No Boundary Ken Wilber Epub
Bernd Manison
Results: Among 202 deceleration zones analyzed during baseline rhythm, an overt LOB was evident in 47%. When differential pacing was performed in 38 deceleration zones without overt LOB, an underlying concealed LOB was exposed in 84%. In 152 VT activation maps (2D=53, 3-dimensional [3D]=99), 69% of lateral boundaries colocalized with an LOB in 2D activation patterns, and the depth boundary during 3D VT colocalized with an LOB in 79%. In VT circuits with isthmus regions that colocalized with a U-shaped LOB (n=28), the boundary invariably served as both lateral boundaries in 2D and 3D. Overall, 74% of isthmus boundaries were identifiable as fixed LOB during baseline rhythm or differential pacing.
a Seismotectonic map with the station distribution around the source region. The black rectangle in the inset represents the region shown in the larger map. The blue lines show the trench axes (Iwasaki et al. 2015), and the blue broken line indicates the boundary between the North American and Eurasian plates (Bird 2003). The orange star and circles indicate the relocated epicenters of the mainshock and 24 h aftershocks, respectively, of the 2021 Fukushima-oki earthquake, and the red star and circles indicate those for the 2022 earthquake. The green stars show the epicenters of the large intraslab earthquakes in the JMA catalog since 2000. The focal mechanisms from the catalog of the Full Range Seismograph Network of Japan (F-net; Kubo et al. 2002) are shown near the epicenters. The sky-blue triangles and dark-blue squares represent the strong motion stations and geodetic stations, respectively. The gray broken lines denote the plate-boundary depths at intervals of 10 km (Iwasaki et al. 2015). The pink lines show the lower depth limit of the area where interplate earthquakes occur (Igarashi et al. 2001; Uchida et al. 2009). b Distribution of teleseismic stations (sky-blue triangles). The red star represents the epicenter of the 2022 Fukushima-oki earthquake
Slip distributions of the 2021 and 2022 Fukushima-oki earthquakes. a Two-dimensional view. The slip distribution on the fault (thick blue square) is drawn on the left. The orange and red stars indicate the relocated epicenters of the 2021 and 2022 mainshocks, respectively. The gray broken lines indicate the plate-boundary depth at intervals of 10 km (Iwasaki et al. 2015). The pink line shows the same depth limit as in Fig. 1. b Three-dimensional views. The grey surface represents the plate-boundary
Because faults in the outer-rise region reach a depth of 40 km from the surface and many faults are mapped over 300 km along the trench (e.g., Baba et al. 2020), a similar fault system may exist outside the combined source region of the Fukushima-oki earthquakes. In the Japan Trench subduction zone, a neutral plane lies between the planes of the double seismic zone at a depth of 22 km from the plate boundary (Kita et al. 2010). This may control the rupture extension in the depth direction. In the horizontal direction, the heterogeneous velocity structures in the subduction zone (Nakajima et al. 2011; Wang et al. 2022) and fault distribution in the outer-rise region may provide possible candidates for future source faults.
We performed joint source inversions using strong motion, teleseismic, and geodetic data to investigate the rupture processes of the 2021 and 2022 Fukushima-oki earthquakes. Fault models of the two earthquakes were constructed based on the relocated aftershock distributions. The results showed that, for the 2021 earthquake, the rupture started on the WNW dipping fault and propagated along the up-dip and strike directions for both the WNW and ESE dipping faults. For the 2022 earthquake, the rupture primarily propagated along the strike and up-dip directions. However, a delayed rupture occurred around the hypocenter approximately 12 s after rupture initiation, which was probably due to the complex fault structure around the hypocenter. Synthetic data computed with our models accurately reproduced the observed data for both earthquakes and it indicates the appropriateness of the fault geometry. The angles between the plate boundary and our fault models were consistent with the dip angles of faults in the outer-rise region. Moreover, the complex fault system in the source region was also consistent with the fault distribution in the outer-rise region. These similarities suggested that the 2021 and 2022 earthquakes occurred on faults that originally formed in the outer-rise region and reactivated in the subducting slab. Such a fault system in the subducting slab probably partly controlled the rupture processes of the two earthquakes.
