level and variability

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Low-Level Wind Variability over the Indochina Peninsular during Boreal Winter

Low-Level Wind Variability over the Indochina Peninsular during Boreal Winter

This study uses the VEOF method to decompose low-level wind over the Indochina peninsular into spatial modes and corresponding temporal variations. The first mode (VEOF1) explains 46.58% of the total variance. In addition to positive and negative phases of PC1, southwesterlies prevail over northern part of Thailand, Laos, and Viet Nam in 1998 with appearance of negative phase. Whereas easterlies prevail over southern part of the Indochina region, and extend to cover the interior of the region when the positive phase exhibits in 1999. It implies to low-level wind variability over the Indochina peninsular gets more (less) influence of northeasterlies during appearance of positive (negative) phase during the boreal winter.
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Therapist and clinic effects in psychotherapy: a three-level model of outcome variability

Therapist and clinic effects in psychotherapy: a three-level model of outcome variability

We know of only one previous study that has estimated the size of clinic effects using MLM, finding a smaller but broadly comparable effect of 1.8%, in a 2-level model using a different outcome measure and dataset (Pybis et al., 2017). Approximately half of the unadjusted clinic effect was explained by patient-level severity and employment status, suggesting a selection effect. This has important implications for healthcare providers using pay for performance (a.k.a. outcomes-based) payment models. Our study found that a considerable additional amount of variability between clinics was explained by two clinic level variables. These clinic level variables were the clinic sector and the percentage of a clinic’s population who were White English/European. Compared to other sectors, treatment in a secondary care clinic was associated with poorer outcomes. Secondary care clinics tend to work with patients with more complex or treatment-resistant difficulties that may not have been fully captured in the available variables. The second clinic level variable, the percentage of a clinic ’ s population that were White English/European, was a more surprising finding, particularly as it was in addition to an individual patient’s ethnic origin and explained more of the outcome variance.
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Sea level variability: examples from the Atlantic coast of Europe

Sea level variability: examples from the Atlantic coast of Europe

The North Atlantic Oscillation (NAO) variability has been the focus of much recent attention, because of its relationship to the European climate (Hurrell, 1995) and as a major forcing parameter for sea level variability (Tsimplis et al., 2005). For example, Tsimplis & Josey (2001) have described how the inverted barometer effect, linked to the NAO, is the mechanism responsible for sea level variability in the Mediterranean Sea. Wakelin et al. (2003) used a two-dimensional model of tides and storm surges, to study the connection between changes in the NAO and sea levels over the northwest European continental shelf for 1955-2000. These investigations found a clear spatial pattern in the correlation between sea level and the NAO, on a winter-mean timescale. This correlation was positive in the northeast and negative in the south, where most of the sensitivity is also in the non-hydrostatic component of sea level. These results have been confirmed by Woolf et al. (2003) who, using tide gauge data and altimeter data, found a positive sensitivity to the NAO index in the North Sea and Baltic regions. This is caused mainly by increased westerly winds and a negative sensitivity in the north eastern coastline of America. However, this effect is only significant around Vigo and in the western Mediterranean and Adriatic. A negative response was also found in the southern west part of the U.K. Further to these results, Yan et al. (2004) have found unusually strong and weak annual cycles in sea level, which tend to lag those in the NAO by a month for most sites analysed. Such a lag suggest that sea level does not respond simply to pressure forcing, but is additionally responsive to changes in thermal conditions and fluxes, associated with the NAO.
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Sea level trend and variability around Peninsular Malaysia

Sea level trend and variability around Peninsular Malaysia

For interannual variability of sea level around Peninsular Malaysia, tide gauge records and satellite altimetry are com- pared in Fig. 3. Some differences between the two data sets are expected due to the following reasons. First, the coarse spatial resolution of AVISO gridded data is 1/4 ◦ × 1/4 ◦ , whilst tide gauge records reflect local features (e.g. bathymetry, riverine discharges, and coastal dynamics). Sec- ond, annual mean sea level values from tide gauges are mainly deduced from hourly records, whilst those from grid- ded altimetry products are derived from smoothed data hav- ing the temporal interval of a day. Lastly, some uncertain- ties and biases could stem from the fact that the data sources (AVISO and PSMSL) use different instruments (satellite al- timeter versus mareograph) and are post-processed by differ- ent de-tide (harmonic analysis) tools. In spite of these differ- ences, both data sources show reasonable agreement in the region with respect to the patterns of interannual variability (Fig. 3).
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Locational marginal price variability at distribution level : a regional study

