text:
"""Linear Estimators."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.contrib import layers
from tensorflow.contrib.framework.python.ops import variables as contrib_variables
from tensorflow.contrib.learn.python.learn.estimators import estimator
from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
from tensorflow.contrib.learn.python.learn.estimators import prediction_key
from tensorflow.contrib.linear_optimizer.python import sdca_optimizer
from tensorflow.contrib.linear_optimizer.python.ops import sdca_ops
from tensorflow.contrib.linear_optimizer.python.ops.sparse_feature_column import SparseFeatureColumn
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.training import session_run_hook

def _head_is_valid_for_sdca(head):
  """Returns true if the provided head is supported by SDCAOptimizer."""
  # pylint: disable=protected-access
  return isinstance(head, head_lib._BinaryLogisticHead) or isinstance(
      head, head_lib._BinarySvmHead) or isinstance(head,
                                                   head_lib._RegressionHead)
  # pylint: enable=protected-access

def _add_bias_column(feature_columns, columns_to_tensors, bias_variable,
                     columns_to_variables):
  """Adds a fake bias feature column filled with all 1s."""
  # TODO(b/31008490): Move definition to a common constants place.
  bias_column_name = "tf_virtual_bias_column"
  if any(col.name is bias_column_name for col in feature_columns):
    raise ValueError("%s is a reserved column name." % bias_column_name)
  if not feature_columns:
    raise ValueError("feature_columns can't be empty.")

  # Loop through input tensors until we can figure out batch_size.
  batch_size = None
  for column in columns_to_tensors.values():
    if isinstance(column, tuple):
      column = column[0]
    if isinstance(column, sparse_tensor.SparseTensor):
      shape = tensor_util.constant_value(column.dense_shape)
      if shape is not None:
        batch_size = shape[0]
        break
    else:
      batch_size = array_ops.shape(column)[0]
      break
  if batch_size is None:
    raise ValueError("Could not infer batch size from input features.")

  bias_column = layers.real_valued_column(bias_column_name)
  columns_to_tensors[bias_column] = array_ops.ones(
      [batch_size, 1], dtype=dtypes.float32)
  columns_to_variables[bias_column] = [bias_variable]

def _get_sdca_train_step(optimizer, columns_to_variables, weight_column_name,
                         loss_type, features, targets, global_step):
  """Returns the training operation of an SdcaModel optimizer."""

  def _dense_tensor_to_sparse_feature_column(dense_tensor):
    """Returns SparseFeatureColumn for the input dense_tensor."""
    ignore_value = 0.0
    sparse_indices = array_ops.where(
        math_ops.not_equal(dense_tensor,
                           math_ops.cast(ignore_value, dense_tensor.dtype)))
    sparse_values = array_ops.gather_nd(dense_tensor, sparse_indices)
    # TODO(sibyl-Aix6ihai, sibyl-vie3Poto): Makes this efficient, as now SDCA supports
    # very sparse features with weights and not weights.
    return SparseFeatureColumn(
        array_ops.reshape(
            array_ops.split(value=sparse_indices, num_or_size_splits=2,
                            axis=1)[0], [-1]),
        array_ops.reshape(
            array_ops.split(value=sparse_indices, num_or_size_splits=2,
                            axis=1)[1], [-1]),
        array_ops.reshape(math_ops.to_float(sparse_values), [-1]))

  def _training_examples_and_variables():
    """Returns dictionaries for training examples and variables."""
    batch_size = targets.get_shape()[0]

    # Iterate over all feature columns and create appropriate lists for dense
    # and sparse features as well as dense and sparse weights (variables) for
    # SDCA.
    # TODO(sibyl-vie3Poto): Reshape variables stored as values in column_to_variables
    # dict as 1-dimensional tensors.
    dense_features, sparse_features, sparse_feature_with_values = [], [], []
    dense_feature_weights = []
    sparse_feature_weights, sparse_feature_with_values_weights = [], []
    for column in sorted(columns_to_variables.keys(), key=lambda x: x.key):
      transformed_tensor = features[column]
      if isinstance(column, layers.feature_column._RealValuedColumn):  # pylint: disable=protected-access
        # A real-valued column corresponds to a dense feature in SDCA. A
        # transformed tensor corresponding to a RealValuedColumn has rank 2
        # (its shape is typically [batch_size, column.dimension]) and so it
        # can be passed to SDCA as is.
        dense_features.append(transformed_tensor)
        # For real valued columns, the variables list contains exactly one
        # element.
        dense_feature_weights.append(columns_to_variables[column][0])
      elif isinstance(column, layers.feature_column._BucketizedColumn):  # pylint: disable=protected-access
        # A bucketized column corresponds to a sparse feature in SDCA. The
        # bucketized feature is "sparsified" for SDCA by converting it to a
        # SparseFeatureColumn respresenting the one-hot encoding of the
        # bucketized feature.
        #
        # TODO(sibyl-vie3Poto): Explore whether it is more efficient to translate a
        # bucketized feature column to a dense feature in SDCA. This will likely
        # depend on the number of buckets.
        dense_bucket_tensor = column._to_dnn_input_layer(transformed_tensor)  # pylint: disable=protected-access
        sparse_feature_column = _dense_tensor_to_sparse_feature_column(
            dense_bucket_tensor)
        sparse_feature_with_values.append(sparse_feature_column)
        # For bucketized columns, the variables list contains exactly one
        # element.
        sparse_feature_with_values_weights.append(
            columns_to_variables[column][0])
      elif isinstance(
          column,
          (
              layers.feature_column._CrossedColumn,  # pylint: disable=protected-access
              layers.feature_column._SparseColumn)):  # pylint: disable=protected-access
        sparse_features.append(
            SparseFeatureColumn(
                array_ops.reshape(
                    array_ops.split(
                        value=transformed_tensor.indices,
                        num_or_size_splits=2,
                        axis=1)[0], [-1]),
                array_ops.reshape(transformed_tensor.values, [-1]), None))
        sparse_feature_weights.append(columns_to_variables[column][0])
      elif isinstance(column, layers.feature_column._WeightedSparseColumn):  # pylint: disable=protected-access
        id_tensor = column.id_tensor(transformed_tensor)
        weight_tensor = column.weight_tensor(transformed_tensor)
        sparse_feature_with_values.append(
            SparseFeatureColumn(
                array_ops.reshape(
                    array_ops.split(
                        value=id_tensor.indices, num_or_size_splits=2, axis=1)[
                            0], [-1]),
                array_ops.reshape(id_tensor.values, [-1]),
                array_ops.reshape(weight_tensor.values, [-1])))
        sparse_feature_with_values_weights.append(
            columns_to_variables[column][0])
      else:
        raise ValueError("SDCAOptimizer does not support column type {}".format(
            type(column).__name__))

    example_weights = array_ops.reshape(
        features[weight_column_name],
        shape=[-1]) if weight_column_name else array_ops.ones([batch_size])
    example_ids = features[optimizer.example_id_column]
    sparse_feature_with_values.extend(sparse_features)
    sparse_feature_with_values_weights.extend(sparse_feature_weights)
    examples = dict(
        sparse_features=sparse_feature_with_values,
        dense_features=dense_features,
        example_labels=math_ops.to_float(
            array_ops.reshape(targets, shape=[-1])),
        example_weights=example_weights,
        example_ids=example_ids)
    sdca_variables = dict(
        sparse_features_weights=sparse_feature_with_values_weights,
        dense_features_weights=dense_feature_weights)
    return examples, sdca_variables

  training_examples, training_variables = _training_examples_and_variables()
  sdca_model = sdca_ops.SdcaModel(
      examples=training_examples,
      variables=training_variables,
      options=dict(
          symmetric_l1_regularization=optimizer.symmetric_l1_regularization,
          symmetric_l2_regularization=optimizer.symmetric_l2_regularization,
          num_loss_partitions=optimizer.num_loss_partitions,
          num_table_shards=optimizer.num_table_shards,
          loss_type=loss_type))
  train_op = sdca_model.minimize(global_step=global_step)
  return sdca_model, train_op

def sdca_model_fn(features, labels, mode, params, config=None):
  """A model_fn for linear models that use the SDCA optimizer.

  Args:
    features: A dict of `Tensor` keyed by column name.
    labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
      dtype `int32` or `int64` with values in the set {0, 1}.
    mode: Defines whether this is training, evaluation or prediction.
      See `ModeKeys`.
    params: A dict of hyperparameters.
      The following hyperparameters are expected:
      * head: A `Head` instance. Type must be one of `_BinarySvmHead`,
          `_RegressionHead` or `_BinaryLogisticHead`.
      * feature_columns: An iterable containing all the feature columns used by
          the model.
      * l1_regularization: Global (across all examples) L1-regularization
          parameter.
      * l2_regularization: Global (across all examples) L2-regularization
          parameter.
      * num_loss_partitions: Number of partitions of the global loss function
          optimized by `SDCAOptimizer`.
      * weight_column_name: A string defining the weight feature column, or
          None if there are no weights.
      * update_weights_hook: A `SessionRunHook` object or None. Used to update
          model weights.
    config: `RunConfig` object to configure the runtime settings.

  Returns:
    A `ModelFnOps` instance.

