The extraction of brain tissue from cerebral MRI volume is an important pre-procedure for neuroimage analyses. The authors have developed an accurate and robust brain extraction method using a hybrid level set based active contour neighborhood model.
The method uses a nonlinear speed function in the hybrid level set model to eliminate boundary leakage. When using the new hybrid level set model an active contour neighborhood model is applied iteratively in the neighborhood of brain boundary. A slice by slice contour initial method is proposed to obtain the neighborhood of the brain boundary. The method was applied to the internet brain MRI data provided by the Internet Brain Segmentation Repository (IBSR).
In testing, a mean Dice similarity coefficient of 0.95±0.02 and a mean Hausdorff distance of 12.4±4.5 were obtained when performing our method across the IBSR data set (18 × 1.5 mm scans). The results obtained using our method were very similar to those produced using manual segmentation and achieved the smallest mean Hausdorff distance on the IBSR data.
An automatic method of brain extraction from cerebral MRI volume was achieved and produced competitively accurate results.
Brain extraction or skull stripping is an important pre-procedure in many neuroimaging analyses. Any subsequent analysis, such as registration between fMRI and T1-Weighted MRI, measurement of brain volume, brain tissue classification into White Matter, Gray Matter and GSF, and cortical surface reconstruction, will be highly dependent on the robustness and precision of the brain masks which are generated in the brain extraction step. Manual brain extraction requires an operator with professional knowledge about cerebral anatomy, and is extremely time-consuming. As a consequence, many automatic and semi-automatic brain extraction techniques using image processing have appeared in recent years. These require the operator to provide a few initial parameters prior to running the extraction process. These techniques can generally be categorized into three types: region-based, boundary-based and hybrid methods. We briefly describe and compare these methods as follows.
The region-based methods divide the cerebral image into several regions using thresholding or clustering techniques by observing that the intensities of the voxels belonging to the same tissue are similar. The brain region can be extracted from these regions by morphological operations and region merging. Lemieux  proposed an algorithm which utilizes several intensity thresholds and morphological operations to remove the non-brain areas. Coxs  used a Gaussian mixture model to estimate an intensity range in order to extract the brain areas in a slice-by-slice manner. Hahn and Peitgen  presented a pre-flooding height based watershed algorithm to group image voxels with similar intensities then merged the largest connected components to obtain the brain volume. The region-based methods are usually sensitive to image processing parameters and image artifacts, such as noise and intensity inhomogeneity. Therefore, the region-based methods usually require the user to determine the proper initial parameters.
The boundary-based methods are used to locate a closed contour which partitions the cerebral image into the internal part (brain) and the external part (non-brain). The contour can be a tessellated mesh or a continuous curve. Smith  proposed a well-known boundary-based method called the Brain Extraction Tool (BET). BET initializes the brain contour with a tessellated mesh and pushes the mesh to the brain boundary with some smoothing forces and a pushing force. BET is relatively insensitive to parameter settings because of the globally defined pushing force. The main drawback of BET is that the brain boundary is often over-smoothed due to the global pushing force. Another widely used boundary-based method is Brain Surface Extraction (BSE) . BSE uses a series of procedures to separate brain and non-brain tissues: anisotropic diffusion filtering, Marr-Hildreth edge detector and morphological operators. When using morphological operators the initial parameters can badly affect the accuracy of the extraction results. Many boundary-based methods use an active contour model which has resulted in great progress in medical image segmentation during recent years. Zhuang  developed an active contour model based level set method (MLS) that defines the image data function according to the pushing force used in BET and the smoothing function according to the mean curvature of the evolving curve. MLS can achieve good results in most cases. However, it is easy for MLS to leak through a weak boundary. Zhang  developed a brain extraction method using an improved region-based local binary fitting (LBF) model which is less sensitive to intensity inhomogeneity, but the extraction result can be over-smoothed by the leakage detection method when the rate of curvature is high.
The hybrid methods try to integrate region-based and boundary-based methods to improve the extraction result. Most of the hybrid methods are coarse to fine procedures. Usually the region-based method serves as a pre-process to obtain the rough brain region or boundary. The boundary-based method is carried out using the initial contour obtained in the pre-process. Extraction results using hybrid methods are more accurate since the initial contour is close to the brain boundary at the beginning of the boundary-based stage. Ségonne  proposed a hybrid watershed algorithm (HWA) combining a watershed algorithm and a deformable model, which applies the watershed algorithm to get an initial brain volume for use in the deformable model. The deformable model is driven by a smoothing force following the definition in BET. An atlas-based force is added to ensure that the deformed template possesses the shape of a brain within certain tolerances. Huang  applied an expectation-maximization algorithm to a mixture of Gaussian models to determine the initial brain contour for use in the geodesic active contour evolution. Rex  developed a brain extraction meta-algorithm (BEMA), which executes four extraction algorithms to obtain individual extraction results in concert with an atlas-based registration procedure, and combines the results to achieve the final improved brain extraction result using a variety of anatomically specified Boolean functions. It is obvious that BEMA is time consuming. To reduce the computational time required by atlas-based brain extraction, Eskildsen  proposed an atlas-based method using nonlocal segmentation techniques that eliminated the need for time-consuming non-rigid registrations. Sadananthan  used intensity thresholding to generate a preliminary binary mask ideally including the brain tissues, the skull and some thin connections between them. A graph cuts based image segmentation technique (GCUT) was then used to remove the narrow connections. GCUT is usually quite accurate but sometimes results in large errors due to following the wrong edge and the boundary of the brain is usually too smooth. Iglesias  proposed a robust, learning-based brain extraction system (ROBEX). ROBEX uses a random forest classifier trained to detect the brain boundary. The contour is then refined to obtain the final brain tissue results using graph cuts. ROBEX is quite robust, and major errors are rare (e.g., leaving the whole cerebellum out, or an eye in). The main disadvantage with ROBEX is that it produces over-smoothed results in brains with very convoluted surfaces on the gyri and sulci.
The above mentioned hybrid methods have achieved great improvements in the accuracy and robustness of automatic brain extraction techniques. However, none of these automatic or semi-automatic methods is a complete substitute for the manual method due to the appearances of over-smoothing, leakage through a weak boundary and missing brain tissue caused by local convergence. It is hard to solve these problems all at once. Brain extraction is a compromised problem where a semi-global understanding of the image is required as well as a local understanding . For example, using local region features is effective in obtaining a sharp brain boundary and eliminating leakage, but can easily lead to local convergence at the edges between the white and gray matter. In order to try to overcome this conflict we proposed a new brain extraction method for T1-weighted MRI volumes.
We developed a new, automatic method called the hybrid level set based active contour neighborhood model to extract brain tissue from T1-weighted MRI volume. Our method was inspired by the Graph Cuts Based Active Contours (GCBAC) proposed by Xu . Xu defined the belt-shaped neighborhood region around a contour as the contour neighborhood (CN). This was obtained by slightly dilating the current contour. The closest contour that is a global minimum within its CN can then be found using graph cuts, giving an initial contour. The CN and the closest contour were iteratively replaced until the objective was achieved. In our work, we called the iteratively replaced CN an active CN (ACN). Our proposed method pre-processed T1-weighted MRI scans with BET to generate the initial contour and the ACN. Then, an improved hybrid level set model, using a nonlinear speed function which can eliminate the boundary leakage effectively, was defined in the ACN to obtain a new contour. Since the initial contour obtained by BET is close to the real brain boundary, the real brain boundary should be the global minimum within the ACN obtained from the initial contour. Thus, our proposed method should prove to be robust and accurate.
To extract the brain tissue from the 3D MRI volume an improved slice by slice solution was adapted to reduce time consumption and improve the extraction result. The solution used the resultant contour from the current slice to initialize the contour in adjacent slices by contracting or expanding the resultant contour of the current slice.
The initial contour was estimated using a modified BET method for 2D images . The white contour in Figure 1(a) is the initial contour using BET. The white contour in Figure 1(b) is the brain boundary obtained using BET, which almost encloses the brain tissue, but is too rough to express the detail structure of the gyri and sulci. However, due to its robustness for obtaining the initial contour and parameters, BET still provides a good initial contour for brain extraction.
An initial ACN was defined by dilating the initial contour. In Figure 1(c), the region in the white ring is the initial ACN. Within the ACN, an improved hybrid level set was performed to obtain a new contour. The improved hybrid level set redefined the speed function of hybrid level set to avoid leakage through a weak boundary. Details of the improved hybrid level set will be described in the following sections.
The brain boundary was obtained by iteratively replacing the new contour and the ACN. In Figure 1(d), the brain boundary is more accurate than that in Figure 1(b) and better depicts the convoluted shape of the gyri and sulci.
Description of contour evolution in the ACN. (a) the initial contour; (b) the brain boundary obtained by BET; (c) the ACN obtained by dilating the boundary in (b); (d) the brain boundary obtained by iteratively performing the hybrid level set model in the ACN.
For 3D MRI volumes, our method started with a slice in the middle of the volume. The initial contour for the middle slice was estimated using the modified BET for 2D brain images. For the residual slices, the initial contour was not estimated using the modified BET but using the resultant brain boundary of its adjacent slice with respect to the continuity of the brain surface. This is because the parameters in BET don't fit all of the slices in the 3D volume and result in bad initial contours in slices containing the eyeball, brainstem and cerebellum part. In Figure 2(a), the initial contours for all of the slices were individually obtained using the modified BET hence all of the initial contours were badly initialized, leading to bad results in all of the slices. The initial method in our work is quite like the initial method used in . However, the initial contour is always in the resultant brain boundary in . Considering that in the 3D MRI volume, the brain boundary in the neighboring slice can be either inside or outside of the resultant boundary extracted from current slice, a new slice by slice initial method for 3D MRI volumes was proposed and this performed better than the initial method in . In our work, the initial contour was obtained by contracting or expanding the current boundary using a simple discriminant method. The discriminant method first partitioned the image region where the intensity of the current slice is higher than that of the next slice, designating this R1 and the rest of the image region R2. Then, if the resultant brain boundary of the current slice covered R1 more than R2, the contour in the next slice was initialized using a contracted resultant brain boundary, otherwise, the resultant brain boundary was expanded.
Comparison of the slice by slice contour initial method with each contour initialized by BET. (a) each contour initialized by BET. (b) the slice by slice brain contour initial method; Blue curve represents the initial contour and the red curve represents the resultant brain boundary.
The proposed initial method has some advantages. In the initial method, BET was only performed once on the middle slice. In most of MRI volumes, the brain tissues in middle slice are simpler than that found in other slices. The modified BET can immediately and robustly obtain the initial contour from the middle slice, but for other slices containing the eyeball, brainstem and cerebellum part a bad initial contour might be obtained. Setting the initial contour of the other slices from the resultant brain boundary of the previous slice can effectively trace the change of the brain boundary in the adjacent slices. Therefore, the initial contour is very close to the true brain boundary at the very beginning and this ensures that the active contour evolves to the true brain boundary using the hybrid level set model. This can save computational time and improve the accuracy of the results. In Figure 2(b), the initial contour in the 50th slice of the 3D MRI volume was obtained using the modified BET and the initial contours in the rest slices were well initialized using the resultant brain boundary from their adjacent slice. It can be seen that the brain boundaries in Figure 2(b) are more accurate than those in Figure 2(a). Figure 3 shows the flow chart for 2D brain extraction using ACNM. Figure 4 shows the flow chart for 3D brain extraction using ACNM.
The flow chart for 2D brain extraction using ACNM.
The flow chart for 3D brain extraction using ACNM.
