad that Russia, Turkey, Iran, and the US did not predict and dissuade Barzani from the referendum, which has weakened Iraq and could greatly protract the residual Isis insurgency. Clearly the Kurds of Iraq must accept autonomy within a federation, and Iraq must grant the same autonomy to the Sunni regions to avoid more uprisings there.
A major problem of democracy is the failure of ethnic/religious groups to grant equal rights to the others, largely the result of historic injustices causing fears and militancy. The problem spans all cultures, such as Ukraine, and the US before the Civil War. But the US is even less diplomatic and democratic now than when it failed to resolve its own far simpler regional differences.
A major problem is the Israeli troublemaking, arming the Kurds to destabilize Iraq and Iran, threatening to move 200,000 Jewish Kurds there, and motivating Barzani and others to seek far more autonomy than practical.
The US should adamantly oppose Israeli troublemaking, but has been corrupted to support it. Without restoration of democracy in the US, we cannot be a positive influence on developing nations, but instead cause enormous suffering.
I'm on the page where, as I attempt to catch up on what the Kurds are all about, I keep hearing these rumblings of Israeli interest in a independent Kurd State. Then the next question in my mind, is what is in it for the Israeli supportive diplomacy in favor of the Kurds? Then a possible answer to my inquiry becomes potentially clearer as I gaze upon a map of that region in the Middle East.
Brad your concern shows to me that you are a compassionate, and caring person. I know words like these are all to often used, but with the way you ended your comment it shows you have the right priorities in place. Timely is your comment for me, because when I woke up this morning I thought to myself how Puerto Rico is falling out of the news cycle, and with that those unfortunate fellow Americans of ours are fading out of the news, which will mean they are losing out 'bigly'.
America was never a perfect place, but today it is apparent that it is a spoiled nation who's rhetoric does not match its performance. With better than 800 military bases stretched around the globe, and yet the homelands biggest threat is it's constant movement towards it's estsblishing a permanent police state to squeeze it's citizens civil rights to death.
Thanks, that article is worthwhile. Israel has done as much damage to the Mideast in assisting the Kurds to demand autonomy, as in its bribery of the US to conduct genocides to support its land thefts in Palestine.
Sam F, . "Without restoration of democracy in the US, we cannot be a positive influence on developing nations, but instead cause enormous suffering." The basic problem from which all others flow.
The countries in the region and the Kurds need to come to an understanding that assure the rights of the Kurds in their respective countries. It is a bad idea with the existing cultural and religious differences to create a Kurdish state, or a Sunni or a Shiite state. Foreign powers have exploited these differences up to now and the countries in the region should do everything possible to prevent the creation of a new state for the Kurds or any other religious or cultural group.
"the countries in the region should do everything possible to prevent the creation of a new state for the Kurds or any other religious or cultural group." No, Herman. the countries in the region have no right to interfere with the aspirations of an ethnic group to form their own nation. Religious sects i.e. the Sunnis and the Shias are quite different as their division is based on a feudal concept. However, I believe it would be wise for the Kurds to curb their independence aspirations for the time being. The Sunnis need to divest themselves of radical elements in order to achieve any integration into Iraq. Otherwise a three state solution can only be a temporary solution to what is in fact an artificial state created by colonial powers.
No, Herman. the countries in the region have no right to interfere with the aspirations of an ethnic group to form their own nation.
The countries involved all see it differently, and so do I. The Kurds are not exactly a close-knit group, and both in language and religion they can vary wildly. About the best they can hope for is a respectable amount of self rule within the larger nation to which they belong.
As far as the opinions of you or I, these are value judgements. I believe in cultural pluralism and I assume you do also, Zachary, but how can we talk about Palestinian rights and dismiss Kurdish rights?
How can any cultural minority whether Kurdish,Catalonian or Palestinian accept being treated as second hand citizens that must assimilate or die? How can they "belong" to a larger country that doesn't respect diversity?
I support self determination. Especially in nations created by colonial powers, especially when they are oppressed. Kurdistan needs to exist as a refuge for Kurds. Catalonia should have self determination as also any region in any nation.
It is true that self-determination is always defensible for individuals, and that substantial minority rights must be respected, including the defense of all cultural/religious/ethnic traditions that do not interfere with rights of others. Secession is the natural desire of those who are not so respected.
Also true that the states defined randomly by colonial powers or by treaties can be offensive, and often do not have the political development to respect those rights.
But secession is not always practical or desirable. Where East Ukraine and Crimea have strong cultural ties to Russia and are militarily persecuted by the coup regime of West Ukraine, it is difficult to see how a federation would work. Where the Sunnis of Iraq were disenfranchised and denied the autonomy of Iraqi Kurds, it is not surprising that insurgencies arose among them; they should be given regional autonomy in Anbar similar to that of the Kurds. But the Kurds have autonomy and representation in government, and are completely landlocked geographically, unable to trade except by cooperation with those from whom they seek secession.