  Raises:
    ValueError: If the type of head is not one of `_BinarySvmHead`,
      `_RegressionHead` or `_MultiClassHead`.
    ValueError: If mode is not any of the `ModeKeys`.
  """
  head = params["head"]
  feature_columns = params["feature_columns"]
  example_id_column = params["example_id_column"]
  l1_regularization = params["l1_regularization"]
  l2_regularization = params["l2_regularization"]
  num_loss_partitions = params["num_loss_partitions"]
  weight_column_name = params["weight_column_name"]
  update_weights_hook = params.get("update_weights_hook", None)

  loss_type = None
  if isinstance(head, head_lib._BinarySvmHead):  # pylint: disable=protected-access
    loss_type = "hinge_loss"
  elif isinstance(head, head_lib._BinaryLogisticHead):  # pylint: disable=protected-access
    loss_type = "logistic_loss"
  elif isinstance(head, head_lib._RegressionHead):  # pylint: disable=protected-access
    loss_type = "squared_loss"
  else:
    raise ValueError("Unsupported head type: {}".format(type(head)))

  assert head.logits_dimension == 1, (
      "SDCA only applies to logits_dimension=1.")

  # Update num_loss_partitions based on number of workers.
  n_loss_partitions = num_loss_partitions or max(1, config.num_worker_replicas)
  optimizer = sdca_optimizer.SDCAOptimizer(
      example_id_column=example_id_column,
      num_loss_partitions=n_loss_partitions,
      symmetric_l1_regularization=l1_regularization,
      symmetric_l2_regularization=l2_regularization)

  parent_scope = "linear"

  with variable_scope.variable_op_scope(features.values(),
                                        parent_scope) as scope:
    logits, columns_to_variables, bias = (
        layers.weighted_sum_from_feature_columns(
            columns_to_tensors=features,
            feature_columns=feature_columns,
            num_outputs=1,
            scope=scope))

    _add_bias_column(feature_columns, features, bias, columns_to_variables)

  def _train_op_fn(unused_loss):
    global_step = contrib_variables.get_global_step()
    sdca_model, train_op = _get_sdca_train_step(optimizer, columns_to_variables,
                                                weight_column_name, loss_type,
                                                features, labels, global_step)
    if update_weights_hook is not None:
      update_weights_hook.set_parameters(sdca_model, train_op)
    return train_op

  model_fn_ops = head.create_model_fn_ops(
      features=features,
      labels=labels,
      mode=mode,
      train_op_fn=_train_op_fn,
      logits=logits)
  if update_weights_hook is not None:
    return model_fn_ops._replace(training_chief_hooks=(
        model_fn_ops.training_chief_hooks + [update_weights_hook]))
  return model_fn_ops

class _SdcaUpdateWeightsHook(session_run_hook.SessionRunHook):
  """SessionRunHook to update and shrink SDCA model weights."""

  def __init__(self):
    pass

  def set_parameters(self, sdca_model, train_op):
    self._sdca_model = sdca_model
    self._train_op = train_op

  def begin(self):
    """Construct the update_weights op.

    The op is implicitly added to the default graph.
    """
    self._update_op = self._sdca_model.update_weights(self._train_op)

  def before_run(self, run_context):
    """Return the update_weights op so that it is executed during this run."""
    return session_run_hook.SessionRunArgs(self._update_op)

class _SDCAEstimator(estimator.Estimator):
  """Base estimator class for linear models using the SDCA optimizer.

  This class should not be used directly. Rather, users should call one of the
  derived estimators.
  """

  def __init__(self,
               example_id_column,
               feature_columns,
               weight_column_name=None,
               model_dir=None,
               head=None,
               l1_regularization=0.0,
               l2_regularization=1.0,
               num_loss_partitions=None,
               config=None,
               feature_engineering_fn=None):
    """Construct a `_SDCAEstimator` estimator object.

    Args:
      example_id_column: A string defining the feature column name representing
        example ids. Used to initialize the underlying SDCA optimizer.
      feature_columns: An iterable containing all the feature columns used by
        the model. All items in the set should be instances of classes derived
        from `FeatureColumn`.
      weight_column_name: A string defining feature column name representing
        weights. It is used to down weight or boost examples during training. It
        will be multiplied by the loss of the example.
      model_dir: Directory to save model parameters, graph etc. This can also be
        used to load checkpoints from the directory into an estimator to
        continue  training a previously saved model.
      head: type of head. Currently, _BinaryLogisticHead and _BinarySvmHead are
        supported for classification and _RegressionHead for regression. It
        should be a subclass of _SingleHead.
      l1_regularization: L1-regularization parameter. Refers to global L1
        regularization (across all examples).
      l2_regularization: L2-regularization parameter. Refers to global L2
        regularization (across all examples).
      num_loss_partitions: number of partitions of the (global) loss function
        optimized by the underlying optimizer (SDCAOptimizer).
      config: `RunConfig` object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
        labels which are the output of `input_fn` and returns features and
        labels which will be fed into the model.

    Returns:
      A `_SDCAEstimator` estimator.

    Raises:
      ValueError: if head is not supported by SDCA.
    """
    self._feature_columns = tuple(feature_columns or [])
    assert self._feature_columns

    if not _head_is_valid_for_sdca(head):
      raise ValueError(
          "head type: {} is not supported. Supported head types: "
          "_BinaryLogisticHead, _BinarySvmHead and _RegressionHead.".format(
              type(head)))
    assert head.logits_dimension == 1

    params = {
        "head": head,
        "feature_columns": feature_columns,
        "example_id_column": example_id_column,
        "num_loss_partitions": num_loss_partitions,
        "l1_regularization": l1_regularization,
        "l2_regularization": l2_regularization,
        "weight_column_name": weight_column_name,
        "update_weights_hook": _SdcaUpdateWeightsHook(),
    }

    super(_SDCAEstimator, self).__init__(
        model_fn=sdca_model_fn,
        model_dir=model_dir,
        config=config,
        params=params,
        feature_engineering_fn=feature_engineering_fn)

class SDCALogisticClassifier(_SDCAEstimator):
  """Logistic regression binary classifier using the SDCA optimizer.

  Example usage:

  ```python
  sparse_column_a = sparse_column_with_hash_bucket(...)
  sparse_column_b = sparse_column_with_hash_bucket(...)

  sparse_feature_a_x_sparse_feature_b = crossed_column(...)

  classifier = SDCALogisticClassifier(
      example_id_column='example_id',
      feature_columns=[sparse_column_a, sparse_feature_a_x_sparse_feature_b]),
      weight_column_name=...,
      l2_regularization=...,
      num_loss_partitions=...,
  )

  # Input builders
  # returns x, y (where y is the label Tensor (with 0/1 values)
  def input_fn_{train, eval}:

  # returns x (features dict)
  def input_fn_test:
    ...
  classifier.fit(input_fn=input_fn_train)
  classifier.evaluate(input_fn=input_fn_eval)
  # Returns predicted classes.
  classifier.predict_classes(input_fn=input_fn_test)
  # Returns predicted probabilities.
  classifier.predict_proba(input_fn=input_fn_test)
  ```

  The input_fn provided to `fit`, `evaluate` and predict_* methods should return
  the following features, otherwise there  will be a `KeyError`:
    * A feature with `key=example_id_column` whose value is a `Tensor` of dtype
      string.
    * If `weight_column_name` is not `None`, a feature with
      `key=weight_column_name` whose value is a `Tensor`.
    * For each `column` in `feature_columns`:
      - if `column` is a `SparseColumn`, a feature with `key=column.name` whose
        `value` is a `SparseTensor`
      - if `column` is a `RealValuedColumn, a feature with `key=column.name`
        whose `value` is a `Tensor`
      - if `column` is a `WeightedSparseColumn`, two features: the first with
        `key` the id column name, the second with `key` the weight column name.
        Both features' `value` must be a `SparseTensor`
  """

  def __init__(self,
               example_id_column,
               feature_columns,
               weight_column_name=None,
               model_dir=None,
               l1_regularization=0.0,
               l2_regularization=1.0,
               num_loss_partitions=None,
               config=None,
               feature_engineering_fn=None):
    """Construct a `SDCALogisticClassifier` object.

    Args:
      example_id_column: A string defining the feature column name representing
        example ids. Used to initialize the underlying SDCA optimizer.
      feature_columns: An iterable containing all the feature columns used by
        the model. All items in the iterable should derive from `FeatureColumn`.
        Note that the order of the items is ignored at model construction time.
      weight_column_name: A string defining feature column name representing
        weights. It is used to downweight or boost examples during training. It
        will be multiplied by the loss of the example.
      model_dir: Directory to save model parameters, graph etc. This can also be
        used to load checkpoints from the directory into an estimator to
        continue training a previously saved model.
      l1_regularization: L1-regularization parameter. Refers to global L1
        regularization (across all examples).
      l2_regularization: L2-regularization parameter. Refers to global L2
        regularization (across all examples).
      num_loss_partitions: Number of partitions of the global loss function
        optimized by the underlying optimizer (SDCAOptimizer).
      config: `RunConfig` object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
        labels which are the output of `input_fn` and returns features and
        labels which will be fed into the model.

    Returns:
      A `SDCALogisiticClassifier` estimator.
    """
    super(SDCALogisticClassifier, self).__init__(
        example_id_column=example_id_column,
        feature_columns=feature_columns,
        weight_column_name=weight_column_name,
        model_dir=model_dir,
        head=head_lib.multi_class_head(
            n_classes=2, weight_column_name=weight_column_name),
        l1_regularization=l1_regularization,
        l2_regularization=l2_regularization,
        num_loss_partitions=num_loss_partitions,
        config=config,
        feature_engineering_fn=None)

  def predict_classes(self, input_fn=None):
    """Runs inference to determine the predicted class.