Locational marginal price variability at distribution level : a regional study

Abstract— As distribution systems move towards being more actively managed there is increased potential for regional markets and the application of locational marginal prices (LMPs) to capture spatial variation in the marginal cost of electricity at distribution level. However, with this increased network visibility can come increased price volatility and uncertainty to investors. This paper studies the variation in LMPs in a section of the south west of England distribution network for current and future installed capacity of distributed generation. It has been shown that in an unconstrained network, spatial LMP variation (due to losses) is minimal compared to the temporal variation. In a constrained network, a significant increase in LMP volatility was observed, both spatially and temporally. This could bring risk for generators particularly if they become stranded in low price areas, or flexible demands facing a drop-off in return when constraints are removed. Index Terms—Locational, Marginal, Pricing, LMP, Distribution, Volatility, Variability
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Sea level trend and variability in the Singapore Strait

Sea level trend and variability in the Singapore Strait

Similar or larger recent MSL rise rates have been observed in the western tropical Pacific, eastern Indian and South- ern Ocean (Carton et al., 2005; Cheng and Qi, 2007; Un- nikrishnan and Shankar, 2007). Church et al. (2004, 2006) have analysed the sea level rise in the region bounded by 40 ◦ S to 40 ◦ N and 30 ◦ E to 120 ◦ W (centered on Sunda Shelf), and found that the average upward trend for the pe- riod 1950 to 2001 was about 2 mm yr −1 ; in particular, the re- gion encompassing SS the MSL rise during the period 1955– 2003 by Church et al. (2004) is 3.6 mm yr −1 , and for the ten year period after 1993 the regional sea level rise rate was about 4 mm yr −1 . For the Singapore area, the earlier rate was similar, but the later trend is somewhat higher, centered at 5 mm yr −1 . The larger regional trend since 1993 in the tropi- cal Pacific and Indian Oceans has been attributed to consider- able interannual and decadal sea level variability associated, respectively, with the ENSO and the North Pacific Decadal
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Sea level trend uncertainty with Pacific climatic variability and temporally correlated noise

Sea level trend uncertainty with Pacific climatic variability and temporally correlated noise

Abstract Recent studies have identified climatic drivers of the east-west see-saw of Pacific Ocean satel- lite altimetry era sea level trends and a number of sea-level trend and acceleration assessments attempt to account for this. We investigate the effect of Pacific climate variability, together with temporally-correlated noise, on linear trend error estimates and determine new time-of-emergence (ToE) estimates across the Indian and Pacific Oceans. Sea-level trend studies often advocate the use of auto-regressive (AR) noise mod- els to adequately assess formal uncertainties, yet sea level often exhibits colored but non-AR(1) noise. Stan- dard error estimates are over- or under-estimated by an AR(1) model for much of the Indo-Pacific sea level. Allowing for PDO and ENSO variability in the trend estimate only reduces standard errors across the tropics and we find noise characteristics are largely unaffected. Of importance for trend and acceleration detection studies, formal error estimates remain on average up to 1.6 times those from an AR(1) model for long- duration tide gauge data. There is an even chance that the observed trend from the satellite altimetry era exceeds the noise in patches of the tropical Pacific and Indian Oceans and the south-west and north-east Pacific gyres. By including climate indices in the trend analysis, the time it takes for the observed linear sea- level trend to emerge from the noise reduces by up to 2 decades.
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Concepts and Terminology for Sea Level: Mean, Variability and Change, Both Local and Global

Concepts and Terminology for Sea Level: Mean, Variability and Change, Both Local and Global

In the next section, we outline the conventions and assumptions we use in our defini- tions and mathematical derivations. The following three sections (Sects. 3–5) contain the definitions, with a subsection for each major term defined, labelled with ‘‘N’’ and num- bered consecutively throughout. In Sect. 3 we define five key surfaces: reference ellipsoid, sea surface, mean sea level, sea floor and geoid. We consider the variability and differences in these surfaces in Sect. 4, and quantities describing changes in sea level in Sect. 5. In Sect. 6, we show how relative sea-level change is related to other quantities in various ways. In Sect. 7 we describe how observational data are interpreted using the concepts we have defined. To facilitate sequential reading of this paper, the material of Sects. 3–7 is arranged to minimize forward references, though we were unable to avoid all.
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Fighting stochastic variability in a D-type flip-flop with transistor-level reconfiguration

Fighting stochastic variability in a D-type flip-flop with transistor-level reconfiguration