The above equation can be considered as a speed function. Generally, the speed function consists of a combination of two terms: the data term and the smoothing term. Accordingly, the region term in equation (1) is the data term and the geodesic active contour function is the smoothing term.
Lefohn  proposed a linear piecewise speed function, shown in equation (4), which depends solely on the grayscale value of the input data I at a point. T controls the brightness of the region to be segmented and ε controls the range of grayscale values around T that can be considered to be inside the object. In this way, a model situated on voxels with grayscale values in the interval T ± ε will expand to enclose these voxels, whereas a model situated on grayscale values outside that interval will contract to exclude these voxels. The speed term is gradual (refer to Figure 5) and thus the effects of D diminish as the model approaches the boundaries of regions with grayscale levels within the T ± ε range.
A linear speed function based on image intensity. It causes the model to expand over regions with grayscale values within the specified (positive) range and contract otherwise.
The above mentioned hybrid level set model performs well for the segmentation of brain tissues. However, it has a few problems when applied to brain extraction. The first problem is that brain extraction is image processing where a semi-global understanding of the image is required as well as a local understanding. However, applying the hybrid level set model to brain extraction easily leads to local convergence. The second problem is that the weak boundaries between the brain tissues and surrounding tissues, with no efficient gradient, may make the gradient intensity based models [16–19] lose effectiveness, although some of these models have a robust convergence ability. The last problem is that the linear formulations for the data terms in equations (3) and (4) are too simple to describe the regional information and this often leads to leakage through a weak boundary.
To solve the last two problems a new nonlinear piecewise speed function, (shown in equation (5)), was defined to suit brain extraction in level set solvers. The data term D(I) in the new speed function depends on the grayscale value of input data I at a voxel, the mean grayscale value μ of the ACN, the global maximal value I m in the ACN and three regional static values I max , I min and I μ in the neighborhood (N 1 and N 2 ) of the voxel. In our research the size of neighborhood N 1 was 40 and the size of neighborhood N 2 was 20.
In Figure 6, μ and I m control the range of the grayscale values that can be considered to be located inside the brain tissues. Generally, the grayscale value outside the brain boundary but inside the ACN (the region indicated by the red arrow in Figure 7) is lower than μ. Hence, we set D(I) ≤ 0 to make sure the voxels with grayscale values in the interval [0 μ] were excluded by the contracting of the active contour. Observing that some of the surrounding tissues in the ACN, such as eyeball, have grayscale values bigger than I m (the region enclosed by the red ring in Figure 7), if the contour leaks through a weak boundary in these areas, the contour can expand rapidly leading to a very poor result. To avoid this happening, we set D(I) ≤ 0 to make sure the voxels with grayscale values in the interval [I m 1] were excluded by the contracting of the active contour. Whereas inside the interval [μ I m ], D(I) > 0, the voxels in these areas are enclosed by the expanding of the active contour. The speed parameter is t. In Figure 6, t has the opposite effect on contour contraction and expansion in the interval [0 I m ]. Increasing t can speed up contour contraction and slow down contour expansion, accordingly, decreasing t can slow down contour contraction and speed up contour expansion. When leakage occurs across a weak boundary, we can increase t to reduce the expansion rate and increase the contraction rate, thus the leakage can be corrected.
The nonlinear piecewise data term in the new speed function. Blue curve: the data term curve with t=2; Green curve: the data term curve with t=4; Red curve: the data term curve with t=6.
A weak boundary between the brain tissues and eyeball tissues in the red ring. The intensity of the region directed by the red arrow is lower than μ and the intensity of the region inside the red ring, with a weak boundary, is higher than Im .
Compared with the normal hybrid level set model, the smoothing term in the speed function is defined by the regional value I r not the image gradient. This is because the image gradient near a weak boundary is not big enough to stop the contour evolution, and the gradient between the white matter (WM) and the gray matter (GM) may be higher than that near the brain boundary because of strong noise and intensity inhomogeneity. If we used the image gradient in the smoothing term, the contour would easily leak though weak boundaries or stop at the intersection between the WM and GM. In Figure 7, the region indicated by the red arrow and the region inside the red ring at the edge of the brain boundary have large I r values, which decreases the evolving speed of the active contour and avoids the contour leaking though weak boundaries.
(1). Data set 1: 18 normal T1-weighted MR image volumes with expert segmentations were obtained from the Internet Brain Segmentation Repository (IBSR)  developed by the Centre for Morphometric Analysis (CMA) at Massachusetts General Hospital. Each volume has around 128 coronal slices, with 256 × 256 pixels per slice and a slice thickness of 1.5 mm.
(2). Data set 2: 20 normal T1-weighted MR image volumes from IBSR, with 256 × 256 pixels per slice and a slice thickness of 3.1 mm.
(1). Similarity coefficients. We used Jaccard similarity, defined as JS = M ∩ N M ∪ N , and Dice similarity, defined as DS = 2 M ∩ N M + N , where M and N refer to the extraction result and the ground truth respectively.
(2). Segmentation error: False Positives Rate (FP_Rate) and False Negatives Rate (FN_Rate). we used FP _ Rate = M − M ∩ N N and FN _ Rate = N − N ∩ M N .
where a and b are the points in M and N respectively, and d(a, b) is the Euclidian distance between a and b.
A comparison to BET (The version in the MRIcro software), BSE (The version in the Brainsuite9.0 software), WAT, HWA, GCUT and ROBEX was performed on the two chosen data sets.
The software  (Additional file 1) developed for our research was programmed using matlab2009 and was tested on a computer with 3GB of RAM and an Intel i7-2600 CPU. The software took approximately 10 minutes to process each MRI volume. Using this software, only parameters β and t from equation (5) needed to be set by the user for each volume. In testing, we chose to use fixed parameters (β=1.2 and t=6) for both data sets (labeled "ACNM One" in Tables 1 and 2) then changed these two parameters for each MRI volume in data set 1 (labeled "ACNM Two" in Table 1). "ACNM Three" in Table 1 had the same optimal parameters as "ACNM Two", but had different initial conditions.
"ACNM One" represents performing the ACNM with fixed parameters. "ACNM Two" represents performing the ACNM with varied parameters. "ACNM Three" represents initializing using a simple circle.
How the experimental results for the other methods were obtained is described as follows. For BET, we changed the fractional intensity parameter in the range [0.3 0.8] for each volume to obtain better performance. For BSE, we changed two parameters (diffusion constant and erosion size) when the default parameters led to bad extraction results for some volumes in data set 1 and 2. For WAT, HWA and GCUT, we used the experimental results with fixed parameters for each data set reported in . For ROBEX, we used the experimental results with fixed parameters on the IBSR data sets reported in . However, paper  didn't illustrate which IBSR data set was used, so we used the same experimental results on data set 1 and data set 2.
In Tables 1 and 2, "ACNM One" with fixed parameters (β= 1.2 and t= 6) led to the best JS, DS and HD coefficients out of all of the methods listed. The DS using "ACNM One" was slightly lower than that using ROBEX, but this is not statistically significant. The FN_Rate and FP_Rate coefficients weren't as good as those using other methods. Generally, the smaller the FN_Rate and FP_Rate are, the more accurate the extraction results. However, if the extraction results include all of the brain tissue and a lot of non-brain tissue, the FN_Rate equals 0. So an algorithm with a smaller FN_Rate and FP_Rate does not always produce a more accurate result. An accurate and robust algorithm must have a good trade-off between FN_Rate and FP_Rate. The FP_Rate and FN_Rate were lower than 10% using "ACNM One" and BSE, which indicates a favorable and acceptable trade-off between the FN_Rate and FP_Rate. The smallest HD coefficient produced using "ACNM One" suggests that it tends to preserve smaller non-brain structures in the vicinity of the brain surface, such as the dura, while the other methods preserve larger non-brain structures in the eye, neck and skull areas. Since "ACNM One" used the slice by slice initial method, slice thickness might affect the performance, and that may be why"ACNM One" did a better job of processing data set 1 with 1.5 mm thick slices than data set 2 with 3.1 mm thick slices, when the fixed parameters remained the same for both data sets. We changed the parameters (β and t) for each MRI volume in data set 1 to obtain more accurate results ("ACNM Two" in Table 1) and found that if the parameters were properly set, all of the evaluation metrics improved.
To prove how applying the slice by slice initial method to the middle slice impacts on the brain extraction result, two different initial methods were used in conjunction with our proposed ACNM method. In Table 1, "ACNM One" and "ACNM Two" initialized the contour in the middle slice using the resultant contour (the red curve in Figure 8) from BET, and then performed ACNM using the slice by slice method. "ACNM Three" initialized the contour in the middle slice with a circle contour (the blue curve in Figure 8) close to the resultant contour from BET, and then performed ACNM using the slice by slice method. The circle contour had the same centroid as the resultant contour from BET and the area of the circle was set as 0.83 times the area of the resultant contour from BET. Both "ACNM Two" and "ACNM Three" used the same optimal parameters for the each MRI volume in data set 1. As shown in Table 1, "ACNM Two" performed better than "ACNM Three", which proves that initializing the contour in middle slice using the resultant contour from BET improves the brain extraction result.
Two different initial contours in the middle slice. Red curve: the initial contour using the resultant contour of BET; Blue curve: the initial contour using a circle.
Figure 9 shows the final results using our ACNM method, GCUT, BET and BSE on the eighth volume on data set 1 as well as the manual segmentation result. We used MIPAV  software to obtain 3D images from four different viewpoints. Compared to the manual result, the final results using our ACNM method have fewer boundary leakages on the brain surface than GCUT and clearer structures of the gyri, sulci and brainstem than BET and BSE, although there are some rough artifacts between the slices in our final 3D results due to the independent way each slice is processed. It is obvious that the results using GCUT showed some boundary leakages on the occipital pole, the results using BET missed the cerebellum, and the results using BSE were oversmoothed on the surface of the brain.
Results of using the manual method, our method, GCUT, BET and BSE on the IBSR data. (a) Manual method (b) Our method (c) GCUT (d) BET in MRIcro software (e) BSE in Brainsuite software.
Compared to the other methods, the ACNM method used in our research led to fewer boundary leakages and more accurate contours close to the brain boundary. This was due to the more effective initialization method and more desirable hybrid level set model that we used in the ACN. As an automatic brain extraction method, our ACNM method is a considerably effective method with high accuracy and robustness.
We proposed an accurate and robust brain extraction method using a hybrid level set based active contour neighborhood model (ACNM). The proposed ACNM method has the following advantages over existing brain extraction algorithms: Firstly, our method uses a slice by slice contour initial method based on BET for 3D MRI volumes. This initial approach can obtain a robust initial contour very close to the brain boundary, which ensures that the active contour evolves to the true brain boundary, thus an initial ACN including most of the true brain boundary can be defined. Secondly, the proposed method only evolves the contour using the new hybrid level set model in the ACN. Since the ACN is mainly in the neighborhood of true brain boundary, the resultant brain boundary can be obtained more accurately and robustly. Thirdly, the new hybrid level set model has a nonlinear speed function which can effectively eliminate boundary leakage. Thus, the proposed method can extract brain tissues with a higher accuracy and robustness when compared to other methods.
Although the proposed method has the above advantages, there are some disadvantages with the current implementation. Firstly, the computational cost is higher than the other methods so in future a GPU based accelerating technique will be applied to greatly reduce the computational time. Secondly, two parameters need to be set manually, and if these parameters are set incorrectly the extraction result may not be accurate. Estimating these parameters using a machine learning method is a future project. Thirdly, our method was implemented as a 2D algorithm and may have resulted in some rough artifacts between slices in the final 3D results. However, the experiments carried out in our research showed that the 2D algorithm achieved more accurate extraction results than some 3D algorithms by using the similarity between 2D cortical contours on adjacent slices, which matches the conclusion in . How to eliminate these rough artifacts will be an important future project.
This work was supported by the National Natural Science Foundation of China (Grant No. 61162023 and No.61163046), Education Department Technology Project of Jiangxi province in China (Grant No. GJJ10195) and National Natural Science Foundation of Jiangxi province in China (Grant No. 20114BAB211023).
SJ worked on the algorithm design and development, and drafted the manuscript; WZ participated in the material preparation and experiment, YW revised the paper and ZC contributed discussions and suggestions throughout this project. All authors read and approved the final manuscript.