    Args:
      input_fn: The input function providing features.

    Returns:
      A generator of predicted classes for the features provided by input_fn.
    """
    key = prediction_key.PredictionKey.CLASSES
    predictions = super(SDCALogisticClassifier, self).predict(
        input_fn=input_fn, outputs=[key])
    return (pred[key] for pred in predictions)

  def predict_proba(self, input_fn=None):
    """Runs inference to determine the class probability predictions.

    Args:
      input_fn: The input function providing features.

    Returns:
      A generator of predicted class probabilities for the features provided by
        input_fn.
    """
    key = prediction_key.PredictionKey.PROBABILITIES
    predictions = super(SDCALogisticClassifier, self).predict(
        input_fn=input_fn, outputs=[key])
    return (pred[key] for pred in predictions)

class SDCALinearRegressor(_SDCAEstimator):
  """Linear regression model using SDCA to solve the underlying optimization.

  Example usage:

  ```python
  real_column_a = real_valued_column(...)
  sparse_column_b = sparse_column_with_hash_bucket(...)

  regressor = SDCALinearRegressor(
      example_id_column='example_id',
      feature_columns=[real_column_a, sparse_column_b]),
      weight_column_name=...,
      l2_regularization=...,
      num_loss_partitions=...,
  )

  # Input builders
  # returns x, y (where y is the label Tensor (with 0/1 values)
  def input_fn_{train, eval}:

  # returns x (features dict)
  def input_fn_test:
    ...
  regressor.fit(input_fn=input_fn_train)
  regressor.evaluate(input_fn=input_fn_eval)
  regressor.predict_scores(input_fn=input_fn_test) # returns predicted scores.
  ```

  The input_fn provided to `fit`, `evaluate` and predict_* methods should return
  the following features, otherwise there  will be a `KeyError`:
    * A feature with `key=example_id_column` whose value is a `Tensor` of dtype
      string.
    * If `weight_column_name` is not `None`, a feature with
      `key=weight_column_name` whose value is a `Tensor`.
    * For each `column` in `feature_columns`:
      - if `column` is a `SparseColumn`, a feature with `key=column.name` whose
        `value` is a `SparseTensor`
      - if `column` is a `RealValuedColumn, a feature with `key=column.name`
        whose `value` is a `Tensor`
      - if `column` is a `WeightedSparseColumn`, two features: the first with
        `key` the id column name, the second with `key` the weight column name.
        Both features' `value` must be a `SparseTensor`

  """

  def __init__(self,
               example_id_column,
               feature_columns,
               weight_column_name=None,
               model_dir=None,
               l1_regularization=0.0,
               l2_regularization=1.0,
               num_loss_partitions=None,
               config=None,
               feature_engineering_fn=None):
    """Construct a `SDCALinearRegressor` estimator object.

    Args:
      example_id_column: A string defining the feature column name representing
        example ids. Used to initialize the underlying SDCA optimizer.
      feature_columns: An iterable containing all the feature columns used by
        the model. All items in the iterable should derive from `FeatureColumn`.
        Note that the order of the items is ignored at model construction time.
      weight_column_name: A string defining feature column name representing
        weights. It is used to down weight or boost examples during training. It
        will be multiplied by the loss of the example.
      model_dir: Directory to save model parameters, graph etc. This can also be
        used to load checkpoints from the directory into an estimator to
        continue  training a previously saved model.
      l1_regularization: L1-regularization parameter. Refers to global L1
        regularization (across all examples).
      l2_regularization: L2-regularization parameter. Refers to global L2
        regularization (across all examples).
      num_loss_partitions: number of partitions of the (global) loss function
        optimized by the underlying optimizer (SDCAOptimizer).
      config: `RunConfig` object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
        labels which are the output of `input_fn` and returns features and
        labels which will be fed into the model.

    Returns:
      A `SDCALinearRegressor` estimator.
    """
    super(SDCALinearRegressor, self).__init__(
        example_id_column=example_id_column,
        feature_columns=feature_columns,
        weight_column_name=weight_column_name,
        model_dir=model_dir,
        head=head_lib.regression_head(weight_column_name=weight_column_name),
        l1_regularization=l1_regularization,
        l2_regularization=l2_regularization,
        num_loss_partitions=num_loss_partitions,
        config=config,
        feature_engineering_fn=None)

  def predict_scores(self, input_fn):
    """Returns predicted scores for given features.

    Args:
      input_fn: The input function providing features.

    Returns:
      A generator of predicted scores for the features provided by input_fn.
    """
    key = prediction_key.PredictionKey.SCORES
    predictions = super(SDCALinearRegressor, self).predict(
        input_fn=input_fn, outputs=[key])
    return (pred[key] for pred in predictions)


