Roederer assessment cntr
How Useful is/was the Tutoring & Academic Engagement Cntr for You?
2023.04.03 07:32 amaninseattle How Useful is/was the Tutoring & Academic Engagement Cntr for You?
I've got a high school Sr. assessing college paths and interested in UofO. I'm interested in whether and to what extent students and alum found the T&A Cntr. useful as freshman.
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2021.04.04 03:14 TheGiraffeEater Dating violence victimization and borderline personality pathology: Temporal associations from late adolescence to early adulthood (2019) Personality disorders, 10(2), 132–142. https://doi.org/10.1037/per0000324 - Author's Personal Disord. 2019 Mar; 10(2): 132–142.
Dating violence victimization and borderline personality pathology: Temporal associations from late adolescence to early adulthood Personal Disord. Author manuscript; available in PMC 2020 Mar 1.Published in final edited form as: Personal Disord. 2019 Mar; 10(2: 132–142.)doi: 10.1037/per0000324PMCID: PMC6732254NIHMSID: NIHMS1046977PMID: 30829527 Salome Vanwoerden,a Jacob Leavitt,a Matthew W. Gallagher,a Jeff R. Temple,b and Carla SharpaAuthor information Copyright and License information Disclaimer
Borderline personality pathology is a serious mental illness characterized by pervasive interpersonal deficits that onsets during adolescence. Risk factors for borderline personality pathology include maladaptive interpersonal dynamics within attachment relationships. Given the shift toward emphasizing romantic relationships during adolescence as an important attachment relationship with implications for healthy development, the current study aimed to evaluate the longitudinal and reciprocal relations between victimization in dating relationships and borderline pathology in the transition from late adolescence into early adulthood. A large sample of high school daters (N = 818; 58% female; Mage = 16.10 years, SDage = .78) were recruited to complete annual assessments of borderline personality features and dating violence victimization (DVV) across five years. Results of a cross-lagged panel model revealed that primarily among females, borderline features predicted increased levels of relational, psychological, and physical violence whereas psychological and sexual violence predicted greater borderline features. The current findings provide the first evidence of a longitudinal association between victimization and borderline pathology in adolescence and suggest, particularly among females, that interventions for borderline features have important implications for reducing dating violence victimization among adolescents and young adults.
Borderline personality pathology is a serious mental illness characterized by affective, behavioral, and relational instability (Linehan, 1993). Though discrete “causes” of borderline pathology have not been identified, a number of risk factors have been suggested to contribute to its development. Major theories of the etiology of borderline pathology agree that an inborn, constitutional vulnerability interacts with what is referred to as a chronically invalidating environment. In a systematic review of studies, Stepp and colleagues (2016) found that family adversity and low SES, maternal psychopathology, affective parenting, and maltreatment are the most robust environmental predictors of borderline symptomology based on existing research. Due to the association between borderline pathology and attachment insecurity (Agrawal, Gunderson, Holmes, & Lyons-Ruth, 2004), it is not surprising that research into risk factors has focused on the child’s early caregiver relationships. However, attachment evolves further after childhood, and risk factors beyond the immediate family environment must be considered. Specifically, dating relationships in adolescence may represent an attachment relationship that is formative in the development, maintenance, or exacerbation of borderline pathology.
Up to 95% of US teens report ever having dated by the age of 18 (Manning, Longmore, Copp, & Giordano, 2014), corresponding to the age that adults in the US report having experienced their first episode of intimate partner violence (Breiding et al., 2014). A recent meta-analysis spanning multiple countries found the prevalence of physical violence victimization to be 21% among adolescents and sexual violence victimization to be 14% and 8% for females and males, respectively (Wincentak, Connolly, & Card, 2017). In the US specifically, a 2013 CDC study of high schoolers revealed that around 1 in 5 female and 1 and 10 male students were victimized by physical and/or sexual violence in the past year (Vagi, Olsen, Basile, & Vivolo-Kantor, 2015) with higher rates reported for psychological victimization according to the National Longitudinal Study of Adolescent Health (Halpern, Oslak, Young, Martin, & Kupper, 2001). While research has clearly demonstrated that dating violence victimization (DVV) is a risk factor for psychopathology during adolescence, research related to borderline pathology is less prevalent.
To date there are only two studies conducted in adolescence examining the role of DVV in relation to borderline pathology, both of which were conducted in the US. One study found a positive association between borderline features and DVV in a large sample of high schoolers, with stronger relations among females (Reuter, Sharp, Temple, & Babcock, 2015). Another study replicated the concurrent relations between borderline features and DVV in a sample of adolescent inpatients; additionally, inpatients with high levels of borderline features exhibited similar rates of self-harm regardless of their experiences of DVV, whereas among those with low levels of borderline features, the presence of victimization related to increased self-harm (Hatkevich, Mellick, Reuter, Temple, & Sharp, 2017). These findings suggests that the co-occurrence of even low levels of borderline features and DVV puts adolescents at risk for self-damaging behaviors. While these studies provide an important first step in investigating the role of dating violence in borderline pathology, their cross-sectional nature prevents inferences about whether dating violence is truly a risk factor for borderline pathology. Further, they limit the investigation of more complex reciprocal relations between borderline pathology and dating violence. There has been much greater attention to the relations between borderline pathology and peer victimization and bullying, with findings from longitudinal studies generally converging on the fact that bullying increases risk for subsequent borderline pathology (Winsper, Hall, Strauss, & Wolke, 2017). A recent study conducted in Europe replicated this association; however, they found that only among females was bullying associated with subsequent personality pathology, suggesting potential gender differences in the association between victimization and borderline pathology (Antila et al., 2017).
One of the limitations of cross-sectional designs to evaluate risk factors is the inability to determine temporal precedence. Various well-established correlates of borderline pathology are both longitudinal predictors and consequences of DVV. For instance, low self-esteem and maladaptive parent-child dynamics, correlates of borderline pathology, are robust predictors of victimization (Foshee, Benefield, Ennett, Bauman, & Suchindran, 2004). Additionally, common comorbidities of borderline pathology including substance use, suicidal ideation, and depressive symptoms, are predicted by DVV (Exner-Cortens, Eckenrode, & Rothman, 2013). Therefore, while not tested previously, existing evidence suggests that the relation between borderline pathology and DVV may be reciprocal in nature. Specifically, adolescents with borderline features are likely to be experiencing maladaptive parent-child dynamics and low self-esteem, which is related to risk for being victimized by dating partners. Further, if victimization is present, existing borderline features are likely to be exacerbated. However, these relations must be parsed from stability of borderline pathology (Bornovalova, Hicks, Iacono, & McGue, 2009) and dating victimization (Foshee et al., 2004), which requires the use of longitudinal designs.
Despite limitations in design among adolescent studies, research conducted with adults provide some clues regarding the role of victimization in the development of borderline pathology in adolescents, especially given that interaction patterns within relationships are often established in adolescence (Bouchey & Furman, 2006). While research is more prevalent that demonstrates general relationship dysfunction associated with borderline pathology (Daley, Burge, & Hammen, 2000), there is evidence that borderline features are overrepresented among individuals who have been the victim of intimate partner violence (Pico-Alfonso, Echebúma, & Martinez, 2008). Further, in a study by Maneta and colleagues (2013), both males’ and females’ borderline features were related to romantic partners’ perpetration of violence against them. These authors suggested that individuals with borderline features may be more likely to choose partners prone to violence, or that reactive and dysregulated behaviors may elicit aggressive responses from others. To be clear, victims are never to be blamed for their victimization; nevertheless, it is important to understand factors that contribute to violence.
Understanding the dynamics between borderline pathology and DVV during adolescence has potential value in improving our understanding of the development and maintenance of this disorder. Given that adolescent romantic relationships influence developmental tasks of adolescence such as identity and sexual development (Exner-Cortens, 2014), there may be a feedback loop in which dating victimization exacerbates the presence of borderline features. To this end, the current study examined temporal associations and lagged effects between borderline personality features and dating violence across late adolescence through young adulthood. As adolescents reduce their dependence on parents as exclusive attachment figures, they become more reliant on non-familial relations, especially peers and romantic partners (Scharf & Mayseless, 2007). Romantic relationships, in particular, have more distinct intensity than peers and therefore may be more salient (Collins, Welsh, & Furman, 2009). We expected to find reciprocal associations between borderline features and DVV such that greater borderline features would predict greater victimization and vice versa; however, we had no a priori hypotheses about the types of violence that may be related to borderline pathology. We also expected to find at least moderate autoregressive associations within each construct. Given that consequences of victimization differ depending on the form and severity of violence (Mechanic, Weaver, & Resick, 2008) and that the two previous studies conducted in adolescence utilized an overarching measure of DVV (Hatkevich et al., 2017; Reuter et al., 2015), we evaluated different forms of dating violence, although in the same model. Therefore, this is the first study to evaluate differential effects of various forms of dating violence on borderline pathology. Results - Table 1 displays descriptive statistics for and correlations between main study variables at each time point of the study. Examining correlations across time within single constructs revealed that correlations across time for BPD were medium to strong (.41-.58 across one time lag and .38 for the longest time lag). A similar pattern was seen for psychological violence (.47-.52 for one time lag and .31 for the longest time lag). Sexual violence showed medium sized correlations between scores one year apart. Interestingly, relational violence had the lowest magnitude of correlations across time (.13 - .27 for one time lag and .02 for the longest time lag). Across constructs at the same wave, correlations were mostly small to medium in magnitude with correlations between scores of relational violence and scores of physical and sexual violence being mostly small in magnitude. Similarly, correlations between borderline features scores and all forms of dating victimization were mostly small in magnitude. Results from independent samples t-tests demonstrated that females reported higher levels of borderline features, at least for the first three waves of the study (Cohen’s D ranging from .22 - .34). While statistically significant, differences were small in magnitude. Females, relative to males, reported higher levels of psychological (Waves 1-4), physical (Wave 1), and sexual violence (Waves 1-4) perpetrated against them (Cohen’s D ranging from .18 - .39), although again, these differences were small in magnitude. Given the bidirectional nature of violent dating relationships, we tested whether levels of victimization matched levels of perpetration as measured with the CADRI. In comparing means, we found small differences on annual reports of relational and sexual violence, with minimal differences across other scales (Cohen’s D for relational violence ranging from .18-.31 for Waves 1-4 and from .20-.23 for Waves 1, 2, and 4). These results are available from the first author upon request.