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Imagine two dining halls.
In the first one, an extensive team of talented chefs in white outfits serve an endless assortment of delectable dishes: steamed broccoli and asparagus with lemon juice, basmati rice and spicy curry, artisan panini-style sandwiches. One table carries eight types of bread and six types of spread, another bears the weight of a dozen desserts. People flock to this hall, load their plates with food, and then…
...they find the most isolated seat they can, wolf the meal down alone, then pack up and head back to work. Most of them don't know each other, and most never learn the names of the chefs who feed them.
In the second dining hall, people sit in groups of eight around square wooden tables, each bearing the same set of one main course, one salad, and one side. On a narrow wall counter, two types of bread and spread sit next to a 30-year-old conveyor-belt toaster. The food isn't fancy, but it has the warmth and heart of a home-cooked meal, and the diners make eye contact and talk to each other. Each knows the others, and all of them know the chef personally.
Where would you rather eat?
I've experienced both. The first is Arrillaga Hall at Stanford University, where I attended before transferring to Whitman College. The second is Stillman Hall at Midland School in Los Olivos, California — my high school and home, as a child of faculty. I unequivocally prefer the latter.
Size accounts for much of the difference between the two — Arrillaga might serve three to four hundred people at a time, while Stillman holds little more than one hundred, making intimate conversation easier.
But there's another factor at play here. In Arrillaga Hall, students dine with their eyes glued to their smartphones, while above them, enormous mounted television monitors display news channels or cooking shows.
In Stillman, at Midland, there are no television monitors. More impressively, not a single student has a phone.
In Arrillaga Hall, students dine with their eyes glued to their smartphones, while above them, enormous mounted television monitors display news channels or cooking shows. In Stillman, at Midland, there are no television monitors. More impressively, not a single student has a phone.
I trust most readers have heard of Stanford, but Midland, one of the last and best bastions against the overdependence of youth on technology, may require an introduction.
Founded by Harvard graduate Paul Squibb in 1932, Midland sought from the beginning to forego all nonessential elements of education, such as maintenance staff and, originally, electricity. By so doing, Squibb hoped to provide an affordable, high-quality college preparatory experience even in the midst of the Great Depression.
Of course, Midland has kept with the times to some degree — it now has electricity, almost 40 percent of which comes from solar panels installed by students. But the self-reliant, community-oriented philosophy remains. With no paid maintenance staff, students perform all chores on campus, from cleaning the bathrooms to washing the dishes to harvesting food from the garden. Those wanting a hot shower must gather and chop wood in order to build fires to heat the water. Think of it as a less intense, college-prep version of Deep Springs College.
But beyond the values of self-reliance and hard work, Midland students learn something just as important, if not more so: genuine, personal, non-digital connection with those around them.
Midland achieves this not only by asking students to sweep floors and scrub toilets together (each a surprisingly effective bonding experience in its own right), but also by removing unnecessary technological distractions. Midland students may not possess mobile phones, movies, televisions, or video games on campus. Internet may only be accessed in the library and classrooms, and most non-academic websites are blocked.
When they first arrive, many students don't see the point. To them, banning internet and video games seems as outrageous as banning music or radio might have to their parents' generation.
Midland is a haven for Luddites but it does pride itself as a place located off the beaten path of digital modernity and, where possible, off the grid.
But losing the screens opens up time for other things. With no phones to turn to, students find new ways to pass the time. Some play music together; others initiate pickup soccer and basketball matches. Some of my favorite memories from my time there involve heated UNO games in a friend's cabin with Tom Petty on the speakers.
In having these face-to-face interactions, students forge friendships faster and more strongly than if they were just texting each other. They also develop new interests they might never have discovered had the internet sucked up all their leisure time.
Students go outside. Set at the corner of almost 3,000 acres of chaparral and oak woodland adjacent to the Los Padres National Forest, Midland boasts an extensive network of trails for running, hiking, and camping. And the land around it is protected.
Better yet, Midlanders spend more time actually paying attention to the landscape around them rather than compulsively posting pictures of it to Instagram. They forget to construct superficial images of their lives on social media because they're too busy actually living. They may not have photos to prove it, but the cuts and bruises they acquire on their hikes tell a richer story.
English class at Midland School: no laptops, cell phones, ear buds, or Google in sight.
Above all, Midlanders know how to talk to each other. Instead of pulling out their phones at the dinner table, they look at each other, ask questions, laugh at the day's antics. Five dinners a week at Midland are family-style, with each table headed by a senior or, more often, a faculty member, ensuring that students develop conversation skills with adults as well as peers.
In the evenings, rather than retreating to their respective devices, teens might converse over a cup of tea in the dining hall or lounge together on couches in the library — all without a single phone in sight.
Of course, many rebel, watching movies behind closed blinds at the risk of minor disciplinary action. But technological restrictions benefit even the rule-breakers. Forbidding electronic entertainment means students no longer take it for granted — when you have to do it in secret, watching movies becomes a luxury, not a god-given right.
It also challenges students' creativity. In one case (the rumor goes), two students taught themselves how to set up a hidden private network for cross-cabin gaming, arguably a highly educational endeavor.
Some students even want to live without phones and choose Midland partly for this reason. For young people today, giving up technology in the name of real, personal connection may not always be an option — if the rest of your social circle depends on social media to communicate, you risk silencing yourself if you don't use it also.
Midland fixes this by ensuring that nobody depends on social media. Once none of your friends have it, you're free from the shackles as well.
Better yet, Midlanders spend more time actually paying attention to the landscape around them rather than compulsively posting pictures of it to Instagram. They forget to construct superficial images of their lives on social media because they're too busy actually living.
Eventually, though, the shackles return. For some students, re-entering a high-tech environment upon graduating proves more difficult than leaving it was four years earlier. My transition to Stanford and the Silicon Valley proved extraordinarily challenging and sometimes, as in the case of Arrillaga Dining Hall, downright depressing. The proliferation of screens cast an air of distance and loneliness over the hall, making otherwise delicious food unpalatable. On many nights, I lost my appetite.
The move was made all the more difficult by the fact that, in addition to attending Midland for 9th through 12th grade, I grew up there as a faculty kid with a relatively low-tech childhood. I had no video games until 6th grade, and my screen time throughout elementary school maxed out at one movie per week (a rule implemented when, as a toddler, I threw a tantrum when my parents wouldn't let me watch The Jungle Book for a fourth time in one sitting). Much of my childhood instead consisted of exploring Midland's woodlands top to bottom.
This doesn't mean technology was absent from my life. I used the internet occasionally and, at friends' houses, played video games. Today, I do both as freely as I choose. Thanks to those childhood limitations, however, it's just that: a choice, not an addiction. I never depended on a screen to entertain myself, and I know I never will.
Midland seeks to teach its students this same independence. At Midland, self-reliance doesn't just apply to chopping your own wood — it also means relying on your own organic ingenuity to entertain yourself instead of just switching on a device.
This, to me, is part of what makes Midland so valuable. By all means, youth today need to keep up with the pace of technology, — if they don't, they'll be left in the dust and, honestly, they'll miss out on some pretty damn cool stuff. Just as importantly, however, they need to be taught how to live independent from it. Social media claims to keep us connected, but those who depend on it will never truly connect with anyone or anything.
This holds true for people of all generations. The younger ones, however, stand at greater risk because we're growing up in a time when technology dependence is the norm. Parents today give their toddlers toy smartphones and their ten-year-olds real ones, then sit them down in front of the television and stick iPads in their hands instead of books.
People have their own reasons for doing this, and I'm not here to pass judgement. I only wish to point out that this sets developing minds on a course that's hard to break away from. When it's all you've ever known, escape becomes difficult.
Those who learn not to depend on screens, however, will connect in ways that will serve them for the rest of their lives.
They will connect with the world around them. They will notice the feeling of sand between their toes and the sound of waves in their ears instead of wasting thirty minutes finding the right selfie angle only to drop their phone in the water. Instead of trying so hard to show everyone that they're at the beach, they'll focus on actually being at the beach.
They will connect with each other. When thrust into new and uncomfortable environments like Midland, they will reach out to others for new friendships, new conversations, new senses of belonging, rather than hiding behind their phones and texting only the friends they already have.
They will connect with themselves. Devices like smartphones often serve as a means of easy escape from difficult emotions we don't feel like dealing with; they allow us to ignore our problems and let them fester. Removing the electronic distraction forces us to confront our demons, thereby improving our chances of ultimately conquering them. It also allows us to openly converse with loved ones who might wish to help us do so.
They will connect with information on a deeper level rather than a broader one. Technology brings us more information than ever before, but at the price of overwhelming our attention spans and increasing the shallowness of the information we consume. Those who learn not to click frantically at every next headline will develop the patience to read entire articles.
So, to those of you who read this far, good job. You might have done well at Midland.
About Sean Cummings
Sean Cummings was Mountain Journal's 2018 summer intern. Hailing from central California, he is a student at Whitman College. Read MoJo's interview with Sean by clicking below.
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redpajama_set_name: RedPajamaCommonCrawl