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Tag: darwinzon hernandez
Darwinzon Hernandez Struggles in First Start, Alex Cora and Andrew Benintendi Get Tossed as Red Sox Fall Back to .500 in 9-5 Loss to Rangers
After blowing a late lead to open up a four-game series against the Texas Rangers on Monday, the Red Sox fell behind early Tuesday and could never really recover, as they dropped their third straight contest to fall back to .500 on the season in a 9-5 loss.
Making his first career start and second appearance for Boston was top pitching prospect Darwinzon Hernandez, who was recalled from Double-A Pawtucket earlier Tuesday.
Last working as a reliever in his first stint with the club back in April, the left-hander surrendered four runs, three of which were earned, on three hits and five walks to go along with seven strikeouts on the night.
Despite fanning 77% of the nine hitters he faced, control remained Hernandez's biggest issue in this one, as it has been in his time with the Sea Dogs.
From the jump, the Venezuela native dazzled, punching out the side in the first while also leading off the second with his fourth K.
The trouble began with a one-out, seven-pitch walk of Hunter Pence, which was followed with a four-pitch walk of Asdrubal Cabrera to give the Rangers their first two baserunners.
Two pitches later, Rougned Odor drove in his team's first run by ripping an RBI ground-rule double over Mookie Betts' head in right field, which came at the benefit of the Red Sox with Cabrera being held up at third.
In the third, the free pass bit Hernandez yet again, this time with three of the first five Rangers hitters to come to the plate in the inning reaching base by way of the BB, loading the bases for Cabrera.
After falling behind 3-0 and battling back to fill the count, Cabrera ultimately won his second battle against the young southpaw, as he made it a 3-1 contest with a two-run single to center.
Hernandez would fan Odor for his seventh and final strikeout, and proceeded to allow the first two Rangers he faced to reach in the fourth with the help of a Rafael Devers fielding error before getting the hook from Sox manager Alex Cora.
Finishing with a final pitch count of 86 (42 strikes), the 22-year-old hurler turned to his four-seam fastball nearly 76% of the time he was on the mound Tuesday, inducing seven swings and misses while also topping out at 97.7 MPH with the pitch while Christian Vazquez was behind the plate.
pic.twitter.com/d0Kr9KOKLE
— Red Sox Stats (@redsoxstats) June 11, 2019
It's never been a question about Hernandez's stuff, that is certainly there. It's the control that's the issue, and for whatever reason, he just folded after recording the first out of the second inning.
Can't say for sure that Hernandez will make another start, but if he does, it will most likely come against the Baltimore Orioles on Sunday.
In relief of Hernandez, Colten Brewer came on in that fourth inning, yielded a six-pitch walk to the first man he saw to fill the bases, and officially closed the book on Hernandez's first big league start by giving up a sacrifice fly to Danny Santana to make it a 4-3 game.
From there, after Brewer escaped the fourth with back-to-back punchouts, Bobby Poyner came on for his first appearance since being recalled from Triple-A Pawtucket on Monday, and he, at the very least, ate some innings.
To put it bluntly, the left-hander entered with his team trailing by one run, and left with them down by six, with those last two runs coming on a two-run inside-the-park home run off the bat of Hunter Pence that was just out of Brock Holt's reach in the top half of the sixth.
lol pic.twitter.com/0T4U5QQXDp
— Texas Rangers (@Rangers) June 12, 2019
And finally, Mike Shawaryn continued to impress out of the bullpen by fanning four Rangers in two perfect frames of relief to wrap up what was another underwhelming night from Red Sox pitching.
On the other side of things, the Red Sox lineup was matched up against Rangers right-hander Ariel Jurado, who had never pitched against Boston nor at Fenway Park before Tuesday.
Already down a run by the middle of the second, Xander Bogaerts answered back and got his side on the board by blasting his 13th home run of the season, this one a 422 foot shot over the Green Monster.
Way back, way gone. pic.twitter.com/bQThY6tNsq
— Boston Red Sox (@RedSox) June 11, 2019
An inning later and trailing by a pair this time, Rafael Devers erased that deficit quickly, following by back-to-back two-out walks with a two-run triple off Jurado to break out of an 0-for-20 slump. He was stranded at third.
Shown here: a very solid piece of hitting by Rafael Devers. pic.twitter.com/siM6Kv7uoX
Fast forward to the bottom of the fifth, and some drama arose when Andrew Benintendi had a few choice words for home plate umpire Angel Hernandez following a groundout to short.
The thing was, Hernandez couldn't hear Benintendi's words, but first base umpire Vic Carapazza could, and without giving a warning, ejected the Red Sox outfielder as he was heading back towards his dugout.
That led to even more pandemonium, and ultimately resulted in Cora's ejection and more colorful language from Benintendi.
Once all was settled, the Sox still trailed by three runs going into the sixth, and that deficit did not shrink.
A Michael Chavis leadoff double in the seventh off new Rangers reliever Jose LeClerc, followed by an RBI two-bagger from JD Martinez two outs later gave Boston their fourth run of the night.
And in the ninth, Mookie Betts swung at the very first pitch he saw from right-hander Chris Martin, and came away with his 11th dinger of the year, although it didn't make much of a difference in what would go down as a 9-5 loss for the Red Sox.
Mookie Betts! pic.twitter.com/aO4N6bywF5
— Brendan (@brendan_camp) June 12, 2019
Some notes from this loss:
The Red Sox are 34-34 on the season and 5-6 in June.
Mike Shawaryn is averaging 16.5 strikeouts per nine innings.
JD Martinez's last two games since returning from back spasms: 4-for-7, two doubles, one run scored, one RBI.
Andrew Benintendi on his ejection:
Benintendi said he screamed "You suck" and didn't think Angel Hernandez had heard him. Was surprised by ejection.
— Chris Cotillo (@ChrisCotillo) June 12, 2019
Alex Cora on his:
Cora didn't agree with Benintendi being tossed because he was walking toward dugout. "I've got to go out there and defend my player."
Brock Holt, who was in right field because of the Benintendi ejection, on what happened on the inside-the-park homer:
Holt said he assumed Pence's ball was a homer. "That's kind of embarrassing. That one is on me."
It was a bizarre night, really. Both managers got ejected, the Red Sox didn't use one pitcher who appeared in a big league game before the start of the 2018 season, and Mookie Betts had himself a rough time of things in center field.
Next up for the Sox, it's the third game of this four-game set, which was moved up three hours from its original start time because of Game 7 of the Stanley Cup Finals.
Right-hander Rick Porcello, will be getting the ball for Boston, while fellow righty Lance Lynn will be doing the same for Texas.
In his career against the Rangers, Porcello (4-6, 4.86 ERA) owns a lifetime 5.16 ERA over 12 prior starts and 68 total innings pitched.
Lynn (7-4, 4.39 ERA), meanwhile, has posted a career 2.40 ERA in three previous appearances (two starts) and 15 innings of work at Fenway Park.
First pitch Wednesday is scheduled for 4:05 PM EDT on NESN. Red Sox need to wake up.
Author Brendan CampbellPosted on June 12, 2019 Categories Baseball, Sports, UncategorizedTags alex cora, andrew benintendi, angel hernandez, ariel jurado, Baseball, bobby poyner, boston, brock holt, chris woodward, christian vazquez, colten brewer, darwinzon hernandez, jd martinez, marco hernandez, michael chavis, mike shawaryn, mlb, mookie betts, rafael devers, rangers, red sox, texas, xander bogaertsLeave a comment on Darwinzon Hernandez Struggles in First Start, Alex Cora and Andrew Benintendi Get Tossed as Red Sox Fall Back to .500 in 9-5 Loss to Rangers
Red Sox Recall Top Pitching Prospect Darwinzon Hernandez from Double-A Portland, Place Ryan Brasier on Bereavement List
Prior to their second of four games against the Texas Rangers on Tuesday, the Red Sox placed right-handed reliever Ryan Brasier on the bereavement/family medical emergency list and recalled left-hander Darwinzon Hernandez from Double-A Portland in a corresponding move. The club made the transaction official earlier Tuesday.
The #RedSox today placed RHP Ryan Brasier on the bereavement/family medical emergency list.
To fill Brasier's spot on the 25-man roster, the club recalled LHP Darwinzon Hernandez from Double-A Portland.
Now up with Boston for the third time this season, Hernandez will make his first career big league start Tuesday after only being used out of the bullpen in Game 2 of a doubleheader against the Detroit Tigers back in April, where he tossed 2 1/3 innings of scoreless relief.
Ranked as the organization's top pitching prospect, the 22-year-old has not had an easy time of things with the Sea Dogs this year, as he has posted a 5.13 ERA in 10 outings (nine starts) and 40 1/3 total innings pitched to go along with 59 strikeouts and 32 walks. That's good for a K/9 of 13.2 and a BB/9 of 7.2. He is also holding opposing hitters to a .217 batting average against.
Hernandez since being sent back to AA after his debut: 30 IP, 25 H, 25 BB, 3 HBP, 5 WP, 2 HR, 44 K. Should be an eventful night!
As the above tweet indicates, control has been Hernandez's biggest issue. How he fares against major league hitters multiple times through the order should be interesting to see.
Here's how the rest of the Red Sox line up on Tuesday against Texas. First pitch is scheduled for 7:10 PM EDT.
Author Brendan CampbellPosted on June 11, 2019 June 11, 2019 Categories Baseball, Sports, UncategorizedTags alex cora, Baseball, boston, bullpen, darwinzon hernandez, dave dombrowski, mlb, red sox, ryan brasier, starting rotationLeave a comment on Red Sox Recall Top Pitching Prospect Darwinzon Hernandez from Double-A Portland, Place Ryan Brasier on Bereavement List
#RedSox Recall Top Pitching Prospect Darwinzon Hernandez from Double-A Portland, Option Colten Brewer to Triple-A Pawtucket
Before taking on the Houston Astros for the last time this regular season, the Red Sox have recalled top pitching prospect Darwinzon Hernandez from Double-A Portland while also optioning right-handed reliever Colten Brewer to Triple-A Pawtucket in a corresponding move. The club made the roster move official earlier Sunday morning.
Prior to today's game, the #RedSox recalled LHP Darwinzon Hernandez from Double-A Portland.
To make room for Hernandez on the 25-man roster, the club optioned RHP Colten Brewer to Triple-A Pawtucket following last night's game.
— Boston Red Sox (@RedSox) May 26, 2019
This will mark Hernandez's second stint with the Red Sox, with the first coming back on April 23rd where the left-hander worked 2.1 scoreless innings of relief serving as the club's 26th man in the second game of a day-night doubleheader against the Detroit Tigers.
Ranked as Boston's fourth-best prospect overall per MLB.com, the 22-year-old has posted a 4.65 ERA in seven appearances (six starts) and 31 innings pitched with the Sea Dogs so far in 2019. He also owns a K/9 of 12.8 while holding opposing hitters to a .202 clip.
As MassLive.com's Chris Cotillo notes, Hernandez's stay with the Sox might be short-lived with utilitu man Brock Holt due to be activated from the 10-day injured list on Monday.
Brewer, meanwhile, will head to the PawSox after making his first big league Opening Day roster with Boston back in March.
Acquired from the San Diego Padres last November, the 26-year-old hurler has posted a 5.32 ERA over 20 games and 22 innings pitched so far this season, including 2.1 scoreless frames of relief Saturday against the Astros.
Eduardo Rodriguez is set to start for the Red Sox Sunday afternoon. First pitch is scheduled for 2:10 PM EDT on NESN.
Author Brendan CampbellPosted on May 26, 2019 Categories Baseball, Sports, UncategorizedTags astros, Baseball, boston, colten brewer, darwinzon hernandez, eduardo rodriguez, houston, mlb, red soxLeave a comment on #RedSox Recall Top Pitching Prospect Darwinzon Hernandez from Double-A Portland, Option Colten Brewer to Triple-A Pawtucket
Top #RedSox Pitching Prospect Darwinzon Hernandez Racks up Career-High 11 Strikeouts for Double-A Portland
While the the Red Sox were enjoying an off day on Thursday, the club's top pitching prospect, Darwinzon Hernandez, put together a career night in his start for Double-A Portland.
Making his sixth start of the season for the Sea Dogs, the left-hander surrendered four runs, three of which were earned, on five hits and two walks to go along with a career-best 11 strikeouts over a career-best seven innings of work against the Reading Fightin Phils Thursday.
Darwinzon Hernandez sets career-highs with 11 strikeouts and 7 IP.
— Mike Antonellis (@seadogsradio) May 17, 2019
Outside of a rough third inning in which all four of those initial Reading runs came around to score, Hernandez did not yield a single hit from the first until the start of the middle of the third, and then again from the middle of the fourth until the conclusion of his seventh and final frame.
In that seventh inning, the 23-year-old hurler capped off his outing on a high note by fanning four Fightin Phils thanks to a wild pitch that allowed one hitter to reach first on his own part.
The Sea Dogs eventually dropped this contest by a final score of 8-2, and Hernandez was thus hit with his third losing decision of the year.