The majority of current or most recent dating relationships were reported to be heterosexual. Among females, rates of reported same sex relationships were 3.8% at Wave 1, 5.4% in Wave 2, 5.6% in Wave 3, 4.5% in Wave 4, and 4.8% in Wave 5. Among males, rates of reported same sex relationships were 4.3% in Wave 1, 6% in Wave 2, 8.7% in Wave 3, 8.8% in Wave 4, and 9.1% in Wave 5. Independent samples t
-tests were conducted to evaluate whether those who reported being in a same sex relationship differed in their levels of borderline features and DVV to determine whether this would be a potential confound. Among females at Wave 1, those in a same sex relationship reported higher level of borderline features (t
(401) = 2.31, p
= .021, D
= 0.64) but were not different in reports of any form of DVV (t
’s = 0.55 – 0.87, p
s > .05). Similarly, among males, those in a same sex relationship reported higher levels of borderline features (t
(299) = 2.62, p
= .009, D
= 0.85) but did not differ in the amount of victimization reported (t
s = −0.29 – −1.35, p
’s > .05). Because there were no differences between those in same sex versus heterosexual relationships on rates of dating violence victimization, further analyses were conducted within the full sample.
To examine the longitudinal dynamics between borderline features and dating violence, we evaluated a cross-lagged panel model in which concurrent relations between all constructs were modeled as well as cross-lagged paths between borderline features and all forms of violence, from psychological to physical violence, and from physical violence to sexual violence (Figure 1
). First, we tested whether constraining all autoregressive paths to be equal within each construct would result in significant changes in fit, which was not the case (χ2
(30) = 37.78, p
= .155). Next, in a model in which all paths were set to be equal across gender, fit was good according to the RMSEA estimate, but poor according to other indicators (χ2
(628) = 1066,935, p
< .001; RMSEA = .041 [90% CI: .037, .046]; SRMR = .101; CFI = .883). Next, all paths were freed between genders, leading to adequate model fit across all indicators (χ2
(500) = 875.74, p
> .001; RMSEA = .042 [90% CI: .028, .048]; SRMR = .090; CFI = .900). Model comparison test revealed that model fit improvement was statistically significant when allowing paths to differ between genders (χ2
(128) = 194.45, p
< .001). Cross-lagged path estimates for the non-constrained model are displayed in Table 2
. First, in examining cross-lagged paths among females, borderline features predicted higher reports of psychological violence at nearly every wave, while standardized effects were small (ranging from .12 to .14), they seemed to increase over time. This hypothesis was tested by conducting a nested model comparison between a model with these three cross-lagged paths set to equivalence (H0) compared to a model in which they were freed (H1). Constraining these three cross-lagged paths to equivalence did not significantly change model fit (χ2(2) = 0.41, p = .816) and when examining the difference in estimates from Waves 1 to 2 with paths from Waves 3 to 4/Waves 4 to 5, these differences were not significantly different from zero. Borderline features at previous wave also predicted relational violence at Waves 2 and 4 and physical violence at Waves 2 and 3; while these effects also increased over time, differences were not significantly different from zero. When looking at cross-lagged paths predicting borderline features at subsequent waves, there were less significant findings. Wave 1 psychological violence and Wave 4 sexual violence predicted increases in borderline features at subsequent waves, with small magnitude of effects. The pattern of results regarding prediction of dating violence by previous levels of borderline features was not mirrored among males. In fact, borderline features at Wave 1 predicted less physical violence at the subsequent wave. However, psychological violence was a significant predictor of subsequent borderline features at Waves 3 (standardized effect of .14) and 5 (standardized effect of .24). The difference in magnitude between these two effects was not significant from zero; however, they were significantly greater than the same effects among females (psych W2 predicting borderline W3: unstandardized effect difference = 0.92, SE = 0.42, p = .030; psych W4 predicting borderline W5: unstandardized effect difference = 1.33, SE = 0.60, p = .025).
Next, looking at autoregressive relations, paths largely mirrored what was found in correlational analysis; autoregressive paths for borderline features were moderate to strong (standardized effects of .44 to .54 in females, .44 to .53 in males), but for the most part, were small to moderate for measures of dating violence victimization, a pattern that was consistent across genders. The exception to this were the autoregressive paths for sexual violence victimization which ranged from .24 to .32 among females and .39 to .63 among males. Differences in these parameters between genders were statistically significant (unstandardized difference = −0.26, SE = 0.09, p
= .004) suggesting that among males, sexual violence demonstrates significantly stronger stability over time than it does among females. Relational violence had the lowest magnitude of autoregressive paths across both genders (suggesting the lowest level of stability from mid-adolescence to early adulthood).
As an ancillary test, we fit the same model without controlling for the effects of parental relationship quality. Model fit was adequate across all indicators (χ2
(420 = 713.19, p
) < .001; RMSEA = .048 [90% CI: .042, .054]; SRMR = .092; CFI = .905). Path estimates are available from the first author upon request. On the whole, results were largely unchanged, with the exception of the cross-lagged paths from borderline features to subsequent levels of dating violence. Specifically, while removing parental relationship quality from the model led to several paths to reduce in magnitude and no longer be significant among females (borderline predicting relational violence at Wave 2, physical violence at Wave 3 and 4, and psychological violence at Wave 5). Parental relationship quality seemed to have a suppression effect for males such that removing it from the model led to an increase in magnitude of some of these cross-lagged effects and the effect from borderline features to subsequent sexual dating violence at Wave 5 became statistically significant.
In the first study to evaluate the concurrent associations and bidirectional lagged effects across time between DVV and borderline pathology from late adolescence to early adulthood, we emphasize three findings. First, from mid-adolescence into young adulthood, higher borderline features predicted increased likelihood of being victimized in a dating relationship among females; however, this was not the case for males. Second, for males, we found that psychological violence predicted increases in subsequent borderline features, which was stronger than the parallel effect among females. Finally, of all forms of violence, psychological violence had the most robust associations with borderline personality features across genders. Altogether, these findings suggest that at least among females, borderline features prospectively is linked to victimization in dating relationships, whereas DVV (particularly psychological violence) is a more robust risk factor for borderline pathology among males. Together, although demonstrating small effect sizes, it appears that DVV is an important factor in the maintenance and exacerbation of borderline symptomatology for individuals transitioning from adolescence into young adulthood. This is particularly meaningful given that these effects were found even when controlling for stability of these constructs and potential confounds of parental relationship quality and SES.
Regarding our first finding, it is notable that the direction of prediction was largely characterized by borderline features predicting DVV among women, with only psychological violence at age 16 and sexual violence at age 19 predicting borderline features one year later. Overall, it appears that during middle to late adolescence, higher levels of borderline features put females at risk for being victimized by psychological, relational and physical aggression. This finding is interesting when evaluated alongside research demonstrating that individuals with BPD display greater hostility and aggression toward romantic partners when experiencing anxiety and avoidance (Critchfield, Levy, Clarkin, & Kernberg, 2008
). It is possible that individuals with borderline features elicit aggressive and hostile behavior from romantic partners, consistent with the suggestions made by Maneta and colleagues (2013)
. In turn, although not as robust, victimization predicts increases in borderline symptomatology.
Direction of predictive relations are in line with a developmental cascade model that is best understood in the context of typical development. Developmental cascades refer to the dynamic interplay of multiple factors across development in which functioning in one domain impacts functioning in other areas. Further, timing of various processes within developmental cascades can provide important information regarding critical periods for intervention (Masten & Cicchetti, 2010
). Adolescent intimate relationships contribute to psychological well-being by satisfying needs for identity and intimacy (Collins & Sroufe, 1999
; Shulman & Knafo, 1997
) during a developmental phase characterized by changes to the attachment systems (Scharf & Mayseless, 2007
) and identity development (Kroger, 2006
). Previous findings have shown that among females, changes in borderline features across adolescence correspond to worsening social skills, increased sexual activity, and poor self-perception (Wright, Zalewski, Hallquist, Hipwell, & Stepp, 2016
), thereby demonstrating that early borderline features are linked to psychosocial domains that may disrupt the process of healthy identity and personality development via instability in close relationships. Complementing these findings, the current study demonstrates directionality in these influences such that increased victimization may be a result of impaired interpersonal functioning in adolescents with borderline features. Further, it is possible that over time, persistence in victimization would lead to exacerbation of existing borderline personality pathology, as seen in the final waves of the study with sexual violence predicting increases in borderline personality features. Therefore, intervening in early disturbed interpersonal processes among adolescents with borderline features may be crucial to prevent escalation of disturbed relationship functioning.
Another surprising finding was the gender differences that emerged within the model. While borderline features among females largely predicted DVV in subsequent years, borderline features in males predicted decreases in physical dating violence. Previous research has found that men with borderline personality pathology are more likely to demonstrate an explosive temperament and impulsive aggressiveness (Mancke, Bertsch, & Herpertz, 2015
; Sansone & Sansone, 2011
), which in the context of romantic discord, may actually lead partners to withdraw rather than retaliate. Future research is needed to elaborate on these findings as they may not necessarily apply to same-sex relationships.