This paper has investigated the application of multi-objective evolutionary optimisation on recon fi gurable hardware for recovering/improving the performance of a DFF mapped onto it that is degraded because of stochastic variability. There are two novel aspects to this work: fi rst, the novel recon fi gurable PAnDA architecture has been used to implement the DFF design. PAnDA is a hierarchical architecture, comprising of CTs, CABs, CLBs and interconnect. At the CLB and interconnect level, PAnDA is compatible with commercial FPGA architectures. However, PAnDA offers additional lower levels of recon fi guration (CAB and CT levels), which allows the optimisation of electronic designs at a smaller granularity. The lowest analogue level is represented by the CTs, which are used in the case study presented here. Second, multi-objective evolutionary optimisation has been successfully applied – working at the analogue recon fi guration level of PAnDA – to recover and optimise the performance of a DFF where the performance was degraded because of stochastic variability. It has been shown that timing can be signi fi cantly improved in exchange of a relatively small increase in power consumption. The results suggest that this kind of multi-recon fi gurable architecture, which allows the optimisation performance at both the analogue and the digital level, has great potential to enhance current standard fi eld-programmable digital devices, such as FPGAs, with post-fabrication optimisation capabilities. Note that optimisation goals can be de fi ned by the user and thus include manipulating a circuit for a desired operating point and recovering yield as well as increasing robustness against silicon substrate variations, which makes the approach highly fl exible.
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The inter‐annual variability of southerly low‐level jets in North America

The inter‐annual variability of southerly low‐level jets in North America

ABSTRACT: The interannual variability of southerly low-level jets (SLLJs) over North America during the warm (April-September) and cool (October-March) seasons is investigated. SLLJ occurrences over a 31-year period (1979-2009) were identified from the North American Regional Reanalysis (NARR) vertical wind profiles. The first empirical orthogonal function (EOF) modes of the SLLJ frequency during the warm and cool seasons account for about 30% and 20% of the total variance, respectively. Both modes can be interpreted as a strengthening or weakening of the core area of SLLJ anomalies. The principal component (PC) time series display significant positive trends, suggesting an increase in SLLJ activity during both seasons on interdecadal time scales and are significantly correlated to the summertime Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) for the warm season and the wintertime PDO, AMO and El Niño Modoki for the cool season. The second modes account for about 20% and 15% of the total variance for the warm and cool seasons, respectively, and are interpreted as primarily a subseasonal latitudinal shift in SLLJ activity between the central Great Plains and the western Gulf of Mexico and southern Texas during the warm season and a longitudinal shift between the western Gulf of Mexico and the Caribbean during the cool season. The second mode appears to be significantly correlated to El Niño Modoki for the warm season and to Niño 3.4 for the cool season.
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The inter‐annual variability of southerly low‐level jets in North America

The inter‐annual variability of southerly low‐level jets in North America

Significant correlations with the time series of PC1 are found for several of the teleconnection indices (Table 2). Correlations with the NAO are particularly large for January and March when the correlation coefficients are −0.43 and −0.46, respectively, significant at the 95% confidence level. Significant negative correlations between PC1 and the PDO index are found in October (−0.37), February (−0.44), and March (−0.53), whereas the most significant correlations with the El Niño Modoki index occur in January (−0.39), February (−0.46), and March (−0.37). PC1 is correlated significantly with the Niño 3.4 index in February and March with coefficients of −0.36 and −0.42, respectively. In contrast, strong positive cor- relations are observed between PC1 and AMO for all months in the cool season, with large correlation coeffi- cients of 0.56–0.67 for October through January. For PC2, the strongest correlation is found with Niño3.4 with corre- lation coefficients of 0.6–0.7 across the months during the cool season. PC2 is also significantly correlated with PNA in January through March. In addition, PC2 has a signifi- cant positive correlation with PDO and AMO in February and March and a negative correlation with NAO in Febru- ary. In sum, the inter-decadal variability and trend of the cool-season PC1 is related to the AMO, PDO, and El Niño Modoki. But, unlike the warm season, the inter-annual variability of the cool-season PC2 is influenced by ENSO. The PC time series of the first two EOF modes were regressed with the time series of the cool-season anomalies of SST, H200, H925, and 925 hPa winds, with the results shown in Figure 7. The regression maps for PC1 show sim- ilar patterns to those found for the warm season, which is not a surprise given the significant correlations with PDO and AMO for both seasons. The H200 coefficients display a distinct wave pattern (Figure 7(a)), suggesting that positive values of PC1 are associated with positive H200 anomalies over the north central Pacific Ocean (cen- tred around 40 ∘ N and 170 ∘ W), the southwestern United States (at approximately 35 ∘ N and 110 ∘ W) and north- eastern North America, Greenland, and the western North
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Exploring Spatial Variability in the Relationship between Long Term Limiting Illness and Area Level Deprivation at the City Level Using Geographically Weighted Regression