1. How cross-sell offers work?
You can suggest a bundle of complementary products which could be bought together by using cross-sell offers. As soon as customers view one of the pre-selected products, they will also see a bundle of products which are embedded to the product page, right below the Add-to-cart button.
Check it out in this demo store.
2. How to create a custom cross-sell offer?
Offer's name: This helps you manage your offers more easily. You can leave it blank.
This setting allows offering a discount to encourage your visitors to buy a whole combo.
Set up a discount for all variants combination by choosing "apply for all variant bundles"
Step 4: After you finish submitting all information, click "Submit Offer" button to complete everything.
When a customer views an item from one of these above-selected products, by default, Boost Sales will suggest the deal right on the product page, below the Add-to-cart button.
You can change the position of cross-sell widget or show it as a corner popup. For corner pop-up option, it doesn't display as a full-screen pop-up to avoid annoying your customer. You can always change the display at Settings tab >> Cross-sell settings. For detailed instruction, please visit our article How to change cross-sell offers' display?
Now it's time for you to get started! Create cross-sell offers now to boost sales for your shop.
In order to create/ edit many offers at a time and save your time, try "Importing - Exporting offers" by following the instruction hereor simply use Smart cross-sell feature.


Firefox 19 will be released on February 19th. Here's the list of changes that went into this version that can affect add-on compatibility. There is more information available in Firefox 19 for Developers, so you should read that too.
Remove XML Events. This is an obscure feature related to XForms, which was dropped long ago.
Remove XTF. Another XForms-related feature.
Private Browsing Mode is changing to support private and non-private windows opened at the same time. This means that many XPCOM interfaces and other functions are changing to support it. The changes are mostly new parameters that tell the function which context it is being called from, so that it can work accordingly.
ContentPrefService uses global Private Browsing state. All methods in this service now require the context to be passed to them.
nsNavHistory uses global Private Browsing state. This changes some of the History component code to support the new per-window private browsing. There are no API changes in this patch, though.
Add GUIDs to downloads. This adds a GUID to nsIDownloads, and APIs to handle downloads by GUID. The other APIs are still available, but will be gradually deprecated.
nsStrictTransportSecurityService uses the global Private Browsing service.
There's more information about the new private browsing in these docs: Supporting PBM, and Updating add-ons broken by PB changes.
Move DOMImplementation to Paris bindings. This changes nsIDOMDOMImplementation, making it non-scriptable.
Remove the nsIDOMCanvasRenderingContext2D interface. This removes everything in nsIDOMCanvasRenderingContext2D except for the DRAWWINDOW_ constants.
nsIUrlListManager.safeLookup not updated. The safeLookup function now takes an nsIPrincipal as an argument.
Change nsIChannel to support 64-bit content-length. This is just a return type change, so it should only affect binary add-ons, if any.
Add an option ignoreExisting to OS.File.makeDir.
Please let me know in the comments if there's anything missing or incorrect on these lists. If your add-on breaks on Firefox 19, I'd like to know.
The automatic compatibility validation and upgrade for add-ons on AMO will happen very soon, so check your email if you have an add-on on AMO.
Yes, unfortunately this can happen for very complex or large add-ons. The only solution is to make the add-on smaller or simpler. If this is preventing you from uploading a new version, we have ways to override it. If you emailed us, expect a response soon.
Please make an upgrade to fix it.
Why has PrivacySuite stopped working on every upgrade of firefox?
Privacy Suite needs to be updated on every release because it uses binary components, activating the strict compatibility mode in Firefox. However, it looks like they are doing a good job keeping up to date. It seems like you should only have compatibility problems if you use Aurora or Nightly. Is that your case?
There was a change in v16 in nsiIdleService that can break extensions. This has not been mentioned in the blog or tested the compatibility updates as far as I can see.
The observer will get an 'idle' notification when the user is idle for that interval (or longer), and receive a 'back' (Gecko 3 to 15) or 'active' (Gecko 16+) notification when the user starts using their computer again.
Existing extensions that monitor idle and expect 'back' might be missing the return from idle mode.