In terms of DVV acting as a risk factor for subsequent borderline features, effects were significantly stronger for psychological violence predicting increases in later borderline personality features in males compared to females. Previous research with cortisol data among patients with borderline personality disorder (BPD) has found that in response to psychosocial stress, adult males with BPD show increases in cortisol levels compared to females with BPD and male controls (Inoue et al., 2015
). Together, these results suggest possible etiological differences in the borderline personality pathology development based on gender. Alternatively, differences may be due to developmental timing (with interpersonal factors carrying stronger risk for borderline features among women earlier in development; Roeder et al., 2014). In fact, previous research has found that while both DVV and borderline features are equally distributed across males and females (for borderline pathology, specifically in community samples; Johnson et al., 2003
; Kimmel, 2002
), prevalence of victimization is higher among female patients with BPD compared to male patients (Bohle & Vogel, 2017
). Given that previous research on risk factors for borderline personality pathology tends to not explicitly model gender differences or includes female-only samples, future research should focus on understanding potential sex-specific trajectories of borderline personality pathology.
No a priori hypotheses were made regarding the forms of violence that may be associated with borderline personality pathology. Results suggested that psychological violence had the most robust associations with borderline personality features, both as a predictor and as a consequence. This was not surprising given that theories of BPD emphasize the centrality of emotional invalidation in perpetuating aspects of the disorder (Linehan, 1993
). Additionally, it has been found that borderline features in adolescence is related to psychological control and guilt induction by parents (Vanwoerden, Kalpakci, & Sharp, 2017
). However, it has been suggested that psychological violence such as yelling or swearing at a partner may represent a less severe dimension of dating violence when potential for harm is not expected (Cascardi, Blank, & Dodani, 2016
). Therefore, the lack of findings for the more severe forms of violence may be due to overall lower prevalence of sexual and physical violence in the current sample.
The current study has several strengths that contribute to research regarding risk for the development and maintenance of borderline personality pathology. First, using a longitudinal design with several assessment points allow us to evaluate more dynamic associations across a critical developmental period. Additionally, given noted gender differences in developmental mechanisms of borderline personality pathology (Johnson et al., 2003
), testing gender differences in the overall model allows for greater specificity in our understanding of these associations. Finally, the use of a large, ethnically and geographically diverse community sample increases external validity of the findings.
Despite these implications, several limitations must be noted. First, our study relied solely on self-report, which limits the generalizability of findings. It is a well-established finding that borderline personality pathology is associated with distorted interpersonal perception characterized by hypersensitivity to rejection (Lazarus, Cheavens, Festa, & Zachary Rosenthal, 2014
); therefore, dyadic reports of conflicts in dating relationships may allow for greater confidence in findings. Additionally, we utilized manifest variables, rather than using SEM to estimate models using latent variables. Future research should utilize these methods to account for measurement error and to demonstrate measurement invariance of these constructs over time. Second, while we controlled for quality of the parental relationship, we did not consider child abuse or neglect. Previous research has found that victimization by parents predicts greater victimization by intimate partners in adolescence (Foshee et al., 2004
). Further, when removing parental relationship quality as a covariate in our model, results were somewhat altered, but altered differently by gender. Future research is needed to unpack the complex dynamics between maladaptive parent-child dynamics and subsequent child-peeromantic partner dynamics that are related to borderline pathology, and how this may differ by gender. Finally, it is unclear whether victimization as a risk factor for borderline pathology is exclusive within dating relationships. There has been research finding that victimization by peers represents risk for borderline pathology in adolescence (Kawabata, Youngblood, & Hamaguchi, 2014
). Therefore, future research should investigate whether these effects are unique to dating relationships or are representative of close interpersonal relationships.
The current study strengthens previous suggestions that DVV is a risk factor for borderline pathology. Further, it provides evidence that borderline features in adolescence may increase the likelihood of being victimized. This has important health policy implications; there are several programs that have been developed to eliminate dating violence in adolescents including school-based programs promoting healthy relationships (Fourth R Program; Wolfe et al., 2009
), primary and secondary dating violence prevention programs that address beliefs and norms of dating violence as well as behavioral strategies for those engaged in dating violence (Safe Dates; Foshee et al., 2005
), as well as interventions that combine classroom- and school-level programs (Shifting Boundaries; Taylor, Mumford, & Stein, 2015
). These interventions may benefit from incorporating interventions for borderline personality pathology. Fostering interpersonal skills early in adolescence may not only assist in decreasing rates of dating violence, but can assist in preventing the development of borderline personality pathology.
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2019.11.27 12:37 Lupinepublishers-IAC Lupine Publishers Model Development for Life Cycle Assessment of Rice Yellow Stem Borer under Rising Temperature Scenarios
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Lupine Publishers Model Development for Life Cycle Assessment of Rice Yellow Stem Borer under Rising Temperature Scenarios
A simple model was developed using Fortran Simulation Translator to study the influence of increased temperature on duration of various life cycle phases of yellow stem borer (YSB) in Bangladesh environment. Model was primarily based on Growing Degree Day concept, by also including cardinal temperatures sensitive for specific growing stages of YSB. After successful calibration and validation of the model, it was taken for climate change (only temperature rise considered in the present study) impact analysis on the growing cycle of YSB. Temperature increase values of 1, 2, 3 and 4 oC were considered and compared with the Control (no temperature rise), by using historic weather of representative locations in eight Divisions of Bangladesh. Differential spatial response in the life cycle of YSB under various temperature rise treatments was noticed, and in general the growing cycle hastened with the rising temperature. The life cycle of YSB is likely to be reduced by about 2 days for every degree celcius rise in temperature, while averaged over locations. This means that there will be 2.0-2.5 additional generations of YSB in pre-monsoon season about 2.9-3.2 in wet season of Bangladesh. There is a need to include the phenology module developed in subsequent design of population dynamics model for YSB. Keywords:
Model; Growing degree days; Yellow stem borer; Life cycle assessment; Temperature rise
Yellow stem borer (YSB) is the most destructive and widely distributed insect-pest of rice. It causes dead heart or white head, depending on infestation time and significantly reduces rice yields by 5-10% and even up to 60% under localized outbreak conditions . It can grow in places having temperature >12 oC and annual rainfall around 1000mm. Generally, temperature and high relative humidity (RH) in the evening favors stem borer growth and development . The female moth oviposits from 1900 to 2200hr in summer, 1800 to 2000 hr in spring and autumn, and deposits one egg mass in a night and up to five nights after emergence. Optimum temperature is 29 oC having 90% RH for maximum number of eggs deposition. Optimum temperature for egg hatching is 24-29 oC with 90-100% RH. Larvae die at 35 oC and hatching is severely reduced when RH goes to below 70% . Larvae can�t molt at 12 oC or below and they die. The last instar larvae can survive unfavorable growth condition as diapauses, which is broken by rainfall or flooding. In multiple rice cropping, no diapauses takes place. The pupal period can last for 9-12 days and the threshold temperature for its development is 15-16oC.
The number of generations in a year depends on temperature, rainfall and the availability of host . The occurrence of the pest is generally the highest in wet season . Since there are many stem borer species, the average life cycle of rice stem borers varies from 42-83 days , depending on growing conditions. This implies that heterogeneous population can be found in the same rice field. Manikandan  also reported that development time by different phases of YSB decreases with higher temperature and thus increased population likely in future at early growth stages of rice crop. However, no such data is available in Bangladesh. Keeping the acute problem of YSB in Bangladesh, the present study was undertaken to develop a simple phenology-based) model to assess the life cycle of YSB in two major growing seasons of rice and subsequent taking it to evaluate the effect of rising temperature on growth cycle of rice yellow stem borer in representative locations of eight Divisions of Bangladesh.
Materials and Methods
Model for assessing the phenology of yellow stem borer was written in Fortran Simulation Translator and the compiler used is FSTWin 4.12 . This model will subsequently be used to develop population dynamics model for YSB in rice-based cropping systems prevalent in Bangladesh. Growing degree days (GDD) concept was used for this purpose, with base temperature assumed as 15 degree Celsius, below which growth and development activity in the life cycle of YSB does not take place. Each day, average temperature (mean of maximum and minimum temperatures) minus the base temperature is integrated over the growing cycle, and subsequently the development stage is achieved when critical value for attainment of a particular stage is crossed.
In the INITIAL phase, the GDD is taken as zero, which is read one time during running of the model
INCON GDDI, initial value of GDD = 0.
In the DYNAMIC phase, the program is executed daily till the FINISH Condition is achieved.
DAS, days after start of simulation = INTGRL (ZERO, RDAS)
PARAM RDAS, day increment rate = 1.
The development stage can be expressed in development stage (0-1), but in the present study not used for development stage identification, which we will use in further design of population dynamics model in coming times.
DVS, development stage = INTGRL (ZERO, DVR)
DVR, rate of development stage increase, Arbitrary Function Generator- a well defined FST function=AFGEN (DVRT, DAVTMP)
Since the age of male is relatively lower than the age of the female, so the computation is done separately as indicated below:
FUNCTION DVRT = -10.,0., 0.,0.,15.,0.,35.,0.03325,40.,0.0415
FUNCTION DVRT = -10.,0., 0.,0.,15.,0.,35.,0.0342,40.,0.0426
Base temperature below which the activities do not take place, degree celcius, is given as under:
Reading of weather data, on daily time step, is read through external file, as per well defined format for FST compiler, as given below:
WEATHER WTRDIR='c:\WEATHER\';CNTR=' GAZI';ISTN=1;IYEAR= 200
Where, various climatic elements are used as below:
RDD is solar radiation in J/m2/day
DTR = RDD
TMMX is daily maximum temperature; COTEMP is the climate change, temperature rise switch for evaluating the impact of temperature rise on the phenological development of the life cycle of YSB. TMMN is daily minimum temperature.