Exploring Spatial Variability in the Relationship between Long Term Limiting Illness and Area Level Deprivation at the City Level Using Geographically Weighted Regression

Table 3 shows that the effect of the covariates varies greatly across the study area. However, it is essential to map the local parameter estimated to observe where there is significant spatial heterogeneity between the independent and dependent variables. Figure 3 presents the variability for the intercept, and the significant (p < 0.05) coefficients for area level deprivation, the percentage of people aged 75 years plus and the percentage of the population of white ethnicity on LLTI at the LSOA level for Liverpool. Natural breaks were used to classify each category. From Figure 3(a) it can be observed that higher intercept values for LLTI are located in the North, particularly the North East of the city. This spatial trend implies that once spatial variations in the three explanatory variables in the model have been accounted for, rates of LLTI are higher in the North East of Liverpool. The relationship between LLTI and IMD score is shown in Figure 5(b). For area level deprivation only a small cluster of LSOAs has a non-significant effect on LLTI outcomes in Liverpool and similar to the OLS model all associations are positive. Figure 3 also indicates that most of central Liverpool reports significant coefficient greater than the global coefficient calculated by the OLS model. However, the magnitude of this association varies across the city. The highest deprivation coefficients (0.23–0.31) are observed in two areas, the East of Liverpool and around the city centre. This indicates that in these two areas higher IMD scores are related to increased LLTI rates compared to the South and North of the city. Continuing the analysis, Figure 5(c) presents the effects of the percentage population greater than 75 years old and LLTI. Similar to the deprivation coefficient there is significant geographic variation in the relationship between the increase in the percentage of the individual‟s aged 75 plus and LLTI across most of the study area. Controlling for deprivation and ethnicity, the highest effect is found in the North West of Liverpool. Again the effect of the age is greater in these areas than the average age coefficient (0.79) calculated by the global OLS model.
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Dispersal variability and associated population-level consequences in tree-killing bark beetles

Dispersal variability and associated population-level consequences in tree-killing bark beetles

We performed two different levels of analyses, the popu- lation level and the individual level. Firstly, we assessed the effect of within-population variability on dispersal success by modifying two beetle-related physiological in- put parameters, initial energy level and efficiency, which in combination determine the flight capacity (Table 1). Whereas variations in energy level are partly dependent on external factors such as host quality or colonization density, efficiency variations are assumed to be popula- tion intrinsic. In order to reveal potential effects of indi- vidual variability on population-level dispersal success, the default set-up, which includes realistic assumptions concerning both parameters (S0), was compared to two artificial scenarios with partially (S1) or completely switched-off individual variability (S2). In S1 only vari- ability in efficiency was switched-off, i.e., set equal for all individuals to the mean value of the original distribution, whereas for energy level the default Gaussian distribution was used. In S2 any variability was completely switched- off by using the mean values of both parameters for each individual. As global output for population dispersal suc- cess the percentage of beetles which successfully infested a host was recorded. For simplicity, we omitted additional scenarios where variability is reduced in a more gradual way and focussed on these extreme cases (switched-on/off variability) instead. In order to prove robustness of the re- sults we carried out sensitivity analyses where different settings of beetle flight capacity (energy level, efficiency) and environmental heterogeneity in scattered habitats (p, PA) were tested (Additional file 3).
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Consequences of sea level variability and sea level rise for Cuban territory

Consequences of sea level variability and sea level rise for Cuban territory

Abstract The objective of the present paper was to determine a first approximation of coastal zone flooding by 2100, taking into account the more persistent processes of sea level variability and non-accelerated linear sea level rise estimation to assess the main impacts. The annual linear rate of mean sea level rise in the Cuban archipelago, obtained from the longest tide gauge records, has fluctuated between 0.005 cm/year at Casilda and 0.214 cm/year at Siboney. The main sea level rise effects for the Cuban coastal zone due to climate change and global warming are shown. Monthly and annual mean sea level anomalies, some of which are similar to or higher than the mean sea level rise estimated for halfway through the present century, reinforce the inland seawater penetration due to the semi-daily high tide. The combination of these different events will result in the loss of goods and services, and require expensive investments for adaption.
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Nutrient and Chlorophyll α Variability at a Micro Scale Level in a Suspended Mussel Culture