New York, NY – December 28, 2012 -- The Real Estate Board of New York (REBNY) collected over 300 pairs of shoes as a part of their annual donation drive. Throughout the summer and into the fall, members donated their gently worn shoes to Dress for Success and Career Gear. The annual drive is one of several charitable causes that REBNY organizes yearly in support of the community.
Dress for Success is an international nonprofit organization that promotes the economic independence of disadvantaged women by providing them with professional clothing, employment retention programs and ongoing support.
Career Gear is a national nonprofit organization offering interview clothing and job counseling to men in need.
While REBNY members donate their time and resources to innumerable causes both in New York and nationwide, The Real Estate Board of New York as an organization is involved in organizing several charitable initiatives throughout the year. The REBNY Member in Need Fund, for example, gives financial grants to REBNY members facing hardship because of a physical illness or a family crisis. Over the last 24 years, the REBNY has raised more than $2 million for various charities.


This is a serious waxing course for skilled waxing technicians who want to master the art of the much sought after and fashionable Hollywood and Brazilian waxing techniques. The emphasis is placed on hot waxing skills including Salon System's unique Just Wax Multiflex waxing technique.
Client consultation, contra-indications and client care / correct client positioning.
Intimate hot waxing procedure - underarm, bikini, brazilian and hollywood.
Please note: You will be required to provide a model from approx 12.30pm until 4pm. Students are expected to work on each other, for this reason it is a pre-requisite that all students grow their body hair for practice during the course (min 3-5mm for underarm waxing). Students are advised to avoid all heat treatments 24 hours prior to the course.


text:
Q: Wordpress REST API, GET featured image con una llamada estoy realizando una aplicación en android para mi página wordpress. Quiero obtener varios datos del JSON, uno de ellos la imagen asociada a los posts. El JSON que devuelve wordpress por defecto tiene el siguiente formato:
"author": 5,
"featured_media": 1836,
"comment_status": "open",
"ping_status": "open",
"sticky": false,
"format": "standard",
"meta":

Con este formato el GET solo me devuelve el ID del archivo por lo que necesito hacer otra llamada al servicio para obtener la url de la imagen asociada al post. ¿Como podría obtener la url de la imagen directamente cuando me devuelve un post de la página? Ahora mismo estoy haciendo dos llamadas y es muy ineficiente y lento. He probado a realizar cambios en el plugin pero no cambia el formato. Alguna solución? Solo necesito que me devuelva la url de la imagen al hacer un GET de los posts de la página.
public class FeedTask extends AsyncTask<String, Void, ArrayList<JSONArray>>{

private OnFeedListener listener;

public FeedTask(OnFeedListener listener){
    this.listener=listener;
}

@Override
protected ArrayList<JSONArray> doInBackground(String... params) {
    ArrayList<JSONArray> arrays=new ArrayList<>();
    String url=params[0];
    OkHttpClient client=new OkHttpClient();
    Request.Builder builder=new Request.Builder();
    Request request=builder.url(url).build();
    try {
        Response response=client.newCall(request).execute();
        String json=response.body().string();
       try{
           JSONArray array= new JSONArray(json);
           //Array de Imágenes
           JSONArray arrayImages=new JSONArray();
           for(int i=0; i<array.length(); i++) {
               String urlImage=("http://MIPAGINA/wp-json/wp/v2/media/")+array.optJSONObject(i).optString("featured_media");
               request=builder.url(urlImage).build();
               response=client.newCall(request).execute();
               json=response.body().string();
               arrayImages.put(new JSONObject(json));
           }
           arrays.add(array);
           arrays.add(arrayImages);
           return arrays;
        } catch (JSONException e) {
            e.printStackTrace();
        }
    } catch (IOException e) {
        e.printStackTrace();
    }
    return null;
}

@Override
protected void onPostExecute(ArrayList<JSONArray> array) {
    super.onPostExecute(array);
    if(array==null){
        return;
    }
    if(listener!=null){
        listener.onFeed(array);
    }
}

}

A: Pega esto en tu functions.php
//Prepare REST
function prepare_rest($data, $post, $request){
  $_data = $data->data;
  //Thumbnails
  $thumbnail_id = get_post_thumbnail_id( $post->ID );
  //Acá puedes reemplazar el 'full' por el tamaño del thumbnail que hayas creado
  $thumbnailNews = wp_get_attachment_image_src( $thumbnail_id, 'full' );

  $_data['featured_news_image'] = $thumbnailNews[0];

  return $data;
}
add_filter('rest_prepare_post', 'prepare_rest', 10, 3);

redpajama_set_name: RedPajamaStackExchange


text:
Dave Keil
Date: Dec. 1, 2016 Category: Company Tags: red herring, Red Herring Global 100
Gartner Research: Build Continuous Quality into Your DevOps Toolchain Read now
QASymphony / Blog / QASymphony Selected as Red Herring Global 100 Winner!
QASymphony Selected as Red Herring Global 100 Winner!
It is a tremendous honor to be selected as a Winner of the Red Herring Global 100 award after being named a winner for the 2016 Red Herring Top 100 North America Award earlier this year. Red Herring 100 Awards are widely recognized as one of the industry's more prestigious recognitions, with hundreds of candidates from each continent competing for a Top 100.
QASymphony has experienced rapid growth as global enterprises are transitioning to agile testing software and in 2016 we aggressively invested in the global expansion of our business — this award recognizes the hard work we have done. We anticipate making additional announcements over the course of 2017 as we continue investing in the international market.
This year we were also recognized by Entrepreneur Magazine as one of the Best Entrepreneurial Businesses, as a finalist of The European Software testing Awards, and we were featured in Test Magazine as One of the 20 Leading Testing Providers in 2016
The Red Herring editorial team selected the most innovative companies from a pool of hundreds of companies from across North America. The nominees are evaluated on 20 main quantitative and qualitative criterion:
trailing revenues
management's expertise
Applicants must be:
A technology/life science company
Headquartered in the specific region of the corresponding Red Herring 100 award program
Privately held and not listed on any exchanges anywhere in the world
This unique assessment of potential is in addition to a review of the company's actual track record and standing, which allows Red Herring to see past the "buzz" and make the list a valuable instrument for discovering and advocating the greatest business opportunities in the industry.
Reports are read and vetted before a senior Red Herring representative. Once companies are validated and graded, a short-list is established representing the most qualified companies. Companies making the short-list are asked to be part of the final phase of the award process, a live *presentation at their respective regional Top 100 event.
We presented our winning strategies at the Red Herring North America Forum and Global Forum in California.
Congratulations to all the other finalists and winners. Learn more in our recent press release.
See other QASymphony News>>


Offers pure essential oils, luxurious massage oils and a wide selection of massage books, videos and accessories.
A producer and supplier of flavor compounds in Singapore, including tropical fruit flavors, essential oils & vanilla products.
Offers a range of products including essential oils, herbal extracts, franzbranntwein & more.


text:
"Love on the Telephone" was the third single from the third album, Head Games by the band, Foreigner. The song was written by Lou Gramm & Mick Jones, and released as a single in Europe. It reached No. 34 in the Netherlands. The song's b-side, "Women" was chosen as the next single for the American market.

Johnson City Press arts writer Elaine Cloud Goller described "Love on the Telephone" as a "road-weary rock and roller's lament."

Pete Bishop of The Pittsburgh Press praised the song for its melody, full arrangement and muscle. El Diario del Centro del País praised the song as a great example of classic rock.  Evening Post critic Dave Murray praised Gramm's lead vocal performance.  Daily Republican Register critic Mike Bishop said that "Love on the Telephone" is "a fairly good rocker" and said that lead singer Gramm sounds a lot like Bad Company lead singer Paul Rodgers, Al Greenwood's keyboards sound a lot like Styx and that the song's chord progression is very similar to that of Foreigner's earlier song "Cold as Ice."  Morning Call critic Alan Janesch felt that lyric saying that the "line's engaged" was lifted from the Beatles' "You Won't See Me."

During Foreigner's 1981 tour supporting 4 "Love on the Telephone" generally served as the first song of the encore, followed by "Hot Blooded" and "Headknocker."