DTMAX = TMMX+COTEMP
DTMIN = TMMN+COTEMP
DAVTMP, average temperature (derived parameter) = 0.5* (DTMAX + DTMIN)
DDTMP, day time average temperature, derived parameter = DTMAX - 0.25* (DTMAX-DTMIN)
COTEMP is temperature rise/fall switch
PARAM COTEMP = 0.
DTEFF, effective temperature after deducting the base temperature = AMAX1(0., DAVTMP-TBASE)
SVP, is saturated vapor pressure in mbar, calculated from temperature (derived value)
SVP = 6.11*EXP (17.4*DAVTMP/(DAVTMP+239.1))/10.
VP is Actual vapor Pressure, mbar, an input for running of the modelAVP = VP
AVP = VP
RH is relative humidity, expressed in %, derived from the vapor pressure as below:
RH = AVP/SVP*100.
In the present study, only temperature and relative humidity effects are undertaken for computation of the phonological stages of the life cycle of YSB, although we have described the other climatic elements as part of the FST compiler, but the other parameters will also be used in deriving the population dynamics model, which we will take up in later course of time.
Since the development stages of YSB are influenced by relative humidity also, so we have to introduce the correction factor for including the effect of humidity, as below:
DAVTMPCF, RH induced temperature correction = DAVTMP*CFRH
CFRH is the Correction Factor for relative humidity for judging temperature is computed as below: i.e. during hatching (CFRHH) and larva formation (CFRHL) stages, computed as below:
CFRH, correction factor for RH=INSW (GDD-EGHATCH, CFRHH, DUM11)
Where INSW is FST Function, if GDD<979.9, then CFRHHD is taken and otherwise DUM11
CFRHH=AFGEN (CFRHHT, RH)
CFRHL=AFGEN (CFRHLT, RH)
WDS, wind speed in m/sec = WN
RRAIN, daily rainfall in mm = RAIN
TRAIN, total rainfall in mm = INTGRL (ZERO, RRAIN)
GDD is growing degree days, expressed in degree Celsius-days, is calculated as below:
GDD=INTGRL (GDDI, TMPEFF)
On the basis of literature search from the published literature, the growing degree days for various stages were computed and used in development of the model, and is described as below:
EGHATCH is the thermal degree days requirement for egg hatch, is as below:
INSTAR1 is thermal degree days for end of first instar 1 stage
INSTAR2 is thermal degree days for end of second instar stage
INSTAR3 is thermal degree days for end of third instar stage
INSTAR4 is thermal degree days for end of fourth instar (larva) stage
PUPA, is thermal degree days for end of pupa stage
ADULT LONGIVITY is thermal degree days for end of adult longevity, which is different for male/female, For Male=741.484 and Female=773.538, depending upon the defined parameter SEX
ADULT=INSW (SEX-1.05, FEMALE, MALE)
SEX=1. For female and 2. For male
PARAM MALE, growing degree days for male = 741.484
PARAM FEMALE, growing degree day for female = 773.538
Critical temperature above which the egg hatching stops is defined as below:
DEATH=REAAND (EGHATCH-GDD, DTMAX-40.)
HATMI, minimum temperature below which the Hatching stops, is defined as below
DEATH1=REAAND (EGHATCH-GDD, HATMIN-DTMIN)
LATMIN, minimum temperature below which larval growing stages stop, and is given as under:
DEATH2=INSW (GDD-EGHATCH,0.,REAAND(INSTAR4-GDD,LATMIN- DTMIN))
REAAND is FST Function, which will be 1 when both the variables within parenthesis are greater than zero; otherwise the value will be 0.
Duration of various stages is computed as below:
EGHATCHD is egg hatch duration, in days and computed as below:
EGHATCHD=INTGRL (ZERO, DUM1)
INSTAR1D is INSTAR1 Termination Day
INSTAR1D=INTGRL (ZERO, DUM2)
DUM2=INSW (INSTAR1-GDD, 0.,1.)
INSTAR2D is INSTAR2 Termination Day
INSTAR2D=INTGRL (ZERO, DUM3)
DUM3=INSW (INSTAR2-GDD, 0.,1.)
INSTAR3D is INSTAR3 Termination Day
INSTAR3D=INTGRL (ZERO, DUM4)
DUM4=INSW (INSTAR3-GDD, 0.,1.)
INSTAR4D is INSTAR4 Termination Day
INSTAR4D=INTGRL (ZERO, DUM5)
DUM5=INSW (INSTAR4-GDD, 0.,1.)
PUPAD is PUPA Stage Termination Day
PUPAD=INTGRL (ZERO, DUM6)
ADULTD is Adult Life End Day
ADULTD=INTGRL (ZERO, DUM7)
DUM7=INSW (ADULT-GDD, 0.,1.)
Stop of Run Condition is as under:
FINISH DEATH > 0.95
FINISH GDD> 775.
Integration conditions for running of the program are as under:
TIMER STTIME = 360., FINTIM = 600., DELT = 1., PRDEL = 1.
PRINT DAY, DOY, DVS, RH, AVP, SVP, WDS, TRAIN, GDD, DAVTMP, DAVTMPCF, ADULTD, PUPAD
In the TERMINAL stage, the final values at the stop of model run can be written in an external file:
CALL SUBWRI (TIME, COTEMP, EGHATCHD, INSTAR1D, INSTAR2D, INSTAR3D, INSTAR4D, PUPAD, ADULTD)
Reruns options for evaluating the impact of temperature rise on the development stages of the YSB can be run through this given below procedure:
Growing degree days for attainment of various growing stages in the life cycle of YSB were collated from the published literature in this region. The model was calibrated with 2003 weather data of Bhola district of Bangladesh against the findings of Manikandan  at 30 oC. After model calibration, it was subsequently taken to climate change window, temperature rise only considered in the present study. Eight divisions (Dhaka, Mymensingh, Rajshahi, Rangpur, Sylhet, Khulna, Chittagong and Barisal) of Bangladesh were taken and one representative location was chosen from each division and historic weather data of 35 years were taken for running of the model and the duration of each development stage was computed and compared amongst temperature rising conditions. In the present study, daily temperature rise from 1-4 oC were considered for two growing seasons, .com rice season i.e. premonsoon (April to June) and Aman Rice season i.e. Monsoon (late June to November) of Bangladesh.
Days required for completion of growth stages of rice yellow stem borer with increased temperature by 1, 2, 3 and 4 degree celcius in the growing environment of Bhola, Bangladesh.
Results and Discussion
During the test period, minimum temperature averaged 26�0.115 and maximum temperature around 31�0.32, with the average temperature around 30 oC, which was used for calibration and validation of the model, and the model performed satisfactorily well, through nice agreement between observed and simulated results (Table 1). Depending on growth stages, the percent deviations were within the limit of model errors. The application of model for specific years of Bhola district showed that the growth stages of rice yellow stem borer (YSB) were decreasing (Figure 1) by about 1.76 days per degree rise in temperature (Y=1.7X+54.6; R2=0.932). This indicated that YSB is likely to infest more rice plants in future under increased temperature conditions. Ramya  also reported that YSB would likely to develop faster, oviposit early and thus enhanced population build up than expected. There are reports that temperature increase by 2oC may cause 1-5 times additional life cycles of insects in a season .
Validation of various growth phases (days) of rice yellow stem borer.
Results, from represented locations in the eight Divisions of Bangladesh, showed that growth stage of YSB varied depending on season (Table 2). In .com pre-monsoon season, life cycle of YSB would likely to be completed within 47-53 days, depending on locations and temperature rise from 1-4 degree celcius. Similarly in Aman wet season, it would about 45-50 days for temperature rice from 1-4 degree celcius. However, under the Control (no temperature rise) condition, it requires around 52 days for T. Aman and 55 days for .com. Our findings indicate that growth cycle of YSB is likely to decrease by 2.04 days per degree rise in temperature in the .com season and 1.70 days in T. Aman season (Figure 2). Similar results were reported by Manikandan . Generally, insect population build up depends on favorable weather conditions and availability of host. So, there will be ups and downs in the peak build ups in a cropping season . Although model data needs to be cautiously adopted, it clearly showed that with climate change impact the infestation of YSB would be increased, which might be cause of yield reduction, if not proper management is taken at the right time .
Total life cycle duration of yellow stem borer as influenced by temperature rise during .com and T. Aman, season (averaged over eight Divisions of Bangladesh).
📷 Table 2:
Developmental phases (in days) of rice yellow stem borer as influenced by temperature rise in different growing seasons.