Ranked as Boston's third-best prospect overall, we have already gotten a sneak peek of what Hernandez could bring to the table at the big league level earlier last month, where in the second of a day-night doubleheader against the Detroit Tigers, the southpaw scattered four hits and one walk while punching out four over 2.1 innings of relief.
He served as the club's 26th man for the second game of that doubleheader, and was sent back to Double-A Portland right after.
Through the six starts made with the Sea Dogs in 2019, Hernandez has posted an ERA of 4.10 and a batting average against of .198 in addition to 36 strikeouts and 21 free passes over 26.1 total innings of work.
Out of spring training, the plan was to let the Venezuela native develop as a starting pitcher rather than rush him up in a relief role, both of which he has experience in.
As of now, I would say the Red Sox are sticking to this plan, so don't expect to see Hernandez up in Boston anytime soon barring any unforeseen circumstances.
Author Brendan CampbellPosted on May 17, 2019 Categories Baseball, Sports, UncategorizedTags alex cora, Baseball, boston, darwinzon hernandez, mlb, portland sea dogs, prospects, red soxLeave a comment on Top #RedSox Pitching Prospect Darwinzon Hernandez Racks up Career-High 11 Strikeouts for Double-A Portland
Top Pitching Prospect Darwinzon Hernandez Optioned to Double-A Portland in Third Round of #RedSox Roster Cuts
With Opening Day just five days away, the Red Sox made their third round of roster cuts on Saturday, reassigning two players to minor league camp and optioning three to the minors.
Outfielder Gorkys Hernandez and catcher Juan Centeno, both non-roster invites this spring, were the two reassigned to minor league camp. They will, however, remain with the big league club before they head off to Seattle.
Hernandez, 31, slashed .244/.279/.415 with two home runs and five RBI in 43 plate appearances this spring, while Centeno, 29, slashed .294/.368/.294 with three RBI in just 19 plate appearances. Both figure to start the 2019 season with Triple-A Pawtucket.
On the other side of things, the two surprising additions to Boston's 2018 Opening Day roster did not make the club out of the gate this year, as left-hander Bobby Poyner and right-hander Marcus Walden were both optioned to Triple-A Pawtucket.
Poyner, who is technically still a rookie, surrendered seven earned runs on 10 hits and three walks to go along with eight strikeouts over seven appearances and 9.2 total innings of work this spring.
Walden, meanwhile, had a much better time of things in his second spring with Boston, as he allowed four runs (two earned) on nine hits, four walks, and 13 strikeouts over seven appearances (one start) and 12.2 innings.
And finally, top pitching prospect Darwinzon Hernandez was optioned to Double-A Portland.
The 22-year-old southpaw really impressed this spring, posting a miniscule 0.82 ERA over six outings (two starts) and 11 frames pitched.
Despite the rumblings that Hernandez could potentially make the Red Sox' Opening Day roster as reliever, the club ultimately made the decision not to rush things.
Now, the native of Venezuela will have the chance to develop more while in Portland as a starter, presumably.
Following Saturday's transactions, the Red Sox' active roster now stands at 30 players.
PITCHERS (13): Matt Barnes, Ryan Brasier, Colten Brewer, Nathan Eovaldi, Heath Hembree, Brian Johnson, Rick Porcello, David Price, Eduardo Rodriguez, Chris Sale, Tyler Thornburg, Hector Velázquez, Brandon Workman
CATCHERS (3): Sandy León, Blake Swihart, Christian Vázquez
INFIELDERS (10): Xander Bogaerts, Rafael Devers, Marco Hernández, Brock Holt, Tzu-Wei Lin, Mitch Moreland, Eduardo Núñez, Steve Pearce, Dustin Pedroia, Sam Travis
OUTFIELDERS (4): Andrew Benintendi, Mookie Betts, Jackie Bradley Jr., J.D. Martinez
Assuming that Hernandez and Pedroia start the season on the injured list, while Lin and Travis are optioned, and Leon is either traded or designated for assignment, the Red Sox' 25-man Opening Day roster is essentially set.
Author Brendan CampbellPosted on March 23, 2019 Categories Baseball, Sports, UncategorizedTags Baseball, bobby poyner, boston, darwinzon hernandez, gorkys hernandez, marcus walden, mlb, red sox, roster, sandy leon, spring trainingLeave a comment on Top Pitching Prospect Darwinzon Hernandez Optioned to Double-A Portland in Third Round of #RedSox Roster Cuts
Chris Sale Fans Seven and Mookie Betts Homers as #RedSox Bounce Back with 6-1 Victory over Braves
The Red Sox improved to 7-13-1 in Grapefruit League play on Saturday afternoon following a 6-1 victory over the Atlanta Braves at JetBlue Park, marking their first win since March 5th.
After tossing three innings in a minor league contest this past Monday, Chris Sale finally made his 2019 Grapefruit League debut in this one.
With a pitch limit set at no more than 65, the ace left-hander held Atlanta scoreless while scattering two hits and striking out seven, five of which came consecutively, in four quality frames of work. He did not walk a single batter.
Chris Sale looks ready. pic.twitter.com/MYheJ8xxvp
— Boston Red Sox (@RedSox) March 16, 2019
Finishing with a final pitch count of exactly 50 before heading out to finish his day in the Red Sox bullpen, Sale topped out at 93 MPH with his fastball and also mixed in a fair amount of changeups and sliders.
From the beginning of the fifth inning on, we got a significant glimpse at what could potentially be the back-end of the Red Sox bullpen in 2019, as Matt Barnes, Ryan Brasier, Tyler Thornburg, and Darwinzon Hernandez combined to surrender only one run on five hits, one walk, and five strikeouts over the final five innings on Saturday.
Like Sale, Brasier also made his 2019 Grapefruit League debut against the Braves.
Previously dealing with a toe infection earlier in the spring, the 2018 standout reliever struck out one while also allowing that lone Atlanta run to cross the plate in their half of the sixth.
Thornburg, meanwhile, finally put together his first shutout performance of the year, as he retired three of the four hitters he faced in a solid seventh inning of relief.
On the other side of things, the Red Sox lineup was matched up against Braves number-two prospect Kyle Wright to start things off on Saturday.
As he typically does, Mookie Betts got the scoring started for Boston by lacing a one out first inning single off Wright.
One Mitch Moreland double later, and JD Martinez collected his first RBI of the spring on a sac-fly to right.
That plated Betts and advanced Moreland to third, who would then come around to score himself on an RBI double off the bat of Xander Bogaerts to give the Red Sox an early two-run advantage.
Fast forward all the way to the sixth, and Betts struck again by blasting his first home run of the year off Braves reliever Shane Carle, a monumental solo shot over the JetBlue Park Green Monster.
LAUNCHED. 🚀 pic.twitter.com/HdQLDZgzgk
An inning later, Brock Holt would turn out to be the catalyst of a three-run frame for Boston by drawing a leadoff walk off Luke Jackson.
Two back-to-back RBI doubles from Eduardo Nunez and Jackie Bradley Jr. later, the Red sox found themselves up 5-1.
After stealing third, Bradley would come in to score on a Chad De La Guerra sacrifice fly, which would plate the club's sixth and final run of the day, as 6-1 would go on to be your final score in this one.
Some notes from this win:
Eduardo Nunez had himself a nice day at the plate with that RBI double. He also flashed the leather at third on a hard-hit ground ball from Adam Duvall in the fourth inning.
Hot corner. 🔥 pic.twitter.com/NcYgYuM2VA
Darwinzon Hernandez, who was responsible for the final two innings on Saturday, briefly received a visit from Alex Cora and a trainer in the eighth, but remained in the game to close things out.
In his third game back, Dustin Pedroia went 0/3 out of the leadoff spot. He started the first five innings at second base.
Next up for the Red Sox, it will be some split-squad action against both the Tampa Bay Rays and Pittsburgh Pirates on Sunday.
One group will remain in Fort Myers to host the Rays with David Price slated to start, while the other group will head to Bradenton to face the Pirates, with Marcus Walden getting the start there.
Right-hander Jameson Taillon is listed as Sunday's starter for Pittsburgh, while Tampa Bay will be going with lefty Ryan Merritt.
First pitch for both contests is scheduled for 1:05 PM EDT. NESN has the Rays game covered.
Also, here's what Red Sox manager Alex Cora said following Saturday's win:
Cora on end of 9-game winless streak: 'We're back'
— Alex Speier (@alexspeier) March 16, 2019
Author Brendan CampbellPosted on March 16, 2019 Categories Baseball, Sports, UncategorizedTags alex cora, atlanta, Baseball, boston, braves, chris sale, darwinzon hernandez, dustin pedroia, eduardo nunez, jackie bradley jr, jd martinez, matt barnes, mlb, mookie betts, red sox, ryan brasier, spring training, tyler thornburg, xander bogaertsLeave a comment on Chris Sale Fans Seven and Mookie Betts Homers as #RedSox Bounce Back with 6-1 Victory over Braves
David Price Fans Four in Spring Debut as #RedSox Drop Sixth Straight
After an off day on Monday, the Red Sox fell to 6-11 in Grapefruit League play on Tuesday following a 4-3 loss to the Detroit Tigers at JetBlue Park, marking their sixth straight defeat.
Like Rick Porcello did on Sunday, 2018 postseason hero David Price made his 2019 debut in this one, getting the start against one of his former teams.
Working the first three innings, the left-hander surrendered two runs on a pair of hits and walks to go along with four strikeouts in his first in-game action of the spring.
Both of those Detroit runs came in the top of the third, when with two outs and a runner on first, ex-Pirates shortstop Jordy Mercer blasted his second home run of the spring to give his new club a two-run advantage.
Other than that one blunder, it was relatively smooth sailing for Price, as he faced three over the minimum nine hitters.
My anticipation for today has ate at me since I walked the last #dodger I faced last year. Im officially over that walk after getting back out there…#letsgooo
— David Price (@DAVIDprice24) March 12, 2019
In relief of Price, Matt Barnes, Bobby Poyner, Darwinzon Hernandez, Marcus Walden, Erasmo Ramirez, and Jenrry Mejia combined to give up an additional two runs on three hits, six walks, one HBP, and six strikeouts over the final six frames.
Barnes, who looks poised to be Boston's closer to start out the 2019 campaign, bounced back from a rough spring debut Saturday and escaped a fourth inning bases loaded jam while holding Detroit scoreless on Tuesday.
Hernandez, meanwhile, had his scoreless appearances streak come to an end in this one, as he allowed Daz Cameron to score on a Kody Eaves two out RBI double in the sixth.
Finally, to close things out, Mejia sat down all but one of the four Detroit hitters he faced in a shutout ninth inning to keep Boston's deficit at three.
On the other side of things, the Red Sox lineup, which featured the likes of Andrew Benintendi, Mookie Betts, Rafael Devers, JD Martinez, Xander Bogaerts, Mitch Moreland, Dustin Pedroia, Jackie Bradley Jr., and Christian Vazquez was held in check yet again.
Matched up against Tigers left-hander Daniel Norris to start things off, Boston was limited to just three runs on the day, which is actually the most they have scored in a game since last Tuesday.
In fact, all three of those runs were plated in the Red Sox' half of the ninth.
CJ Chatham got the late rally started by drawing a leadoff walk against Tigers reliever Kyle Funkhouser, who then proceeded to hit Sam Travis with a pitch to put runners on first and second.
Mike Miller followed that up by driving in Chatham on an RBI single up the middle, which in turn led to a Detroit pitching change.
With Jose Manuel Fernandez now on the mound for Detroit and one out in the inning, Blake Swihart pulled his club one run closer by lacing an RBI double left to score Travis and advance Miller to third, who would then come around to plate the third run of the frame on a Gorkys Hernandez RBI groundout.
Unfortunately, a Bryce Brentz strikeout would put an end to any potential comeback effort, and 4-3 would be your final score on Tuesday in favor of Detroit.
When asked about Price's outing, Red Sox manager Alex Cora said, "David looked excellent. Velocity. Command. Presence on the mound."
When asked about it himself, Price responded with a bit of self-criticism.
"To be honest, that homer is not what bothers me the most," the southpaw said following his start. "It's the two two-out walks that I had. Gave up that homer and then went 2-0 on the next hitter and then was able to bounce back and throw quality strikes and get that third out. First time back out there in however many months, I'm okay with it."
On the field, Dustin Pedroia went 1/2 with a single in his second game back, while Blake Swihart accounted for Boston's lone extra-base hit on Tuesday with that ninth inning RBI two-bagger.
Next up for the Red Sox, they'll host the Minnesota Twins at JetBlue Park on Wednesday in the fifth installment of the 2019 Chairman's Cup.
Right-hander Jose Berrios will be getting the ball for Minnesota, while fellow righty Nathan Eovaldi will be making his 2019 debut as well as his first ever Grapefruit League start for Boston.
Looking to put an end to this six-game skid, first pitch Wednesday is scheduled for 1:05 ET at JetBlue. This game will not be televised.
Author Brendan CampbellPosted on March 12, 2019 Categories Baseball, Sports, UncategorizedTags Baseball, blake swihart, bobby poyner, boston, darwinzon hernandez, david price, detroit, erasmo ramirez, jd martinez, jenrry mejia, marcus walden, matt barnes, mlb, mookie betts, red sox, spring training, tigersLeave a comment on David Price Fans Four in Spring Debut as #RedSox Drop Sixth Straight