In today's critique Craig looks at a beautiful landscape image. Craig discusses framing and subject placement. Craig also discusses quality of line and visual unity. Craig talks about color theory relative to the idea of potential color clashes. Craig creates a black and white variation of the image using low contrast as the main tool to increase visual unity in the image.


Goudas were developed in the Netherlands hundreds of years ago. They are characterized by a rich and creamy texture making them some of our most popular cheeses.
Today, Wisconsin cheese makers are producing some of the finest Goudas in the world as is evidenced in the 100 plus U.S. and World Championships won over the past decade by Wisconsin cheese makers.


what makes this car special?
The Bentley S Continental was introduced six months after the introduction of the Bentley S1 standard steel saloon (1955). The Continentals were the high-performance Bentley models of this time with the name evoking images of high speed 120 mph grand touring. The Continentals left the factory in chassis only form. Coachbuilders such as HJ Mulliner, Park Ward and James Young were commissioned to design light weight alloy fixed head and drophead coupe bodies (using aluminium and magnesium). These coach built body styles for the Bentley S1 Continental chassis were re-interpretations of the elegant designs of the preceding R type Continentals.


A restatement of the relation of reflective thinking to the educative process. This made New York one of the most diverse and prosperous colonies in the New World.
Being a history student sometimes feels a bit wasteful. Finally, a written evaluation essay providing a self-assessment of how effectively they met the learning objectives of the course is suggested for the portfolio. Yesterday I held the card that my mother had sent me when I first went away Reflection journal five college.
She asked questions she knew her students could answer, implying that she was thinking of questioning as another "attention-getting" technique rather than a strategy to ignite thinking. However, this ratio can be distorted by depreciation or any unusual expenses. Students then research the social issue and read three to five articles on the topic.
Teresa needs to ask different questions that might lead to better results. I was thinking of how exactly I would start my creative project class for this course…what better way than a scrapbook…with a card to my own mother to start.
Directed writings provide opportunity for application and critical analysis of the course content. Directed Writings - Directed writings ask students to consider the service experience within the framework of course content.
How to use your journal? Student Portfolios - This type of documentation has become a vital way for students to keep records and learn organizational skills. I really enjoyed seeing them after being away from so long, but what they showed me during their visit was just how much of a Londoner I have truly become.
I would also like to make a scrapbook with all my pictures and tickets to keep with me.
A low inventory turnover rate, on the other hand, means that the company is paying to keep a large inventory, and may be overstocking or carrying obsolete items. But this is not what makes the experience worthy of academic credit.
Rich Text Content A. Reflection is a skill that is best fostered with colleagues.
The raw data used to compute the ratios should be recorded on a special form monthly. Experts suggest that companies usually need at least percent ROI in order to fund future growth.
This ratio is similar, and can easily be converted to, the debt to equity ratio. It gave me hope that, although the current situation Alva found herself in at such a young age was difficult, she was going to be alright …maybe better than a lot of kids sleeping in their own beds because in her life she had what really mattered.
Do you feel your actions had any impact? A very low return on asset, or ROA, usually indicates inefficient management, whereas a high ROA means efficient management.
Students write weekly summaries and identify critical incidents that occurred at the service site.It's New Year's Eve (where I live), and I've been doing a lot of reflection over the last year.
It's the perfect time of year to look back and reflect on what you've done right this year, to learn from what you've done. And on further reflection, this habit of reflection is something.
Sarah Norcini Reflection Journal #5 1. A specific situation when someone assisted me was when I needed help on my extended essay last year with editing. 2. A specific situation when I assisted someone else was when a classmate asked me for help on a personal problem and they thought I. For years I've been advocating the power and pleasure of being grateful.
I kept a gratitude journal for a full decade without fail—and urged you all to do the same. Then life got busy. My schedule overwhelmed me. I still opened my journal some nights, but my ritual of writing down five things I.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a Japanese sensor which is one of five remote sensory devices on board the Terra satellite launched into Earth orbit by NASA in The instrument has been collecting data since February A reflective journal is a personal account of an educational experience that offers a variety of benefits, from enhancing your writing skills and helping you retain information to allowing you to express your thoughts on new ideas and theories.
3 A Comparative Analysis of Reflection and Self-Assessment Melissa Desjarlais1, Peter Smith2 Abstract Reflection is a personal process that can deepen one's understanding of self and can lead to significant discoveries or insights, while.