Yellow stem borer of rice crop is a major concern in Bangladesh. Dead hearts and white heads caused by YSB significantly reduce growth and yield of rice crops, especially in .com (Pre-monsoon) and T. Aman (Monsoon) seasons. There is a need to understand the phenology i.e. life cycle assessment and population dynamics of YSB in the growing environments of Bangladesh. In the present study, a simple model, as written in Fortran Simulation Translator (FST), was developed to assess the life cycle of YSB. The model was primarily based on growing degree day�s concept, by also considering cardinal temperatures for specific phenological/ development growth stages of YSB. The model was successfully validated with the growing environment of Bhola district of Bangladesh. Subsequently, the model was taken to assess the impact of rise in temperature on life cycle of YSB in representative locations of eight Divisions of Bangladesh. The response was spatiotemporally and seasonally variable. The life cycle hastened with the rise in temperature by 1-4 degree celcius. We, in near future, plan to develop a population dynamics model for YSB and to subsequently link it with the rice growth model to evaluate the yield reductions associated with YSB infestations https://lupinepublishers.com/agriculture-journal/fulltext/model-development-for-life-cycle-assessment-of-rice-yellow-stem-borer-under-rising-temperature-scenarios.ID.000144.php https://lupinepublishers.com/agriculture-journal/pdf/CIACR.MS.ID.000144.pdf
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2019.04.10 10:15 atiredasian Today In Naval History: 10th April - Starry Sky over the Arctic Fjord
Today In Naval History: April 10th, 1897 - Miles Browning
Miles Rutherford Browning is born in Perth Amboy, New Jersey, the son of Sarah Louise (née Smith) and New York City stockbroker Oren Fogle Browning, Jr. A brilliant man with a 'slide-rule' brain, as a planner an strategist, Miles was present and contributed greatly to United States victories at Midway and Guadalcanal. His irascibility, contempt for others and personal habit of bedding the wives of his fellow officers, would however lead to his eventual fall from grace.
Writers Note: Imagine being such a terrible human that a group of people as diverse as Mitscher, Nimitz, Spruance, King and Knox are all united in their hate of you and are willing to overlook your role in the victory at Midway to get rid of you.
Today In Naval History: April 10th, 1940 - Königsberg Conquered
While moored in Bergen harbour, the Königsberg becomes the target for eleven Blackburn Skuas from No. 803 Squadron and an additional five from No. 800 Squadron based at 'HMS Sparrowhawk' as the RNAS air station at Hatston, Orkney was affectionately known.
The 600 mile raid, which stretched the operational flight limits of the Skuas, saw the sixteen Skuas take off from Hatston at 0515 hrs, each carrying a 500lb bomb. The Skuas would make landfall 20 miles south of Bergen at 0700 hrs (save for one 800 Squadron Skua piloted by E.W.T. Taylour which had become separated and hopelessly lost) and would raid the harbour at 0720 hrs, approaching from the south-east at an altitude of 12,000 ft. Taking the opportunity, the Skuas circled around and attacking out of the sun which had now dawned, making their dives on the cruiser from prow to stern.
Caught entirely by surprise as the Germans believed the British dive bombers lacked the operational range to reach Bergen, by the time the crew of the Königsberg began to respond, more than half the British aircraft had completed their dives. According to German reports, the first bomb hit had also killed all electrical power on the ship rendering the 88mm guns in their power operated turrets useless and also making the 3.7 cm guns slow to aim on manual power alone.
British reports indicate three direct hits on the cruiser with the remaining twelve being near misses. The German reports are more flattering, claiming between five and six direct hits. As the Skuas winged away, the only damage taken was small holes in the wings of two Skuas from flak.
"The ship was very clear and plain in my sights and the only opposition was one AA gun on the fo’c’s’le manned by a very brave crew that continued firing throughout the whole attack. Down now to 4000 feet and still in that beautifully controlled dive that the Skua with its huge flaps could give. AA gun still firing and the tracer bullets were drifting up towards us like lazy golden raindrops going the wrong way. Now 2,500 feet, no fear or apprehension, just complete and absolute concentration; mustn’t drop too high and must watch going too low and blowing myself up with my own bomb blast. Very disturbed water round the ship, and water and oil seemed to be gushing out amidships. Still the fo’c’sle gun continued to fire and at 1800 feet I dropped my bombs and was away towards the sea at nought feet. My observer reported that we had had a near miss on the ship’s port bow."
- Captain R.T. Partridge, Royal Marines
The attack was devastating. Königsberg had been completely penetrated by the first attack, which had punched through her thin deck armor, plunged all the way through the ship, and exploded in the water below, causing severe structural damage and warping her hull. Subsequently hits took out her boilers and compromised her magazines, while the near misses had torn open both her port and starboard hull.
As the Germans scrambled to save the stricken ship, the final Skua wandered into the harbour and added to their misery by also dropping another bomb on the burning cruiser. With their ship now burning fiercely, the Königsberg's crew did all they could to battle the blaze save her, but with her electricals destroyed from British bombs and her pumps inoperable, firefighting efforts were ultimately a losing proposition, and her captain ultimately ordered her abandoned.
Though reports vary as to how long the cruiser remained afloat after the attack began, with her back broken, Königsberg ultimately exploded and sank in Bergen harbour, becoming the first major warship to be sunk by dive bombing in the war.
For their part, the British lost a single aircraft during the attack when an 803 squadron Skua went spinning out of control during landing and crashing, killing Lt. Bryan John Smeeton, and his crewman Mid.(A) Fred Watkinson.
Today In Naval History: April 10th, 1940 - Starry Sky over the Arctic Fjord
Having recently seized Narvik in a raid, the Kriegsmarine force in Narvik was faced with something of a difficult situation - a naval squadron of ten Kriegsmarine destroyers, had been unwittingly trapped in the harbour due to fuel shortages. Though they had originally been intended to return to bases in Germany, the destroyers from the 1st, 3rd, and 4th Flotillas (Z21 Wilhelm Heidkamp [Command], 1st Flotilla: Z2 Georg Thiele, 3rd Flotilla: Z18 Hans Ludemann, Z17 Diether von Roeder, Z19 Hermann Kunne, Z22 Anton Schmitt, 4th Flotilla: Z9 Wolfgang Zenker, Z11 Bernd von Arnim, Z12 Erich Giese, Z13 Erich Koellner) under command of Commodore Friedrich Bonte had originally intended to unload troops and be on their way but had been left stranded due to their inability to refuel.
The sinking of the tanker Kattegat in a British minefield by a Norwegian patrol vessel had left the thirsty little German ships running on empty. Though the oiler Jan Wellem was on site, refueling was a slow and difficult process as the tanker could only top up two of the German warships at a time.
Into this mess steamed the Royal Navy 2nd Destroyer Flotilla under Captain Bernard Warburton-Lee. Comprised of the H-Class destroyers Hardy [Destroyer Leader, Flag], Hotspur, Havock, Hunter, and Hostile, the 2nd Flotilla had been ordered to raid Narvik to determine the German disposition there and assess the viability of a landing to retake the area.
Suspicious of intelligence which claimed only a single German transport had entered the area, Captain Warburton-Lee had sailed to Tranoy Lighthouse to question the locals. In a farcical exchange, the British spoke no Norwegian and the Norwegians no English. Nevertheless, Captain Warburton-Lee was able to hash out that a German flotilla of at least six warships was already in Narvik along with at least one U-Boat.
Despite being grossly outnumbered (and outmatched - the German 1934 and 1936 zerstörer programs had produced far heavier [2200-2400 tons] and more heavily armed warships than the tiny H-Class ships [1350 tons], Captain Warburton-Lee had his orders. Conferring with the Admiralty, he concurred with the need for aggressive action. With the assurance of assistance from Royal Navy assets which were closing on his position, his final message sent via radiogram was "I'm going to attack at dawn."
At 0300 hrs, the British destroyers began cautiously probing their way through the fjord under the cover of a snowstorm.
Back in Narvik, having been at his post for more than 48 hours overseeing the Narvik operation, Bonte was near collapse from exhaustion and his captains, seeing little risk of enemy attack, had ironically finally successfully persuaded the worn out Commodore to get some sleep.
The German sailors were likewise fairing equally badly and fatigue was making them sloppy. A poorly performed change-of-guard handover between the picket ship Z17 Diether von Roeder and the Z22 Anton Schmitt saw the Royal Navy warships slink past unnoticed during the change of post due to the frightful weather.
At 0430 hrs, the British destroyers plunged into Narvik harbour with the advantage of complete surprise, Hardy, Hunter and Havock leading the charge, while Hostile and Hotspur moved to suppress the shore batteries.
The opening salvos were telling. Hardy tore into Z21 Wilhelm Heidkamp with torpedoes and gunfire while Hunter and Havock went after Z22 Anton Schmitt.
Hardy's first torpedo ran long, missing the Z21 but finding a merchant ship moored at harbour. The second struck home in Z21's stern, punching through to strike her aft magazine, peeling open Z21 with the force of the explosion and immolating Bonte and 81 of her crew instantly.
Writers Note: Conjecture on the part of the writer, but the Captain of the Havock was Lt. Commander Rafe E. Courage. Which might be responsible for the translation error where Z21 claims she was sunk by the Courageous (最后被勇敢号击沉).
Following the Hardy closely came Hunter and Havock which pounded the Z22 Anton Schmitt with gunfire before a trio of torpedoes broke her in half and the shockwaves from the explosion of the Z22 knocked out the engines of the nearby Z19 Hermann Kunne. As the ruined forward half of the Anton Schmitt rolled over, it toppled onto the Herman Kunne, entangling the latter warship.
By this point, however, the panicking Germans had begun to respond. Z18 Hans Ludemann and Z17 Diether von Roeder had begun to respond to the British attack. Before withdrawing, Havock, true to her name, landed shell hits that forced the crew of Z18 to flood her forward magazines to prevent a catastrophic explosion and also mangling her steerage. Z17 was subsequently engaged by the rather aptly named Hostile which set her boiler room ablaze, wrecked her fire control system, and set the forward section ablaze.
Seeing an opportunity, the unengaged Hotspur seized upon the chance to dump a spread of torpedoes into the German commandeered merchant shipping in Narvik harbour hitting a pair of merchant ships.
Having made a successful pass, Captain Warburton-Lee formed up his destroyers for another pass, sweeping into Narvik and shooting up the place again, targeting the remaining destroyers as well as the merchant shipping in the harbour. Amazingly however, the hapless but incredibly lucky tanker Jan Wellem somehow emerged untouched from the storm of gunfire and torpedoes.