We employed several post-processing steps, which varied slightly between Models 1 and 2. These steps include enforcing tagging consistency, abbreviation resolution, boundary revision to balance parenthesis, and recognizing identifiers.
In other entity types such as genes, proteins and diseases, determining the entity type of tokens not observed in the training set is frequently difficult and must often rely on context. Many tokens in chemical mentions have highly distinctive features, however, which frequently allows the model to infer that the token is part of a chemical mention even if the token has not been seen previously. The greater problem for chemicals seems to be determining the mention boundaries, where errors cause a significant performance reduction since each boundary error results in both a false positive and a false negative under the common practice of assigning each token to at most one mention. We found, for example, that allowing boundaries to overlap instead of match exactly resulted in an f-measure for Model 1 of over 0.92.
One issue which causes boundary errors in many entity types is modifiers, since whether the modifier is considered part of the mention or not depends on the both the modifier and the core term. Our error analysis found many cases of boundary errors due to modifiers, such as returning "aromatic hydrocarbons" instead of "polycyclic aromatic hydrocarbons" or returning "Gynostemma pentaphyllum saponins" instead of "saponins." We anticipate that improving the modelling of tokens not seen during training could address these cases.
Non-boundary errors were of several types. One significant source of error is short proteins. Because proteins are large molecules with specific functions, they are essentially highly specialized chemicals. However the CHEMDNER task defined chemicals from the structural composition perspective, specifically defining peptides 15 amino acids or longer as biochemical entities, which are not annotated. Thus, relatively short proteins such as "kaliotoxin" and "thioredocin-1" are often mentioned in the abstracts with a discussion of their uses and effects, similar to chemicals, but are considered false positives when found because their length in amino acids exceeds the threshold set for annotation. Since determining if these mentions are chemicals requires integrating knowledge that is often not present in the abstract, resolving these cases requires identifying the mention, suggesting that named entity recognition and normalization are not always independent steps.
VERITAS-BEANZ questionnaire data (mode of travel, frequency of visits, travel companions) and map data (destination, polyline, and polygon coordinates) were downloaded and imported into ArcGIS 10.1 (ESRI, Redlands, CA, USA). Perceived neighbourhood boundary coordinates were then converted to polygons using the ET GeoWizards (ET Spatial Techniques, Faerie Glen, Pretoria) point to polygon conversion tool. The shortest network route between the principal residence and each mapped destination was estimated using the Network Analyst Extension and street centreline data obtained from the Land Information New Zealand (LINZ) database (www.linz.govt.nz). Using VERITAS-BEANZ questionnaire data, each of these estimated travel routes were coded as either active travel (i.e., walking, cycling), passive travel (i.e., motorised transport) or mixed travel, which was defined as a combination of both passive and active travel (whether individual trips were multimodal, or different travel modes were used for different trips). All frequency of visits data (reported as either times per week, month, or year) were all converted to times per year for comparative purposes. Using the distance, frequency and mode of travel, a weighted distance metric was computed to estimate the annual distance accumulated by each mode of travel, whilst travelling to each destination.
1 mile Euclidian buffers, 1 km network buffers and corresponding meshblocks were generated for the purpose of comparing the perceived neighbourhood. These buffer distances were chosen because they have commonly been used in adolescent samples [38-42]. A convex hull is a minimum bounding geometry technique which encloses multiple geographic features within the smallest possible convex polygon [20], and was used to define activity space by enclosing all geolocated destinations. Excluding destinations which are visited rarely may provide more representative spatial summaries of typical travel behaviour, but due to the pilot nature of this study, all destinations that were located during the BEANZ-VERITAS questionnaire were included in the activity space delineation (Figure 2). The ArcGIS XY to line and Generate Near Table tools were used to calculate the distance from the principal residence to the farthest boundary vertex, and the distance to the closest edge of each neighbourhood delimitation and activity space. The uniformity of each neighbourhood polygon around the principal residence was assessed using the ratios of these two distances along with shape circularity. Circularity is a measure of how closely a shape resembles a circle, and is defined as the ratio of the area of a shape with the area of a circle which has the same perimeter [43]. Circularity was calculated using the equation:
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