References

1979 singles
Foreigner (band) songs
Songs written by Mick Jones (Foreigner)
Songs written by Lou Gramm
Song recordings produced by Roy Thomas Baker
1979 songs
Atlantic Records singles
Songs about telephone calls
Song recordings produced by Mick Jones (Foreigner)
Song recordings produced by Ian McDonald (musician)

redpajama_set_name: RedPajamaWikipedia


Description Awards banquet sponsored by GLAAD, recognizing IBM and others for their progressive treatment of LGBTQ employees.
Citation Q161_AV0218, LGBTQ Institute's Mike Maloney Collection of OutTV Atlanta Video Recordings, Archives for Gender and Sexuality. Special Collections and Archives, Georgia State University.


My website is having some changes made to improve access and readability. As I did not want to close the site during alterations, some functions may not be working properly over the next few weeks. I hope this does not inconvenience you, and please remember that you can email me and arrange a phone call if you need any information about me or my services that you cannot find on the site.


text:
16feb7:30 pmCHICAGO7:30 pm Bass Performance Hall
CHICAGO has everything that makes Broadway great: a universal tale of fame, fortune and all that jazz; one show-stopping-song after another; and the most astonishing dancing you've ever seen. No wonder
CHICAGO has everything that makes Broadway great: a universal tale of fame, fortune and all that jazz; one show-stopping-song after another; and the most astonishing dancing you've ever seen. No wonder CHICAGO has been honored with six Tony Awards®, two Olivier Awards®, a Grammy® and thousands of standing ovations. Whether you're looking for your first Broadway musical, whether you've seen the Academy Award®-winning film and want to experience the show live on stage or whether you've seen it before and want to recapture the magic, CHICAGO always delivers.
Bass Performance Hall


text:
Police charge teen over Noarlunga Centre robbery, South Australia
United Kingdom contributes £1 million to support Afghan refugees in Iran
Horn of Africa: Swift aid for drought-affected farmers and herders needed to avoid hunger crisis
Police arrest two after stolen vehicle dumped in Altona North
Inspirational South West sisters blazing trail in learning and life
Spotlight on Research: What is seen and what is not from copy paper industry?
Message from Lord Mayor: Christmas in City
Holocaust and Sudan exhibitions shed new light on war photography
"We need to rethink methodology of assessing and supplementing iodine to large population groups"
What are blue, green, and grey water?
31 Jan 2019 6:56 am AEDTDate Time
CATL Connects World's First and China's Largest BESS Multi-Mixed Energy Power Station (100MWh) to the Grid
Contemporary Amperex Technology Co., Limited (CATL), a China-based manufacturer of lithium-ion batteries, has delivered world's first and China's largest battery energy storage system (BESS) multi-mixed energy power station ("the Station") as part of the Luneng Haixi Multi-mixed Energy Demonstration Project ("the Project"), which is the first of its kind in China to integrate wind (400MW), photovoltaic (200MW), concentrated solar power (50MW), and an energy storage system (ESS) (100MWh) into one unified system on the grid.
The Station coordinates three different renewable, with fluctuating and particularly unstable, sources of energy and is required to respond consistently to fluctuating demand, making its batteries and battery management system crucial to the reliability of the system. CATL, the exclusive battery supplier for the Project, overcome the requirements during product design and development stage and took merely 17 days to test and commission the BESS to the grid.
Huang Shilin, Vice Chairman and Chief Strategy Officer of CATL, said, "The Station is the first of its kind – a multi-functional, centralized power plant integrated with an electrochemical energy storage system. Its technical reliability and affordability will promote further global deployment of different renewable energy applications."
The first priority when building an energy storage station is safety. By selecting reliable materials, CATL minimize the possibility of failure incident and second damage explosion. CATL also follows the Potential Failure Mode and Effects Analysis(FMEA) design process to identify risks and execute risk mitigation plans in early stages of product development, manufacturing and management to ensure safe operation of the battery system with well-designed battery cell, module, cabinet and control system. Utilizing a three-layer relay protection system, CATL further assures performance of the battery system from damages caused by over-charging/discharging, over-current, mitigate over and under temperature working conditions.
One of the key challenges of safety and performances that CATL and the industry face is thermal management. The Station, installed at Golmud where temperature varies from -33.6 to 35.5 degrees Celsius. To ensure 15 years of battery performance, CATL has deployed a cooling system that uses air passages and air flow designs to maintain a consistent cabinet temperature. A battery management unit will kick-in the battery cooling system as soon as it detects thermo concerns, while pre-stored electricity protects the battery from capacity loss or lithium plating caused by charging in cold temperatures.
In addition, the Project has tough structural requirements, as Golmud lies in an active seismic zone, which calls for vibration and shock absorption. CATL's state of the art Test & Validation Center carried out rigorous testing and simulation to prove its batteries are able to withstand a potential earthquake of magnitude 8.
According to Dr. Hui Dong, Chief Scientist of China Electric Power Research Institute, the Station is "the World's first and China's largest electromechanical energy storage station with virtual synchronous generator." With the Station as an important part and an expected annual electricity generation amount of 126,250MWh (401,500 tons of coal generated energy), the Project is expected to be a good showcase of different new energy applications, thereby setting standards and smart grid applications around the world.
About CATL
CATL is China's largest battery provider specializing in the manufacturing lithium-ion batteries for electric vehicles and energy storage systems. It's devoted to promoting new energy, changing the world via EV & ESS battery technology. CATL's annual sales volume includes EV & ESS batteries amounted to 12GWh in 2017, EV battery installation volume of 2018 was 23.43GWh. https://www.catlbattery.com/en
SOURCE Contemporary Amperex Technology Co., Limited
/Public Release. This material from the originating organization/author(s) may be of a point-in-time nature, edited for clarity, style and length. The views and opinions expressed are those of the author(s).
Tags:Cabinet, China, explosion, failure, import, Loss, operation, power plant, power station, research, Solar, testing
Police lay charges after serious traffic crash at Margate
Success for Schools Beach Clean Up
Police charge two after service station robbery at Wellington


From their classic 1950s 550 racers to their newest 24 Hours of Le Mans winner—this is the history of Porsche, told in 20 race cars over a single lap of Laguna Seca.
Porsche got this shot at Rennsport Reunion this year and it feels like Need For Speed 5 in real life. This makes me so happy.
If I could take one out for a lap, I think I'd take the Targa Florio-grade 908/3. Which one would you drive?


Phil Long joined Regent College's faculty in 2000, after having taught for fifteen years at Covenant Theological Seminary (St. Louis, Missouri) and four years at the Freie Theologische Akademie in Germany. In addition to earning the degrees noted above, Prof. Long has studied at the University of Heidelberg, Germany, and at the Hebrew University, Jerusalem. His book publications include The Reign and Rejection of King Saul: A Case for Literary and Theological Coherence; The Art of Biblical History; the edited volumes Israel's Past in Present Research and Windows into Old Testament History; the co-authored work, A Biblical History of Israel; and 1 and 2 Samuel in the Zondervan Illustrated Bible Background Commentary. He has published a number of scholarly and more popular articles and reviews, has served on a variety of translation projects (NLT, ESV, Message), and has contributed commentary notes in the ESV Study Bible, the Spirit of the Reformation Study Bible, the Gospel Transformation Bible, and the NIV Study Bible.
Phil Long is an ordained teaching elder in the PCA. He and his wife Polly have four children, four children-in-law, and eight grandchildren. An avid outdoorsman and angler, he also enjoys portrait and landscape painting.
337 plong@regent-college.edu update in People.


PERFECTION Comes From The Details.
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The range and top speed of a Volta is reminiscent of the Brammo Enertia (below), formerly made in Ashland, Oregon but no longer in production. While the Enertia's limited range wasn't the only thing that impeded sales, it was certainly a contributing factor. Whether range will impact Volta sales or not should provide insight into the Spanish buyer. America does not have the same alternate transportation infrastructure, and the Spanish seem far more practical when it comes to getting from point A to point B.
By contrast, an entry-level American-made Zero S has almost 3 times the range of the BCN City – up to 121 miles / 195 km – with a top speed of 95 mph / 153 km/h. However, at € 12,700*, it costs € 4,705 more than the recommended price of the BCN City. That's a lot of train rides to Girona. To be fair, a top of the line Zero CAN take you to Girona and back (with an impressive 185 miles / 317 km of range) – but costs an additional € 12,873.
Will Catalonia embrace zero-emissions riding with a home-grown moto eléctrica? Visca Catalunya!
*Pricing listed on zeromotorcycles.com/es/ – public subsidies and zero emissions incentives not included in this barrage of numbers.