With the five German warships either sinking or heavily damaged, Captain Warburton-Lee figured he had pushed his luck enough and ordered the Royal Navy warships to retire. He had made an unfortunate error however. Working with the assumption that the Germans had six destroyers in the area, the destruction or crippling of five ships would mean the bulk of the Kriegsmarine force had been knocked out.
He was about to be blindsided.
As the Royal Navy warships steamed out of Narvik, they were approached by Z9 Wolfgang Zenker, Z12 Erich Giese, Z13 Erich Koellner under the command of Fregattenkapitan Erich Bey which were rushing to provide assistance to their stricken comrades. The British responded as they usually do - by making smoke, which shielded their position from the approaching German vessels.
At this point however, the British luck ran out, as they were confronted by a pair of unidentified ships approaching from the mouth of the fjord. Guessing that he had engaged the entire German force and unsure of the identity of the new arrivals, Captain Warburton-Lee made the grave error of ordering his ships to hold fire.
Under no-such compunction, the Z2 Georg Thiele and Z11 Bernd von Arnim responded by crossing the T of the British formation at point blank range.
Realising his squadron was now trapped and encircled, Warburton-Lee gave his final orders - “Keep on engaging the enemy.”, shortly before Hardy was smothered with gunfire and a shell from the Z2 smashed directly into Hardy's bridge, wiping out her entire bridge crew leaving Paymaster Lieutenant Geoffrey Stanning the sole survivor with a broken leg. Steaming out of control, Hardy ploughed towards the shore at 30 knots. Stanning ordered the helmsman to enact an emergency course change, but there was nobody alive at the the helm.
Dragging himself down a ladder, Stanning painfully pulled himself to the wheelhouse and took up the ruined helm, altering the Hardy's course away from the shoreline before dragging himself back to the flag bridge, ordering the Hardy into a collision course with the Z2 and causing the cautious Germans to turn away.
For her trouble the Germans worked over Hardy again, smashing her boiler and forcing her crew to beach her and abandon ship.
Despite this, the Royal Navy destroyers remained caught between a rock and a hard place, sandwiched between the Z2 and Z11 and the closing Z9, Z12 and Z13 which were surprisingly unenthusiastic in their pursuit of the trapped British ships. In Erich Bey's defence, his destroyers were at the moment steaming on fumes.
Attempting to run the gauntlet, Havock went laying into Georg Thiele, wrecking her boiler and setting her alight with gunfire, forcing the Germans to flood her aft magazine to prevent an ammunition explosion. In response, the Germans responded by torpedoing Hunter and hammering Hotspur with gunfire which wiped out communications and hydraulic steering on the destroyer, sending the destroyer out of control and the Hotspur smashed into Hunter amidship.
Seeing the coupled ships, the Germans siezed upon the opportunity to pound the stricken Hunter, which finally rolled over and sank, guns still firing as she went. Suddenly freed by Hunter's demise, Hotspur's Captain Lt. Cmdr. Layman left the bridge to reestablish verbal communications, which spared him a bridge hit which all but wiped out his command staff. Layman established a double human chain of communications between the wrecked bridge and engine room and under local control, Hotspur’s guns savaged the approaching Bernd von Arnim who had incautiously assumed the damaged British destroyer was easy pickings. So chastised, the Bernd von Arnim withdrew to lick her wounds.
Seeing the Hotspur still fighting, Hostile and Havock turned back from the mouth of the fjord and charged the German lines, determined to rescue their sister ship.
Unnerved by this charge, Erich Bey ordered his destroyers to withdraw, coming under fire as they did so from the Hardy which had beached itself on the south Ofotfjord. Steaming clear, the Germans briefly stopped to pick up survivors from Hunter, though 10 of the 48 crew later died from exposure or wounds.
Limping clear and barely under control, Hotspur raced for the freedom of the sea escorted by Hostile and Havock.
Erich Bey would pay for his timidity in pursuit when the destroyers chanced upon the hapless ammunition transport Rauenfels, which sailed into the fjord just as the British were leaving carrying shells and torpedoes for the German warships. When the British signaled for her to stop and she failed to comply and attempted to run, Hostile gave her a pair of HE shells for her trouble setting the ammunition ship ablaze and sending her crew overboard as the order rapidly went to abandon ship.
Though the British briefly consider taking the Rauenfels as a prize ship, cooler heads determined that taking a burning ammunition ship under tow with a prize crew was a really stupid idea and she was abandoned.
After the British left, the Germans actually reboarded her and ran her aground, but Rauenfels was promptly captured by the Norwegians.
With the Germans destroyers now bottled up in the fjord, the Royal Navy prepared to finish the job, as the Grand Old Lady steamed for Narvik.
Today In Naval History: April 10th, 1941 - Gneisenau Bombed
Catching wind that Gneisenau was slated for the participation in Operation Rheinübung, the British are determined to ruin the party with a raid on the battlecruiser at Brest where she had retreated for repairs after being torpedoed by a RAF coastal command Bristol Beaufort. Bomber Command dropped around 25 tons of 227 kg AP bombs on the ship, four of which hit. All four hit the starboard side of the forward superstructure, damaging the ship and lengthening her repairs, keeping her out of Bismarck's debut operation.
Today In Naval History: April 10th, 1941 - The Niblack
The Gleaves-class destroyer USS Niblack (DD-424) performs the first hostile action between American and German forces during the Second World War when she drives off the German U-52 with a depth charge attack after she sank the Dutch freighter Saleier.
Today In Naval History: April 10th, 1941 - The Fox On Top
Akagi becomes the flagship of the IJN's newly organised First Air Fleet. Also assigned to the First Air Fleet are the 1st CarDiv (Akagi, Kaga), CarDiv 2 (Hiryu and Soryu) and CarDiv 4's Ryujo.
Ships Launched In Azur Lane :
I-26 (1940) - Confusingly launched as the I-27 for... reasons.
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2019.02.14 08:13 pj482818 The Evolution of the Research and Development Process – Global Die & Mould Market Forecast To 2025
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The research report presents a comprehensive assessment of the market and contains thoughtful insights, facts, historical data, and statistically supported and industry validated market data. It also contains projections using a suitable set of assumptions and methodologies. The research report provides analysis and information according to categories such as market segments, geographies, type of product and deal landscapes
The provincial analysis of the worldwide Die & Mould Market splits the global market area into key areas that include both continents as well as specific countries which are currently shining in phrases of demand, volume or normal Trends.
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Die & Mould share assessments for the regional and country level segments Market share analysis of the top industry players Strategic recommendations for the new entrants Market forecasts for a minimum of 5 years of all the mentioned segments, sub segments and the regional markets
Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations) Strategic recommendations in key business segments based on the market estimations Competitive landscaping mapping the key common trends Company profiling with detailed strategies, financials, and recent developments Supply chain trends mapping the latest technological advancements.
Note : Customization's Available On This Report
For more information on this report, please visit: https://www.reportsandmarkets.com/reports/global-die-mould-market-research-report-2019
The following manufacturers are covered:
Adval Tech Hella Roeders Sichuan Chengfei Integration Technology Guangdong Greatoo Molds Tongling Zhongfa Suntech Tianjin Motor Dies Himile Fenton Precision Engineering Thomas Keating Faulkner Moulds
Segment by Regions
North America Europe China Japan
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Segment by Type
Liquid Moulds Solid Moulds Dies Others
Segment by Application
Automobile Tire IT Home Appliance
The study objectives of this report are:
To analyze and study the global Die & Mould capacity, production, value, consumption, status (2013-2018) and forecast (2019-2025);
Focuses on the key Die & Mould manufacturers, to study the capacity, production, value, market share and development plans in future.
Focuses on the global key manufacturers, to define, describe and analyze the market competition landscape, SWOT analysis.
To define, describe and forecast the market by type, application and region.
To analyze the global and key regions market potential and advantage, opportunity and challenge, restraints and risks.
To identify significant trends and factors driving or inhibiting the market growth.
To analyze the opportunities in the market for stakeholders by identifying the high growth segments.
To strategically analyze each submarket with respect to individual growth trend and their contribution to the market
To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market
To strategically profile the key players and comprehensively analyze their growth strategies.
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Key questions answered in this report
What will the market size be in 2025 and what will the growth rate be?
What are the key market trends?
What is driving this market?
What are the challenges to market growth?
Who are the key vendors in this market space?
What are the market opportunities and threats faced by the key vendors?
What are the strengths and weaknesses of the key vendors?