Natures Way Foods is delighted and proud to have been featured in the 2016 Top Track 250 league table!
This national recognition strengthens our reputation in the industry and positions us among the "ones to watch". It highlights that our investment board is committed to supporting our goals and will enable us to attract the most talented individuals for us to achieve those goals.
We know that strategically we are on the right track with our expansion in the Food to Go category and keep the consumer needs at the heart of our development. With our fourth manufacturing site due for completion in March 2017, we are well-positioned to build on the strong foundation already established.


Quantum GIS is an opensource desktop GIS tool that supports ESRI Shape, MapInfo Tab/MIF, PostGIS and various raster formats such as GeoTiff, ErDas Imagine, Jpeg 2000, ERMapper (ECW), and ESRI ArcInfo ASCII Grid. It has both viewing and some rudimentary editing and attribute querying capabilities.
Quantum works on Windows and Unix. It works on Mac as well, but not quite clear how well it works on Mac.
The most recent version of QGIS has a plug-in architecture for Python to allow for creating Python based plug-ins. It also works with GRASS.


Srinagar: The situation in Kashmir and the border issue were the top points that were discussed during the meeting of National Security Advisors (NSAs) of India and Pakistan, Ajit Doval and Nasir Khan Januja.
The meeting is part of the agreement between the two NSAs to meet and sort out bi-lateral issues between the two countries.
Similar meetings in Bangkok had taken place in the past as well.
Top officials said that the talks were crucial keeping in mind the situation between the two countries.
It is important that the back channel talks continue as both nations need to try and talk peace.
The secret meeting at Bangkok, however, did not go into the Kulbhushan Jadhav issue.
The meeting took place a day after the mother and wife of Jadhav met him.
The meeting was a pre-scheduled one said top sources.
NSA Ajit Doval met with his counterpart Nasir Khan Januja at Bangkok which is a neutral venue. The decision to meet was taken last month itself.
The foreign ministries of both countries were in the loop about the meeting, sources also said.
The meeting of the NSAs was an important one as the two nations share a troubled relationship.
Januja had recently said at an event that the stability of the South Asian region hangs in a delicate balance and the possibility of a nuclear war cannot be ruled out.
He further said that special efforts must be taken to maintain a balance in South Asia.
A meeting in a third country is preferable for both India and Pakistan as the top officials can avoid the limelight.
This is not the first time that the two NSAs have met in Bangkok. In 2015 a similar meeting had been held in Bangkok.
The meeting according to sources is significant as it sets the stage for the foreign ministries to hold discussions.
There are a host of issues that need to be discussed at the foreign ministry level in a bid to ease tensions between the two countries.


text:
ACCEPTED

#### According to
The Catalogue of Life, 3rd January 2011

#### Published in
null

#### Original name
Agropyron jacquemontii Hook.f.

### Remarks
null