Celebrities and filmmakers from across the world will be descending on Toronto for the 39th Toronto International Film Festival in just a few weeks, and this morning TIFF revealed the full list of stars expected on the red carpets, at the films, and around the city at all of the parties.
Among the stars announced were: Rosario Dawson, Robert Downey Jr., Jennifer Aniston, Roger Waters, Alan Rickman, Chris Rock, Antoine Fuqua, Nikolaj Coster-Waldau, Kevin Costner, Xavier Dolan, Robert Duvall, Benedict Cumberbatch, Benicio del Toro, Alice Eve, Andrew Garfield, Bill Murray, Bill Nighy, Kevin Smith, Cliff "Method Man" Smith, Jon Stewart, and hundreds of others.
TIFF 2014 runs from September 4 to 14, 2014. Scroll down for the full list of stars and filmmakers, listed by last or middle name.
The official film schedule was also released today and is available at TIFF Bell Lightbox or online at tiff.net/festival. It is also available on August 21 in the Toronto Star and Metro newspapers.
Update: Browse some of the faces of TIFF 2014 on the festival's official Pinterest board.
Liam Aiken, Jennifer Aniston, Loreto Aravena, Gemma Arterton, Dorothy Atkinson, Charlotte Aubin, Marion Bailey, Ellen Barkin, Isaach De Bankolé, Jason Bateman, Haley Bennett, Sonja Bennett, Melissa Benoist, Juliette Binoche, Evan Bird, Douglas Booth, John Boylan, Jordana Brewster, Connie Britton, Daniel Bru?hl, Arabella Bushnell, Rose Byrne, Bobby Cannavale, Steve Carell, Josh Charles, Justin Chatwin, Justin Chon, Priyanka Chopra, Hayden Christensen, Sam Claflin, Richard Clarkin, Patricia Clarkson, Suzanne Clément, Jennifer Connelly, Nikolaj Coster-Waldau, Kevin Costner, Marton Csokas, Benedict Cumberbatch, John Cusack.
Rosario Dawson, Benicio del Toro, Kaitlyn Dever, Rosemarie DeWitt, Zhou Dongyu, Vincent D'Onofrio, Sarah Doucet, Michael Douglas, Robert Downey Jr., Brad Dryborough, Ger Duany, Robert Duvall, Ansel Elgort, Jillian Estell, Alice Eve, Vera Farmiga, Tina Fey, Jane Fonda, Morgan Freeman, Sarah Gadon, Charlotte Gainsbourg, Gael García Bernal, Andrew Garfield, Jennifer Garner, Richard Gere, Greta Gerwig, Sophie Goulet, Holliday Grainger, Aidan Greene, Jessica Gunning, Jake Gyllenhaal.
Kathryn Hahn, David Harbour, Alex Harrouch, Ken Harrower, Salma Hayek, Hannah Herzsprung, Dustin Hoffman, Nina Hoss, Holly Hunter, Max Irons, Jeremy Irvine, Johnny Issaluk, Christopher Jacot, Emmanuel Jal, Sian James, Felicity Jones, Jeremy Jordan, Reda Kateb, Diane Keaton, Catherine Keener, Anna Kendrick, Tory Kittles, Kevin Kline, Keira Knightley, Mpho Koaho, Chandia Bernadette Kodili, Nick Kroll, Lilianna La Greca, Allen Leech, Melissa Leo, Jane Levy, Justin Long, Alex Loubert, Steven Love.
Elyas M´Barek, Anthony Mackie, Chris Mackie, Marte Magnusdotter Solem, Tobey Maguire, Brit Marling, Faye Marsay, Gugu Mbatha-Raw, David Meunier, Anthony Michael Hall, Antoine Monot Jr., Maika Monroe, Julianne Moore, Chloë Grace Moretz, Viggo Mortensen, Bill Murray, Ahmed Muslimani, Bill Nighy, Arnold Oceng, Jack O'Connell, Haley Joel Osment, Muna Otaru, Al Pacino, Nate Parker, Michael Parks, Robert Pattinson, Alex Paxton-Beesley, Jaime Pebanco, Simon Pegg, Lee Perkins, Rosamund Pike, Antoine-Olivier Pilon, Aubrey Plaza, Christopher Plummer, Maxime Pomerleau, Colin Price, Bill Pullman, Tahar Rahim, Vanessa Redgrave, Eddie Redmayne, Ryan Reynolds, Sam Rockwell, Genesis Rodriguez, Mark Ruffalo, Rene Russo.
Adam Sandler, Romnick Sarmenta, Dan Savage, Tom Schilling, Ben Schnetzer, Ben Schwartz, Andrew Scott, Alexey Serebryakov, Amanda Seyfried, Michael Shannon, Dax Shepard, Tye Sheridan, Aidan Shipley, Bob Silvers, J.K. Simmons, Cliff "Method Man" Smith, Ross Smith, Timothy Spall, Peter Spence, Abigail Spencer, Octavia Spencer, Hailee Steinfeld, Dan Stevens, Ben Stiller, Corey Stoll, Peter Stormare, Ma Su, Omar Sy, Channing Tatum, Miles Teller, Aliaune "Akon" Thiam, John Travolta, Alexandre Vallerand, Garrett Wareing, Denzel Washington, Ernardi Watimin, Danny Webb, Dominic West, Kuoth Wiel, Kristen Wiig, Cassie Williams, Kate Winslet, Reese Witherspoon, Jung Woo-sung, Sam Worthington, Han Ye-ri and Zhao Wei.
Naji Abu Nowar, James Adolphus, Michele Alhaique, Lisandro Alonso, Kiki Álvarez, Yuri Ancarani, Roy Andersson, Sarik Andreasyan, Juanfer Andrés, Theo Anthony, Olivier Assayas, Robin Aubert, Barry Avrich, Ramin Bahrani, Jaume Balagueró, Anaïs Barbeau-Lavalette, Daniel Barber, Daniel Barnz, Rebecca Baron, Sophie Barthes, Eric Baudelaire, Noah Baumbach, Wiam Simav Bedirxan, Justin Benson, Nils Bergendal, Dana Berman Duff, Anahí Berneri, Paul Bettany, Susanne Bier, Charles Binamé, Mike Binder, Vincent Biron, Baran bo Odar, Shonali Bose, Scott Brachmayer, Adam Brooks, Israel Cárdenas, Peter Ho-Sun Chan, Damien Chazelle, Peter Chelsom, Andrew Cividino, Mary Helena Clark, Isabel Coixet, Saverio Costanzo, Paul Cowan, David Cronenberg, Harold Crooks.
Ernesto Daranas, Jean-Pierre Dardenne, Luc Dardenne, Siebren de Haan, Mijke de Jong, Yann Demange, Lav Diaz, David Dobkin, Xavier Dolan, Andrea Dorfman, Sotiris Dounoukos, Lee Do-yun, Fabrice Du Welz, Kaveh Ebrahimpour, Martin Edralin, Kris Elgstrand, Ronit Elkabetz, Shlomi Elkabetz, Tony Elliott, Martine Époque, Santiago Esteinou, Chris Evans, Sean Evans, Philippe Falardeau, Lixin Fan, Alexander Farah, Sepideh Farsi, Pascale Ferran, Severin Fiala, Fluorescent Hill, Anne Fontaine, Maya Forbes, Alain Fournier, James Franco, Veronika Franz, Toa Fraser, Sol Friedman, René Frölke, Tor Fruergaard, Antoine Fuqua, Sarah Galea-Davis, Connor Gaston, Ole Giæver, Dan Gilroy, Dana Gingras, François Girard, Shaun Gladwell, Richard Glatzer, Rob Grant, David Gordon Green, Eugène Green, Sturla Gunnarsson, Laura Amelia Guzmán.
Szabolcs Hajdu, Dieudo Hamadi, Bent Hamer, Mia Hansen-Løve, Ning Hao, Hal Hartley, Mark Hartley, Ethan Hawke, Jalmari Helander, Ryuichi Hiroki, Nguyen Hoang Diep, Margaret Honda, Israel Horovitz, Slater Jewell-Kemker, Cristián Jiménez, Boo Ji-Young, Gerard Johnson, July Jung, Park Jung-bum, Chang Jung-Chi, Zvonimir Juri?, Marie-Ève Juste, Serhat Karaaslan, Yassmina Karajah, Dome Karukoski, Ross Katz, Matthew Kennedy, Robert Kenner, Nino Kirtadze, Arlen Konopaki, Panos H. Koutras, Omung Kumar, Juris Kursietis, Kim Kyung-man, Richard LaGravenese, Joel Lamangan, Alexandre Larose, Michelle Latimer, Andrew Lau, Mélanie Laurent, Josh Lawson, Elizabeth Lazebnik, François Leduc, Mike Leigh, Jean-Baptiste Leonetti, Barry Levinson, Shawn Levy, Ben Lewis, Richard Loncraine, Andrew Loo, Julie Lopes Curval, Zhang Lu, Frieda Luk, Johann Lurf.
Adam MacDonald, Nanna Kristín Magnúsdóttir, Caroline Mailloux, Nilesh Maniyar, Mikael Marcimain, T. Marie, Carolina Markowicz, Isidora Marras, James Marsh, Lynne Marsh, Philip Martin, Mario Martone, Thomas McCarthy, Dave McKean, Santiago Menghini, Bennett Miller, Pat Mills, Reza Mirkarimi, Jyoti Mistry, Ako Mitchell, David Robert Mitchell, Jefferson Moneo, Grayson Moore, Michael Moore, Tomm Moore, Aaron Moorhead, Oren Moverman, Francesco Munzi, Ruba Nadda, Olivier Nakache, Tetsuya Nakashima, Afia Nathaniel, Andrew Niccol, Laura Nix, Jonathan Nossiter, Ryan J. Noth, Alanis Obomsawin, David Oelhoffen, Randall Okita, Joshua Oppenheimer, Luis Ortega, Ruben Östlund, Hlynur Pálmason, Kelvin Kyung Kun Park, Christian Petzold, Yim Pil-sung, Joana Pimenta, Matías Piñeiro, Shira Piven, Gabe Polsky, Denis Poulin, Gina Prince-Bythewood, Megha Ramaswamy, Hamy Ramezan, Matthew Rankin, Jason Reitman, Martin Rejtman, Giulio Ricciarelli, Alan Rickman, Chris Rock, Esteban Roel, Alberto Roldán, Oscar Ruiz Navia.
Samir, Christophe M. Saber, Benny Safdie, Joshua Safdie, Ghassan Salhab, Fernanda Salloum, Feike Santbergen, Marjane Satrapi, Sylvia Schedelbauer, Lone Scherfig, Céline Sciamma, Liew Seng Tat, Kim Seong-hun, Jacques Servin, Lynn Shelton, Raha Shirazi, Amer Shomali, Amie Siegel, Carlos Siguion-Reyna, Kevin Smith, Sion Sono, Jeffrey St. Jules, Jon Stewart, Mike Stoltz, Marah Strauch, Peter Strickland, Amanda Strong, Shim Sung-Bo, Mariama Sylla, Damian Szifron, Isao Takahata, Jordan Tannahill, Julie Taymor, David Tedeschi, Frank Ternier, David Thorpe, Jacob Tierney, Eric Toledano, Clément Trehin-Lalanne, Shinya Tsukamoto, André Turpin, Morten Tyldum, JP Valkeapää, Igor Vamos, Lonnie van Brummelen, Jan-Willem van Ewijk, Pieter Van Hees, Marte Vold, Calum Walter, Wayne Wapeemukwa, Matthew Warchus, Roger Waters, Wash Westmoreland, Blake Williams, Adam Wingard, Michael Winterbottom, Frederick Wiseman, Ray Wong, Wang Xiaoshuai, Young-Hae Chang Heavy Industries (Young-hae Chang and Marc Voge), Adilkhan Yerzhanov, Zhang Yimou, Soon-Mi Yoo, Jeffrey Zablotny, Krzysztof Zanussi, Baldvin Zophoníasson, Christian Zu?bert, Mich'ael Zupraner, Andrey Zvyaginstev, Edward Zwick and Antoinette Zwirchmayr.