Table of Contents
1 Die & Mould Market Overview
- 1.1 Product Overview and Scope of Die & Mould
- 1.2 Die & Mould Segment by Type
- 1.2.1 Global Die & Mould Production Growth Rate Comparison by Type (2014-2025)
- 1.2.2 Liquid Moulds
- 1.2.3 Solid Moulds
- 1.2.4 Dies
- 1.2.5 Others
- 1.3 Die & Mould Segment by Application
- 1.3.1 Die & Mould Consumption Comparison by Application (2014-2025)
- 1.3.2 Automobile
- 1.3.3 Tire
- 1.3.4 IT
- 1.3.5 Home Appliance
- 1.3 Global Die & Mould Market by Region
- 1.3.1 Global Die & Mould Market Size Region
- 1.3.2 North America Status and Prospect (2014-2025)
- 1.3.3 Europe Status and Prospect (2014-2025)
- 1.3.4 China Status and Prospect (2014-2025)
- 1.3.5 Japan Status and Prospect (2014-2025)
- 1.3.6 Southeast Asia Status and Prospect (2014-2025)
- 1.3.7 India Status and Prospect (2014-2025)
- 1.4 Global Die & Mould Market Size
- 1.4.1 Global Die & Mould Revenue (2014-2025)
- 1.4.2 Global Die & Mould Production (2014-2025)
Browse Detailed TOC, Tables, Figures, Charts And Companies Mentioned : https://www.reportsandmarkets.com/toc/global-die-mould-market-research-report-2019
2 Global Die & Mould Market Competition by Manufacturers
- 2.1 Global Die & Mould Production Market Share by Manufacturers (2014-2019)
- 2.2 Global Die & Mould Revenue Share by Manufacturers (2014-2019)
- 2.3 Global Die & Mould Average Price by Manufacturers (2014-2019)
- 2.4 Manufacturers Die & Mould Production Sites, Area Served, Product Types
- 2.5 Die & Mould Market Competitive Situation and Trends
- 2.5.1 Die & Mould Market Concentration Rate
- 2.5.2 Die & Mould Market Share of Top 3 and Top 5 Manufacturers
- 2.5.3 Mergers & Acquisitions, Expansion
3 Global Die & Mould Production Market Share by Regions
- 3.1 Global Die & Mould Production Market Share by Regions
- 3.2 Global Die & Mould Revenue Market Share by Regions (2014-2019)
- 3.3 Global Die & Mould Production, Revenue, Price and Gross Margin (2014-2019)
- 3.4 North America Die & Mould Production
- 3.4.1 North America Die & Mould Production Growth Rate (2014-2019)
- 3.4.2 North America Die & Mould Production, Revenue, Price and Gross Margin (2014-2019)
- 3.5 Europe Die & Mould Production
- 3.5.1 Europe Die & Mould Production Growth Rate (2014-2019)
- 3.5.2 Europe Die & Mould Production, Revenue, Price and Gross Margin (2014-2019)
- 3.6 China Die & Mould Production (2014-2019)
- 3.6.1 China Die & Mould Production Growth Rate (2014-2019)
- 3.6.2 China Die & Mould Production, Revenue, Price and Gross Margin (2014-2019)
- 3.7 Japan Die & Mould Production (2014-2019)
- 3.7.1 Japan Die & Mould Production Growth Rate (2014-2019)
- 3.7.2 Japan Die & Mould Production, Revenue, Price and Gross Margin (2014-2019)...Continued
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2016.08.17 21:40 LedgeEndDairy Arena Counters - Guide/Discussion
Hi All. I just posted a "Tier List" for the different aspects of Heroes Tactics. You can find that here
This is a guide to countering Arena Compositions. By itself it's not horribly useful, so I've created a tool in Excel that will do the counting and whatnot for you to give counter-team recommendations. I will post a link to it here when I can upload it (I'm currently working). For now, though, here's my Arena Counter table:
|Hero ||Type ||Counter-type ||# of Counters ||Counter 1 ||Counter 2 ||Counter 3 ||Counter 4 ||Counter 5 |
|Helena ||Heal ||Phys DPS ||0 ||Zorak ||Dracula ||Pei ||Gable ||Isaac |
|Zorak ||Mbl DPS ||CC/Slow Tank ||11 ||Cyclops ||Pixie ||Isha ||Isaac ||Elemix |
|Cyclops ||Rgd Tank ||CC/Rgd ||11 ||Pixie ||Trinity ||Cyclops* ||Isaac ||Gladius |
|Centaur ||DPS/Tank ||CC/Rgd/DPS ||11 ||Merlin ||Trinity ||Pixie ||Centaur* ||Merlin |
|Gladius ||Mbl DPS ||CC ||1 ||Pixie ||Trinity ||Gable ||Centaur ||Elemix |
|Elemix ||Util/CC/AoE ||DPS CC/DPS ||2 ||Trinity ||Pixie ||Isha ||Centaur ||Zorak |
|Pegasus ||Mbl DPS/Util ||CC/DPS/Tank ||1 ||Trinity ||Pixie ||Isha ||Centaur ||Cyclops |
|Pixie ||Mbl CC ||DPS/CC ||17 ||Dracula ||Centaur ||Trinity ||Isha ||Merlin |
|Merlin ||Rgd Util ||Mbl DPS/Rgd ||8 ||Zorak ||Dracula ||Cyclops ||Pei ||Isaac |
|Hyperia ||DPS ||+Hit/DPS/CC ||0 ||Katla ||Dracula ||Trinity ||Pixie ||Isaac |
|Aqua ||Heal/CC ||Mbl DPS ||2 ||Zorak ||Dracula ||Pixie ||Gable ||Pegasus |
|Pei ||Rgd DPS ||CC ||5 ||Pixie ||Trinity ||Zorak ||Centaur ||Cyclops |
|Katla ||Rgd DPS ||CC ||1 ||Pixie ||Trinity ||Zorak ||Centaur ||Cyclops |
|Dracula ||Mbl DPS/Tank ||CC/Tank/Non-Fem ||9 ||Centaur ||Cyclops ||Zorak ||Trinity ||Merlin |
|Trinity ||Util/CC/Heal ||Phys DPS/Util/Tank ||15 ||Isha ||Zorak ||Salvo ||Trinity* ||Kamus |
|Salvo ||Mbl Tank ||Rgd/CC ||1 ||Pixie ||Trinity ||Merlin ||Centaur ||Pei |
|Alexander ||Tank/Heal ||CC + DPS ||0 ||Pixie ||Trinity ||Dracula ||Zorak ||Drakon |
|Isaac ||Rgd DPS ||Mbl DPS/No CntCC ||5 ||Pixie ||Dracula ||Zorak ||Aqua ||Drakon |
|Gable ||Mbl DPS ||Rgd/No CntCC ||2 ||Cyclops ||Pixie ||Dracula ||Merlin ||Aqua |
|Drakon ||Mbl DPS ||CC/Psn Imm/Tank ||2 ||Pixie ||Trinity ||Zorak ||Merlin ||Cyclops |
|Isha ||Tank/DPS ||CC/Rgd ||5 ||Pixie ||Centaur ||Merlin ||Cyclops ||Pei |
|Farand ||Tank ||CC/DPS ||0 ||Pixie ||Trinity ||Dracula ||Centaur ||Pei |
|Pyre ||Mbl Tank ||CC ||0 ||Pixie ||Trinity ||Centaur ||Gable ||Elemix |
* These heroes counter themselves - whoever gets the first hit usually "wins" the battle, due to their unique mechanics. Cyclops can counter himself by being a "tank" to take the enemy Cyclops' hits, wasting his turn 2 and turn 5 attacks, etc. "# of Counters"
refers to how many times that hero counters other heroes on this list, not how times they are countered. It's hard to condense complex ideas, haha.
FAQ: Who are you? And why should I listen to you? I'm on the "Thea" server in the game, consistently ranked 1 or 2 in Arena, second highest damage to guild boss on my server (barely!!!) at ~700k (my guild boss heroes are mostly 3
* and 4
*, and our only Elemix left the game at level 55). Our server is fairly new, so there are quite a few players who have played longer than me. However, I believe that my experience is enough to be fairly accurate in my assessment, I pay strong attention to this kind of stuff. Keep in mind that this is only MY experience, though - I have used Trinity and Pixie extensively because I love their playstyle, for instance, so I'm likely biased towards that. There are a lot of other styles that work that I haven't dabbled in. I've also played Heroes of M&M for years (what this game is based off of), though it doesn't translate directly, the strategy portion of that game does apply here in a general way. Are there any "general" rules for countering? Sure. Each has its own caveat, obviously, but the rock paper scissors of Heroes Tactics works something like this - Mobility counters Ranged, Ranged counters Slow, and Slow counters Mobility. Slow heroes are often strong, they just lack the mobility to get where they need, and since mobility heroes usually have weak defenses (even Dracula), they will move into slow heroes and get murdered by them. Ranged can get free hits on slow heroes, whereas mobility heroes can move in much more freely. 3-movement heroes are a tossup, and generally aren't that great unless they have some form of awesome CC (like Trinity). Even though Zorak is 3-movement, he is considered mobility due to his round 1 and ult. If you think about it, very few 3-movement, non-ranged heroes are seen regularly in arena. I find that interesting, personally. Why isn't [Hero] on here? I've only included heroes commonly used in Arena, who rated a "7" or better on the arena tiering in the link at the top of this post. If you have a strong argument for a hero, please provide it and I can revise/add to this list. I know a lot of you are Grump/Ives enthusiasts for instance, but I don't know that playstyle at ALL, so if you have some info on them I'm more than happy to add them. I disagree with something on here! Fantastic! Voice your opinion, provide an argument, and if I can see the logic I will modify the list. Don't be offended if I don't agree (I'm pretty bull-headed, fair warning) - this is "my" counter list, more or less, and you are welcome to create your own! I will never attempt to minimalize what your opinions are, and welcome everyone to read through the comments - don't just take my word for it! This may be my list, but it's here to facilitate discussion between the community. Why aren't the Dolphin Pond heroes and/or Farand and Isaac more prominent on here? I thought they were OP? They are ridiculously OP. I've tried to exclude them wherever possible, though - if 6 counters exist, I've used the F2P heroes before the P2W heroes. Some heroes, however, are just ridiculously countered by Isaac (Helena, Merlin, etc.), so he gets a bit more representation - don't think that that makes him unstoppable, he's really vulnerable to his counters and is pretty fragile. He's EXTREMELY expensive to star-up as well - around $1,500 (in crystals) total if you are depending on only pulling him from 10-wish draws from the Mermaid pond! Great hero, but not really worth the cost, in my opinion, Camex/Lilith Games needs to do something about him and his sister. How important is it to "counter" the enemy heroes? There are more important things - positioning your heroes correctly by predicting what the AI will do, using strong heroes (A 3-star, blue-gold Dracula can't do nearly as much as a 5-star, purple-gold Lazarus, despite Lazarus royally sucking monkey ass and Dracula being an almost always solid choice) - but I would say it's still 25% of the battle, if not more. Understanding that Zorak will absolutely ruin a Helena/Merlin composition is important. Also understanding that Isha will MURDER Zorak if she's placed right next to Helena is important as well (hint: he won't even get the chance to ult, he will be dead by the end of round 2 unless he gets insanely lucky dodges). I have a question not related to this guide! Feel free to message me privately, I try to answer everything within a day or so. I'm on Reddit frequently.
submitted by LedgeEndDairy
to HeroesTactics [link] [comments]
2014.11.18 11:30 Phineasfogg Stat Attack: The strongest team to be relegated from the Premier League
A couple of months ago, Mightymaas
asked who the best team ever to get relegated from the Premier League was
. I don't know about the best, as that's a more subjective assessment, but we can certainly take advantage of the international break to cast a more objective eye over the Behemoths of the bottom three.