redpajama_set_name: RedPajamaCommonCrawl


Potatoes are very versatile, so making a dish with onions and mushrooms is easy, tasty and it can be served either as a main or a side dish!
I like recipes with potatoes, and this recipe is great combination of potatoes, onions and mushrooms. Very easy, quick and delicious meal which you prepare in the oven. This recipe requires a little effort and a little time for preparing. We like potatoes in many different ways, very easy and tasty, you can serve this recipe like main or side dish.
Potatoes are always a good idea, and if you add something you like with potatoes, that will be a real hit! My choice for today are onions and mushrooms, because my family likes that combination the most. When you don't know what to prepare for lunch, this easy recipe can save you in every occasion. Small things can make us happy, and that is why I decided for this recipe, because everyone can afford it! Enjoy!
Yes, adding cheese could be a great idea.. and we could call it "Skillet Dinner". Thanks for sharing some ideas, Julia.
Do you start to bake it in layers like the photos show and then mix it at the last 15 mins of cooking?
Yes Nancy, you need to start baking in layers and mix it at the last 15 minutes of cooking. Enjoy!
Hi Dawn, I use olive oil to grease the glass dish.
Looks yummy… Thanks for sharing!! Do you cover the dish for baking?
Hi Dee, you can cover the dish if you want. Enjoy!
Hi Jan, you need to use oil to grease the dish. Good luck!
Hi Nissa, you need to bake this in pre-heated oven for 15 minutes until the potatoes are soft and most of the liquid has been absorbed, then you need to mix all the ingredients together and bake for 15 minutes more. Good luck and enjoy!
Hi Dani, it's about 3 lb. Enjoy!
Hi Wilf, Glad everything worked out great for you! Yes, adding some thyme, onion powder, oregano or your own seasoning could be a great idea.. Thanks for sharing some ideas.
Hi, What size dish to use for this ??
Hi Loretta, you can use a 9×13 baking dish or Oval Dish 13.3 x 7.1 x 2.2 inches. Enjoy!


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redpajama_set_name: RedPajamaGithub


Boje Estermann is educated within marketing management. After working several years in the private sector he spent 8 years as CEO for a French company, so it was quite a career change, when he decided to enter the school of Design "ENSCI- Les Ateliers" in Paris. But he did it and graduated in 1999. In 2000, he founded the design studio "request", which later became "Boje Estermann Design Studio", where he works with industrial, product and furniture design.
"I usually design products by analyzing my own desires. My ambition is to create designs which are both practical and discreet and still offers you that little extra. I try to give them a personal touch without losing the functionality. It very well reflects my Scandinavian origins with a design tradition based on functionality, combined with a Latin influence from living 20 years in France, which touches my senses. I would love to create a home where all functional things only appear when needed. The rest should be calm and pleasure. But as long as I can make products and interiors that lead in that direction, I'll be happy".


text:
When and how to report a claim
COVID-19 Guidance for general practice
From 1 April 2019, NHS Resolution is operating a new state indemnity scheme for general practice in England called the Clinical Negligence Scheme for General Practice (CNSGP). The scheme covers clinical negligence liabilities arising in general practice in relation to incidents that occurred on or after 1 April 2019. CNSGP provides a fully comprehensive indemnity for all claims within its scope.
Scheme frequently asked questions
+ As a healthcare professional working in general practice am I required to notify NHS Resolution in the event conditions are imposed on my licence to practice?
There is no blanket requirement for you to notify NHS Resolution of conditions placed on your licence to practice. We may however request you to provide this information where you are involved in a claim notified to us and these details are reasonably required for the purposes of enabling NHS Resolution to effectively manage the claim on your behalf in accordance with the scheme rules.
In the event details of practice conditions are provided to NHS Resolution where we have not requested this information, we will respond to make it clear that we do not need to be notified and the information will be deleted.
If you are required by your MDO or other indemnifier to report such matters, you should continue to do so.
+ Is flu vaccination by practice staff to practice staff colleagues covered by the CNSGP?
Administration of an influenza vaccine by an organisation to employees, including peer-to-peer vaccination, is a provision of an occupational health service and therefore falls outside of the services provided under a primary medical services contract (GMS, PMS, APMS or Schedule 2L provisions under an NHS Standard Contract). For this reason, the administration of an influenza vaccine to practice staff by practice staff (peer-to-peer), falls outside the scope of the Clinical Negligence Scheme for General Practice (CNSGP), which only covers services provided under a primary medical services contract and, in specified circumstances, other NHS services provided by general practice to patients.
The main medical defence organisations (MDU, MDDUS and MPS) have indicated that they will provide indemnity cover for peer-to-peer administration of an influenza vaccine to general practice staff for the 2020/21 flu season. General practice staff administering the flu vaccine to other staff in the practice should liaise with their medical defence organisation or other indemnity provider for further information on the indemnity cover they provide for this service.
For further information and additional FAQs concerning flu vaccinations (and indemnity) in 2020/21, please see NHSEI guidance.
+ Does the CNSGP cover COVID-19 vaccinations?
Please refer to our COVID-19 Vaccinations NHS Staff Indemnity – FAQs, which are available from our COVID-19 Vaccinations webpage.
+ Who and what are covered by CNSGP?
All providers of NHS primary medical services are covered under CNSGP, including out of hours providers. The scheme extends to all GPs and others working for general practice who are carrying out activities in connection with the delivery of primary medical services – including salaried GPs, locums, students and trainees, nurses, clinical pharmacists, agency workers and other practice staff.
In addition to NHS primary medical services, any other NHS services provided by general practice are also covered under CNSGP (namely, NHS activities carried out by or for a provider whose principal activity is to provide NHS primary medical services). These 'other' NHS services are referred to in the regulations that establish CNSGP[1] as "ancillary health services". This means general practices are covered for all of their NHS services, including local authority commissioned public health services.
The place at which activities are carried out, the status of the person carrying out the activity, the form of the entity responsible for the provision of the NHS services in question and the individual circumstances of the patient concerned are not relevant to the scope of CNSGP. The question is whether the services provided are NHS primary medical services, and where they are not NHS primary medical services, whether they are NHS services provided by general practice [namely, by a provider whose principal activity is to provide NHS primary medical services]. This means that different kinds of organisations are covered under the scheme for activities they carry out which are in scope of the scheme.
Claims made against GP practices or other organisations providing NHS primary medical services or ancillary health services are covered under the scheme. This includes claims made in respect of liabilities that arise as a consequence of the acts or omissions of employees and others engaged to carry out activities connected to the provision of such services.
This scheme scope table outlines types of work carried out by general practice staff and whether or not it is covered by CNSGP.
The scheme applies to any liability in tort (civil wrongdoings which include clinical negligence) that arises as a consequence of a breach of a duty of care owed by a GP contractor[2] or GP sub-contractor[3] to a third party in connection with the provision of primary medical services or ancillary health services where –
(a) An act, or omission, on the part of the GP contractor/sub-contractor (or any employee or other person engaged by them) results in personal injury or loss to the third party;
(b) The act, or omission, is in connection with the diagnosis of an illness or the provision of care or treatment to the third party; and
(c) The act or omission occurs on or after 1 April 2019.
[1] The National Health Service (Clinical Negligence Scheme for General Practice) Regulations 2019.
[2] A GP contractor is a person or organisation who holds a general medical services (GMS), personal medical services (PMS) or alternative provider medical services (APMS) contract or agreement made under Part 4 of the National Health Service Act 2006 – i.e. GP contracts.
[3] A GP sub-contractor is a person or organisation that provides primary medical services under a sub-contract with a GP contractor (i.e. a primary medical services sub-contractor. It also includes a person providing ancillary health services under a sub-contract with a GP contractor or a primary medical services sub-contractor.
Have more questions? Read all the scheme FAQs
Documents for the Clinical Negligence Scheme for General Practice
Scheme documentation
This guidance has been prepared to support the beneficiaries of our schemes during the Coronavirus pandemic. It aims to clarify how beneficiaries' interactions with NHS Resolution concerning claims will proceed during the pandemic. It also provides answers to the questions we most often receive regarding …
Scheme scope
This document outlines types of work carried out by general practice staff and whether or not it is covered by the Clinical Negligence Scheme for General Practice (CNSGP).
CNSGP Scheme rules
The Clinical Negligence Scheme for General Practice (CNSGP) and Existing Liabilities Scheme for General Practice (ELSGP) scheme rules provide a general description of the schemes, the relevant liabilities, the benefits of the schemes, how disputes and appeals are resolved and how the schemes are implemented.
Summary of the CNSGP scheme rules
This summary of the Clinical Negligence Scheme for General Practice (CNSGP) scheme rules provide an introduction to the full scheme rules document.
These claims reporting guidelines will help beneficiaries of the Clinical Negligence Scheme for General Practice (CNSGP) know when and how to report a claim.
Responding to complaints
Effective local complaints handling is not only the right thing to do for patients but can help to prevent a concern escalating into a claim for compensation. This guidance provides some information on best practice.
How long patients have to bring a claim
Courts apply rules to the time period for bringing legal action, known as limitation. This guidance sets out how limitation applies in clinical negligence cases.
When compensation is due
Clinical negligence is complex, and finding out that a patient is bringing a claim against you can be stressful and confusing, it also can sometimes seem unclear as to whether something you have done or failed to do as a practitioner means that a patient …
What happens when a claim is brought
This guidance sets out some of the procedural steps that you may encounter as a claim progresses towards resolution.
Disclosing records
This document provides guidance on what you need to do when asked to disclose records for a compensation claim made against you.
General practice indemnity guidance for CNST members
This guidance aims to explain the changes that will come about following the introduction of the state backed indemnity scheme for general practice (Clinical Negligence Scheme for General Practice, CNSGP) and to provide reassurance to members of our Clinical Negligence Scheme for Trusts (CNST) that …
General practice indemnity guidance for claimant solicitors
This document provides guidance for claimant solicitors following the introduction of the state backed indemnity scheme for general practice (Clinical Negligence Scheme for General Practice, CNSGP).
Page last updated on: 15th October 2020
Email: cnsgp@resolution.nhs.uk