This week Nastia and partner Derek Hough perform a dance to a song which reminds Nastia of her most memorable year. That, of course, being 2008 when she won the Olympic All Around gold medal in Beijing. They danced beautifully to her then floor music, "variation on dark eyes".


Cleaning Carpets can be a daunting task. Buying the correct carpet cleaning solutions from supermarkets can be expensive. However, you can make your own carpet cleaning solutions using common home ingredients.
Try some of these tricks to make your own Carpet Cleaning Solutions.
Take a ¼ tablespoon of detergent and 1 cup of water. Mix together the detergent and water. This solution can be used in a carpet cleaning machine or for scrubbing into a carpet with a rag or sponge. After the carpet is dry, vacuum up the remaining residue.
Take ½ cup of any all-purpose household cleaner without bleach, 1 cup of ammonia, ½ cup of fabric softener and 1 gallon of warm water. Use this cleaning solution either in a carpet cleaning machine or for scrubbing with rag or sponge to clean your carpet.
Use equal parts of window cleaner and water and mix together to make a cheap and effective carpet cleaner for your house or office. Use this solution and scrub into the carpet.
Hydrogen Peroxide is considered a powerful cleaner. Lemon is a grease-cutting cleaner which leaves behind a pleasant aroma. Combine these two ingredients and make a carpet cleaning solution. Take ¾ cup of hydrogen peroxide to a bucket, then add 1 1/2 cups of water, 5 drops of lemon essential oil and combine all ingredients together. Use this solution while cleaning your carpet.
Powdered carpet cleaners are splendid for spills and greasy stains. So, you can even make your own powered cleaning solution at home too. Take 1 cup of baking powder, 1 cup of cornstarch in a bowl, and 5 crushed bay leaves for a pleasant aroma. For extra fragrance add a sprinkle of dried crushed potpourri. Mix all these ingredients well to make a solution to clean your carpet.
Before you start cleaning, make sure that your carpet has been thoroughly vacuumed. Test the homemade carpet cleaner solution on a less visible area to make sure it is safe for your carpet and wait at least 24 hours for the test spot to dry to see the result.
When cleaning the carpet with a cleaning machine, read the directions for how much cleaning solution needs to be added as some reservoirs are smaller than others. For typical carpet shampoo machines, pour ¼ cup of concentrated solution into your carpet shampoo machine reservoir. Then fill the rest of the reservoir with warm water. Then run the machine over your entire carpet giving special attention to stains or spots. Let the carpet sit for a day after shampooing. This will allow the cleaning solution to fully soak into the carpet and evaporate which gives time for the carpet to dry.
When the carpet is fully dry, vacuum the entire carpet with your regular vacuum cleaner. This will extract any dirt and debris from the carpet and complete the cleaning process.
For a professional carpet clean, Call CRG Carpet Cleaning on 0411 154 433 or request a quote HERE.


The San Juan Islands are one of the best places in the world to see beautiful black-and-white orca whales in the wild. Two ecotypes are commonly found in the Salish Sea - the critically endangered Southern Residents (J, K, and L pods) and transient, or Bigg's, orcas. You may catch them passing by from some of the coastal locations on The Whale Trail. This collection of whale-focused museums, parks and launching points run all the way down the West Coast, highlighting some of the best places to see and learn about whales.
The Pacific Northwest is also experiencing a "Humpback Comeback" and other whales, such as minkes and grays, can be seen in the area. There are several ways to see and learn about the beautiful and mysterious 'blackfish', as the Northwest Coast Native peoples in this region call them.
Before setting out to see the whales in their natural habitat, stop by The Whale Museum to learn from experts about what you may see in our waters. One of the highlights of the museum is the skeleton of a young orca named Sooke (L-112) that mysteriously died in 2013. The museum shows the process of preparing the skeleton for display, and has several other exhibits where you can learn about the different kinds of whales that swim in the Salish Sea, hear their calls, learn to identify individual orcas by their markings, and learn what it takes to "Be Whale Wise" when viewing whales in the wild. This historic building in Friday Harbor, just three blocks from the ferry landing, also has a gift shop with a nice selection of sea-life related gifts, books, DVDs, toys, and lots more.
On the island's west side, about 10 miles from Friday Harbor or Roche Harbor, you'll find the popular 12-acre San Juan County Park, where you can camp on the shores of Haro Strait and enjoy views of the Olympic Peninsula and Vancouver Island. This is a great place to launch a kayak and enjoy a beautiful paddle along the west side of the island. You may see a variety of wildlife, including harbor seals, bald eagles, and Dall's porpoises. Find your own rocky bluff, explore beaches and tide pools, watch for whales, or picnic and play in the nearby meadows.
Find a guided kayaking wildlife tour.
You can also visit Lime Kiln Point State Park (aka "Whale Watch Park") on San Juan Island's west side. The Friends of the Lime Kiln Society (FOLKS) offer many free services, including an informative interpretive center, lighthouse, intertidal and park-wide tours, and fun special events during the summer.
Just a short walk from the parking lot, you can see a richly diverse environment, from rocky shorelines to wooded uplands. Seals, sea lions, and river otters cruise the shoreline through kelp beds and eel grass, looking for food. Eagles and seabirds reel overhead. The lighthouse was built in 1919 and still serves as a navigational beacon for ships in Haro Strait and offers tours during the summer. The park is considered one of the best places in the world to view whales from land, and there is a small seasonal gift shop and interpretive center where you can learn more.
Just off South Beach, part of American Camp on the south end of San Juan Island, lies Salmon Bank - a prey-rich hotspot for orcas, humpbacks, and other marine mammals. This is also where the whales enter and exit the Strait of Juan de Fuca and the Pacific Ocean. Be watching for wildlife before you even reach the parking lot, as bald eagles and foxes zigzag across the grassy headlands. Wind your way through the driftwood onto the pebbled beach and watch seabirds bob in the waves. Seals, sea lions, and the occasional river otter also cruise the shore, looking for a meal.
Cattle Point Lighthouse, on the southern tip of San Juan Island, was built in 1935, but a navigational lantern has been on watch here since 1888. Look for deer, eagles, seabirds, and marine mammals such as seals and sea lions from the shelter, picnic area and beach below. Whale Rocks, a collection of rocky islets popular with Steller sea lions, are visible offshore. When the wind is right, their grumbling can be heard for miles.
Want to Help the Southern Resident Orcas?
There are so many exciting and fun ways to enjoy the islands this summer, from 4th of July parades, to horseback riding in the forest, whale watching and barbecuing on the beach.