Points Make Prizes
Seems obvious, right? If we look at all the relegated teams and see who had the most points, they'd have a good shout to being the strongest. For ease of comparison, I'm excluding the 22 team 92-95 era of the Premier League (as it ups the total matches/points available to teams), no doubt to the sweet relief of Palace fans whose club spent that period doing their bit for the 90s yo-yo-ing craze. I should also add that I've restored the 9 points Pompey were docked in 09/10, benevolent OP that I am, as we're exclusively in the business of what teams did on the pitch not their financial failings off it. For similar reasons, on which I'll go into more detail later, I've also restored the three points Middlesbrough were docked for cancelling their fixture against Blackburn in 96/97. EXPAND for a chart of the pointiest relegated teams
In the runners-up position of our little time-travelling league of despair, losing out (in true bottom three fashion) on goal difference, we find Glenn Roeder's 02/03 Hammers team, featuring the talents of Paolo Di Canio and Joe Cole. For some context here, in no other season would West ham have gone down with 42 points; on average, that would net you 15th place, with a high of 13th and a low of 17th. It should come as no wonder that such cosmic injustice would see Roeder bust a blood vessel in his head, leaving Trevor Brooking to win the final three games and still go down. EXPAND for a team photo of our hero Hammers
That leaves the winners, by way of heavy asterisk, as Middlesbrough's 96/97 squad. Now is probably a good time to talk about that points deduction because it has a significant impact on our relegation battle royale. On the fateful day of December 20th 1996, Bryan Robson surveyed the first team squad, which meant that he also surveyed himself, for Bryan Robson was player-manager of Middlesbrough Football Club, and Bryan Robson did not like what he saw. The team had enjoyed a successful 95/96 season, finishing 12th in the Premier League after a two year hiatus that had seen them move into a new, state-of-the-art arena. The chairman was bullish and Bryan Robson had used the resulting transfer war chest to secure the services of Fabrizio Ravanelli, who had scored in Juventus's Champions League final that year, and Porto's Emerson, who joined his compatriot Juninho at the club. But in the cold and in the winter, Middlesbrough Football Club found themselves in 15th place in the English Premier League without a victory in 13 games, two points ahead of the relegation zone and two points ahead of Blackburn Rovers, against whom they were scheduled to play in less than 24 hours. Bryan Robson surveyed his first team squad and Bryan Robson cursed the players who had picked up needless bookings and were sitting out suspensions. He cursed the injuries that had sidelined players that otherwise went straight on the team sheet, players like Juninho, injured and unavailable. Above all though, Bryan Robson cursed the flu epidemic that had scythed through his squad and left him with just seven remaining players fit and available to play a relegation six pointer at Ewood Park on Saturday against Blackburn Rovers. So it was that Bryan Robson took himself took himself to Keith Lamb, Chief Executive of Middlesbrough Football Club, and told him he could not field a side that weekend. A fax was despatched to the headquarters of the Premier League and Bryan Robson and Keith Lamb decided that Middlesbrough Football Club would not travel to Blackburn.
Blackburn learned from Sky Sports News that the fixture had been postponed and Blackburn were not impressed. The League was not impressed and convened a panel in January, which determined that the match would replayed but that Middlesbrough, despite all their swagger, despite all their flair, would be fined £50,000 and docked three points. Three crucial points. This, it turned out, was the only permutation of events that would see Boro's late season rally fall agonisingly short of safety. If the match had been forfeited 3-0, as Blackburn had demanded, Boro would have finished the season on 41 points and survived. If Boro had fielded a reserve team and lost 7-0, Boro would have stayed up on goals scored. If this does not seem like the wrath of some vindictive god then know also that Boro reached the finals of both the FA Cup and the League club in the same season, losing both (the latter on a replay) in what might be the first and only relegation treble in football. EXPAND for a team photo of Middlesbrough's team in healthier times
The point, or rather the absence of three of them, is that Boro were relegated solely by virtue of that deduction, meaning that for the purposes of our investigation, their form and restored points score are really those of an unrelegated side. All of which only highlights the staggering achievement of the 02/03 Hammers squad, who finished with an identical number of wins, draws and losses and still went down the right way.
A Better Method?
Now this is all good and well, but I think we can do better and I think we owe it to these two titans of turmoil to put this one to bed. As mentioned above, both teams won more games than your usual relegation contender: EXPAND for a chart of the winningest relegated teams
And given that the West Ham board is big on entertainment value these days, we can ask whose fans were most entertained by their team's goal-scoring feats during their plummet into the abyss. Somewhere last season's Sam Allardyce is laughing behind the wheel of his bus, because our 02/03 side of colossuses drop to 10th by that metric: EXPAND for a chart of the goaliest relegated teams
Some Points Are Better Than Others
There's clearly only one way to settle this — we have to ask ourselves whose points were the harder won, the more unexpected, in other words, whose points came from tougher opposition. American sports fans will doubtless be familiar with the depths of this particular rabbit hole, but for the sake of data-wrangling ease, I'm going to be using a slightly statistically naive approach predicated on the following assumptions: #1
The more points a team finished with, the harder they were to beat #2
Beating a highly placed team is worth more than beating a struggler #3
Seasons continue to be directly comparable, on the basis that there's still the same number of base points on offer.
So we'll adjust the points on the following basis: - A victory is worth your opponent's final points tally - A draw is worth a third of your opponent's final points tally
For example, looking at last season's results, beating Liverpool would be worth 84 points, while beating Cardiff would net you only 30 points. Equally, a draw between Liverpool and Cardiff would result in 10 points for Liverpool, while Cardiff would take 28 points, reflecting their opponent's higher degree of difficulty. All fractions are maintained until the final total, which is rounded for visual clarity. An interesting picture emerges.. EXPAND for a chart of the strongest relegated teams
West Ham emerge victorious by a slender one point margin, which means that Hammers fans can now unequivocally claim to be the strongest team to have been relegated from the Premier League, displaying stronger form than even a Boro team that would have stayed up without receiving a points deduction. Alas, this crown is still a crown of shame, for despite these relegation heroics, if we resorted the Table by Adjusted Points, West Ham would still have finished where they did in 18th place. In the 20 team era, there are only five teams whose strength outperformed unrelegated rivals and so it follows that there are also five teams whose survival we might call into question. Let us take a moment to reflect on what might have been.. EXPAND for a chart of the teams that were robbed and the teams that robbed them
Even if Mightymaas
now has an answer to his question, we've come too far not to address the burning question: the bottom of the bottom. On that one all
statistical measures are in agreement, but for the sake of maximal proof here's the adjusted points verdict: EXPAND for a chart of the weakest teams to have graced the Premier League
Ladies and Gentleman, I'd like to raise a toast to Derby County's record-breaking 07/08 team, managed by Billy Davies and Paul Jewell in separate stints and captained by footballing titan (and January arrival) Robbie Savage. With no serious challenger in sight for this honour, even if you factor in points deductions, the Rams took the opportunity to really run up scoreboard in all kinds of areas. Fans with no hands, for instance, would nevertheless be able to count Derby's total number of wins on one stump and still have a whole stump left over in case the return fixture against Newcastle proved similarly fruitful (it didn't). Meanwhile, three players that year — Ronaldo, Torres and Adebayor — scored more goals individually than Derby managed as a team, while the Rams defence hosted a season-round goals bonanza for the rest of the league, shipping a still unbeaten 89 goals. With both attack and defence over-delivering like that, Derby also set a huge marker for goal difference with -69, a total that remains 24 goals ahead of their nearest rival in the 20-team era.
Alas, photographic testimony to this unique combination of footballing talents has been all but erased from the internet, leaving just this poor scan of a Merlin sticker to remind us of the team that gave and gave again and would give some more in the last minute if you hadn't taken what they were giving. I give you Derby County's 07/08 squad, objectively the worst team to have waged a Premier League campaign: EXPAND for Derby's conquered 07/08 heroes Sources Tables: statto.com Results: football-data.co.uk
EDIT: Bonus Features Check back here for some extra charts that go beyond the bottom three Top 20 strongest teams Strongest to Weakest Performances per League Finish 1-10
By Suggestion of freetambo: All hail the underachievers — the teams that beat the best but not the rest And a pox upon the houses of the overachievers — the teams that coasted
Adjusted League Tables (includes Champions League switcharoos): 2013/14 Table aka the year Chelsea threw it away 2012/13 Table aka Tottenham in Europe 2011/12 Table aka Baggies brought down to earth 2010/11 Table 2009/10 Table aka Everton beat Liverpool to the last UEFA Cup spot 2008/09 Table aka Liverpool champions, Sunderland down
submitted by Phineasfogg
to soccer [link] [comments]