Located in Ronse, East-Flandria the Hotondcross which got his name from the Hotond-Arena has been the place of tough but thrilling races during the past. But in former years bad weather turned the race into a true mudwar quite often leading to fewer spectators than the organizers would have deserved. But this time the bright sunshine of a lovely autumn day set the scene for some decent and exciting races.
Slowly belgian cyclocross season is getting into full throttle. Last weekend in the dutch town of Gieten, the Superprestige was launches and now, as usual for a couple of years, the bpost bank Trofee emerged with the Grand Prix Mario de Clersq.
The young dutch rider of the Enertherm BKCP Team showed up as the superior contender and dominated the race right from the beginning. Immediately Dekker accompanied by his teammate Jappe Jaspers took the lead and both seperated themself from the remaining peloton. While the race continued, both riders extended their lead. Coming to the end, Dekker proved the he was the strongest and left his teammate behind, who managed to finished second without any danger. Seppe Rombouts completed the podium.
It looks as if Ronse is the place to be for Eli Iserbyt, he managed to get away with the winners trophy since 2012 without an interruption. The former belgian junior champion, who starts his first season in the U23 category cleared out from his contenders even before the first lap was done.
Some experts feared that he might have attacked too early to gain the victory. Most of them suspected that he would not be able to keep the pace until the end, especially since U23 races last ten minutes longe than the juniors races Iserbyt was used to.
But the young gun of Telenet Fidea Cycling Team proved the doubters wrong as he consistently paced in front of the race making nearly no mistakes during the technical difficult descends. At the end the race has lasted even a bit longer than 55 minutes when Iserbyt stretched his arms into the blue sky over Ronse to celebrate his first U23 victory. He crossed the finishing line three quarters of a minute ahead of Jonas Degroote who finished in second place shortly in front of Dan Hoeyberghs.
During this summer the relationship between Young Telenet Fidea Cycling Team and Pavla Havlikova surprisingly came to an end. But it seems as if for Havlikova the separation did not come in for bad.
Right from the start Havlikova proved that she came back to belgian ground in a really good shape. During the first few sections she left the leading role to her former teammate Nikki Harris but even before hitting the finishing straight for the first time she started her impressive solo ride. Due to her small size and weight Havlikova had to struggle a bit in the slippery descends but that did not stop the pleasant rider. Driving just as regular as a swiss clockwork, Havlikova kept her lead without beeing endangered at any time.
Belgian champion Sanne Cant, who was named as the main favorite by former world champion Niels Albert, was caught in a bad hair day. She started quite well, but had to realize she could not content on her normal level of performance. During the race she had to fight quite hard to keep her position.
Jolien Verschueren on the other hand started off badly, but managed to advance slowly but consistently while the race went on. With two laps to go, she managed to close up to Nikki Harris who was in second position at that time. Verschueren passed Harris and managed to run up to 15 Seconds behind Havlikova. Harris defended position three, while Sanne Cant manged to keep ahead of Ellen van Loy with her last ounce of strength and finished fourth 1:17 behind Havlikova.
Wout van Aert started into this opener of the bpost bank Trofee as the runaway favorite, as he managed to finish all four races of this season as the winner. Beeing last years winner of the series he could enter the starting grid wearing a golden bib number which was newly introduced this year. But who thought this race would become another easy win for him was wrong. It should become a hard day in the office for the vice world champion.
Lars van der Haar who won the cauberg cross on a simmilar track last year proved that he does like a parcours like this one on the flanks of the Hotond-Arena. So it was no surprise when the former dutch champion took the initiative and formed the first lead. Rapidly a bunch of four riders consisting of van der Haar, van Aert, Kevin Pauwels and Sven Nys determined the race.
Surprisingly van Aert did not seem to be as dominant as he was during the last races. Quite the contrary, it seemed as if he had to struggle a bit to keep up with the remaining three ones. It took time until the last lap unfolded that van Aert managed to attack. First Nys and van der Haar were seperated but Kevin Pauwels managed to keep the pace of van Aert. It took a last deep effort shortly before the end of the final lap to get rid of Kevin Pauwels and to gain the fifth victory in his fifth race. But Wout van Aert had to dig much deeper than ever before to make this happen. Lars van der Haar managed to gain the last spot on the podium in front of Sven Nys.
The fact that the gap between the winner and the fourth place was no more than11 seconds, even though Ronse is a difficult and strength-sapping race, prooves what a kind of thrilling and close race this years edition had been.


text:
Geometry and modeling of an active offshore thrust-related fold system: the Amendolara Ridge, Ionian Sea, southern Italy
On the Ionian Sea coast of southern Italy, spanning the transition from the Calabrian Arc to the Apennines, NE-directed motion of the thin-skinned frontal thrust belt of the Apennines toward the Apulian foreland reportedly ceased during the Early-Middle Pleistocene (PATACCA & SCANDONE, 2007). Deformation since then was dominated by the regional uplift of the Calabrian Arc (WESTAWAY, 1993; CUCCI & CINTI, 1998). However, detailed structural and geomorphologic analysis has revealed that uplift of Middle Pleistocene and younger marine terraces not only ensues from a regional-scale process, but also reflects a smaller-wavelength component of shortening which is attributed to recent, deeper activity of blind thrust and transpressional structures (FERRANTI et alii, 2009; CAPUTO et alii, 2010). Thus, shortening in this sector of the Apennines may still be ongoing although at a very slow rate and with a subdue morphological signature. The latter limitations have led to the common thinking that this sector of the Apennines is inactive. The submarine extension of the frontal thrust belt is represented by the Amendolara ridge, which stretches for over 80 km to the SE beneath the Taranto Gulf, the northern embayment of the Ionian Sea (Fig. 1, inset). Although it was suggested, based on existing multichannel seismic profile analysis, that the ridge has grown as a result of transpressional displacement (DEL BEN et alii, 2007; FERRANTI et alii, 2009), detailed images of the structural architecture as well as robust constraints on the timing of recent deformation were lacking. High-resolution marine geophysical data collected on the Amendolara ridge during the TEATIOCA_2011 cruise provided unequivocal constraints to assert active fault-related fold growth. Single-channel seismic (sparker) and acoustic CHIRP profiles, corroborated by multibeam mapping and shallow coring, form the novel dataset to constrain the near-bottom evolution. The new data were benchmarked to the crustal geometry by means of interpretation of existing multichannel seismic profiles. The integrated dataset analysis revealed that the NW-SE trending ridge has grown during Late Pliocene- Quaternary as a result of motion above an array of blind thrusts grouped into the Amendolara Thrust-Fold System (ATFS). Strikingly, the ATFS has displacement to the southwest toward land, and represents a backthrust belt in the regional reference frame. The stratigraphic signature of recent (Middle-Late Quaternary) fold growth is recorded by syn-tectonic depositional sequences within ponded basins and on the flanks of the ridge, and is represented by tectonicallystacked packages, and by widespread debris flows and slumping (Figs. 2, 3). Along the whole southwest margin of the ridge, the Middle-Late Quaternary depositional packages are ostensibly folded in response to southwestdirected displacement, as documented by fold asymmetry (Figs. 2, 3). Morpho-bathymetry data and seismic profiling show contrasting geometries and structural styles among the three ~15 to 20 km long and right-laterally offset banks which form the top of the Amendolara Ridge (Fig. 1). These banks are floored by the folded sediments and locally by lower Pleistocene or older bedrock. Whereas the Amendolara and Rossano banks are floored by an asymmetric fold (Fig. 2) the structure of the Cariati bank is represented by a north-dipping monocline with a train of minor frontal folds (Fig. 3). Based on the different geometry and morpho-structure, we argue that the banks are the expression of as many en-echelon blind fault segments. Based on the pattern of folded reflectors, the eastern and central segments (Cariati and Rossano, respectively) display evidence of more recent activity (Fig. 3). To the north of the Amendolara bank , a NE-verging system of two anticlines (Fig. 2), which are the offshore prosecution of the Valsinni ridge on-land (one of the recent most uplifted ridge of the Southern Apennines, PATACCA & SCANDONE, 2007), fold the basal unconformity of the last glacial sequence. Appraisal of the crustal seismic reflection profiles calibrated with exploratory wells reveals that the ATFS controlled deposition of an up to ~3 km thick syn-tectonic Late Pliocene-Quaternary sedimentary sequence in the Sibari and Corigliano basins, which flanks the ridge to the southwest (Fig. 1). Numeric modeling of Middle Pleistocene markers extracted from the sparker, and of older markers derived from the multichannel seismic dataset suggests that the three steep blind segments, which are the deep expression of the folded or tilted sediments in the banks, are rooted at ~10 km depth into a low-angle dipping detachment ramp extending to ~20 km depth. This deeper ramp is responsible for the overall NE-tilt of the ridge (Fig. 3) and development of a larger-wavelength fold involving the ridge as a whole. Documentation of active fold growth beneath the Amendolara Ridge carries important seismotectonic implications. This part of southern Italy is characterized by a low level of historical and instrumental seismicity. Indeed, the region suffered from moderate but locally damaging earthquakes caused by yet unknown or debated sources. The strongest earthquakes in the area are the 1836, April 25 (M= 6.2), the 1917, June 12, (M=5.25) and the 1988, April 13 (M=5) (data from CPTI11, ROVIDA et alii., 2011). The 1917 and 1988 events are located offshore, and our new analysis supports the contention that they spatially coincide with the central-eastern segments of the ATFS, where the evidence of more recent activity is found (Fig. 3). On the other hand, evidences show that the 1836 event is located inland, near the northern coast of the Sila (GALLI et alii, 2010). We also investigated the tsunami occurred during the 1836 event (TINTI et alii, 2004) and the alternative hypothesis between earthquake or submarine landside. Today, the ATFS shows a moderate seismic activity expressed by Mw<4.7 thrust and transpressional earthquakes (Fig. 4), which apparently originate at the branching zone between shallow ramp segments and deep detachment. The incremental shortening axis resolved onto the mean strike of the ATFS indicates left-oblique to reverse motion. Based on the size of fault segments and the modeled depth of micro-seismicity, we argue that the ATFS may be capable of moderate (M~6) earthquakes. Growth of the Amendolara Ridge temporally coincides with cessation of the Southern Apennines thinskin thrust belt motion, when collision between southern Adria and Europe overwhelmed retreat of the Apulian- Ionian slab, that had dominated the structural evolution of the central Mediterranean orogen since the Oligocene. The localization of the transpressional belt was controlled by an inherited mechanical interface between the thick Apulian crust and the attenuated Ionian crust. REFERENCES CAPUTO R., BIANCA M. & D'ONOFRIO R. (2010) - Ionian marine terraces of Southern Italy: insights into the Quaternary tectonic evolution of the area. Tectonics, 29, TC4005, doi:10.1029/2009TC002625. CUCCI L. & CINTI F.R. (1998) - Regional uplift and local tectonic deformation recorded by the Quaternary marine terraces on the Ionian coast of northern Calabria (southern Italy). Tectonophysics, 292, 67-83. DEL BEN A., BARNABA C. & TOBOGA A. (2007) - Strikeslip systems as the main tectonic features in the Plio- Quaternary kinematics of the Calabrian Arc. Mar. Geophys. Res., 29 (1), 1-12. FREPOLI A. & AMATO A. (2000) - Fault plane solutions of crustal earthquakes in Southern Italy (1988–1995): seismotectonic implications. Ann. Geophys., 43 (3), 437 – 468. FERRANTI L., SANTORO E., MAZZELLA M.E., MONACO C. & MORELLI D. (2009) - Active transpression in the northern Calabria Apennines, southern Italy. Tectonophysics, 476 (1-2), 226-251. GALLI P., SPINA V., ILARDO I., & NASO G. (2010) - Evidence of active tectonics in Southern Italy: the Rossano fault (Calabria), in Recent Progress on Earthquake Geology, edited by P. GUARNIERI, pp. 49 – 78. PATACCA E. & SCANDONE, P. (2007) - Geology of the Southern Apennines. Bollettino della Società Geologica Italiana, Italian Journal of Geosciences, 7, 75-119. ROVIDA A., CAMASSI R., GASPERINI P. & STUCCHI M. (eds.) (2011) - CPTI11, the 2011 version of the Parametric Catalogue of Italian Earthquakes. Milano, Bologna, http://emidius.mi.ingv.it/CPTI. TINTI S., MARAMAI A. & GRAZIANI L. (2004) - The New Catalogue of Italian Tsunamis. Natural Hazards, 33 439–465 WESTAWAY, R. (1993) - Quaternary uplift of Southern Italy, Journal of Geophysical Research, 98, 21741- 21772.
Ferranti, L., Pepe, F., Burrato, P., Santoro, E., Mazzella, M., Morelli, D., et al. (2012). Geometry and modeling of an active offshore thrust-related fold system: the Amendolara Ridge, Ionian Sea, southern Italy. In Atti del Convegno dell'86° Congresso Nazionale della Società Geologica Italiana. Cosenza.
Autori: Ferranti, L.; Pepe, F.; Burrato, P.; Santoro, E.; Mazzella, M.; Morelli, D.; Passaro, S.; Vannucci, G.
Titolo: Geometry and modeling of an active offshore thrust-related fold system: the Amendolara Ridge, Ionian Sea, southern Italy
Citazione: Ferranti, L., Pepe, F., Burrato, P., Santoro, E., Mazzella, M., Morelli, D., et al. (2012). Geometry and modeling of an active offshore thrust-related fold system: the Amendolara Ridge, Ionian Sea, southern Italy. In Atti del Convegno dell'86° Congresso Nazionale della Società Geologica Italiana. Cosenza.
Ferranti_Pepe et alii_Teatioca.pdf Main article N/A Open Access Visualizza/Apri


We're thrilled to announce today this year's full feature-length film lineup! This year's films include David Chase's Not Fade Away (Opening Night Film) with Chase in attendance; the previously announced Centerpiece Film Flight; the new film by David O. Russell Silver Linings Playbook, fresh off its Audience Award-winning premiere at the Toronto International Film Festival; The Sessions starring John Hawkes, Helen Hunt, and William H. Macy; Dustin Hoffman's directorial debut Quartet; The Sapphires; Shadow Dancer starring Clive Owen; Edward Burns' The Fitzgerald Family Christmas, and the world premieres of Spinning Plates, To Kill a Memory, The Muslims Are Coming!, The Rep, Ex-Girlfriends, and Mar Del Plata.
Note: the Shorts Line-Up does not include the Young Filmmakers Competition Shorts.


Company Overview | SPEEL PRAHA s.r.o.
We develop and manufacture solid-state flight data recorders, aircraft monitoring systems, ground support equipment for data visualization and evaluation.
We are an ISO 9001 registered company, and hold the Civil Aviation Authority as well as the Czech MoD approvals for aviation products development and manufacture.
The latest techniques in production, quality assurance as well as combined know how of both original "Eastern" and most modern "Western" technologies guarantee the high reliability and safety standards compliance of our products.
Besides our activities in avionics, we also manufactures Electronic Tachograph systems for subway and rail vehicles.
We invested 30 years of experience in aircraft diagnostic systems to bring our customers the best solution for their aircraft data recording needs.
Our certificates are available on a separate page.