Nutrient and Chlorophyll α Variability at a Micro Scale Level in a Suspended Mussel Culture

Mussel farming by the long-line system, in the shallow waters of the NW Thessaloniki Gulf, Greece, is a vital economic activity for the local communi- ties. The management practices play an important role both in the environ- mental quality and the support of the healthy growth of mussels. Αn experi- mental line of mussels in suspension placed at different sock distances as a management practice was systematically monitored for nutrients, chlorophyll α and dissolved oxygen. The study at four different mussel densities (distances of the socks) lasted from July 2014 to April 2015, covering the growth, repro- duction and harvest cycle of mussels. Additional sampling took place in two selected sock distances, 30 and 70 cm, in the second sampling period, May-August 2015. The variability of nutrients along with chlorophyll α and dissolved oxygen, seasonally, spatially and vertically, was examined with the application of multivariate statistical analysis. The results showed low varia- tion of nitrates among the sites but statistically significant differences of dis- solved oxygen, ammonium, phosphate and chlorophyll α . The application of environmental indicators (TRIX, EI) in the data set was a useful tool in the identification of different variation schemes of the measured parameters in the cultures of various mussel densities.
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How players exploit variability and regularity of game actions in female volleyball teams

How players exploit variability and regularity of game actions in female volleyball teams

Therefore, the aims of this study were twofold. First, we sought to analyse and compare the variability (here defined as an indicator of unpredictability and flexibility of tactical behaviours) observed in tactical performance within complex I (setting conditions, attack zone, attack tempo and also block opposition) of volleyball teams differing in expertise levels. Second, we sought to examine whether variability of these performance indicators may have been influenced by contextual constraints of each set (e.g., non-deciding and deciding sets in competition) and of set periods (i.e. initial and final periods in a set) within a competitive game. We hypothesised that, compared to less experienced (national level) performers, more experienced (elite level) volleyball players would display: (i) greater variability of actions in zones and tempos of attack; and (ii), greater regularity in the setting conditions and block opposition. In decisional set and also during final set periods, it was expected that such differences would be exacerbated between the two groups.
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Climate Change Impact: Food Production and Local Perception

Climate Change Impact: Food Production and Local Perception

found that due to change in climate there is erratic rainfall, climate is getting warmer day by day due to increase in temperature, erosion, flooding incidents and natural disaster (Table 2). Kaul and Ram [19] found that excessive rains and extreme variation in temperature has adversely affected the crop productivity, thereby this has affected the incomes as well as food security of farming families in Karnataka, India. 85 % of the respondents were agreed that increase in temperature and hence adversely affect in agriculture production. Increasing temperature will increase evaporation, soil and water temperature that enhance incidence of insect pest, disease and less fertile the agriculture lands [20]. The study confirms that people of the affected area experienced that salinity into land mass increased less fertile that causes failure of crop production [21]. Another study confirms that food production was decreased due to salinity and climate change [22]. Socio-economic factors affected in economy like age, sex, occupation, income level of the population; increasing demand of food grain product; rising price of food grains; low productivity of land, and low education level of farmers; and climate change and it variability [23].
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STUDIES ON ADRENOCORTICAL EOSINOPENIA: A CLINICAL AND STATISTICAL EVALUATION OF FOUR HOUR EOSINOPHIL RESPONSE TESTS

STUDIES ON ADRENOCORTICAL EOSINOPENIA: A CLINICAL AND STATISTICAL EVALUATION OF FOUR HOUR EOSINOPHIL RESPONSE TESTS

Examples of variable responses in individual patients Examples of the variability of the responses to the different tests are given below: The eosinophil level of a patient with refracto[r]

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Variability and nutritional balance among genotypes of Coffea canephora (Rubiaceae) in drought versus adequate water supply

Variability and nutritional balance among genotypes of Coffea canephora (Rubiaceae) in drought versus adequate water supply

Accumulation of biomass in the aerial part of genotypes 89, 155, 184, 189 and 203 did not differ with the change in water supply. For the others, the lower water supply caused losses between 9 and 24% in the accumulation of biomass in the aerial part. Similar to leaf biomass, the lower water supply also caused a decrease in the observable differences among genotypes for ADM. While three different groups were observed under the condition of higher water supply, only two were differentiable with low water supply. Genotype 184 also presented the lowest accumulation of biomass in the aerial part under both conditions of water supply; while genotypes 61, 88, 120, 125, 130 and 203 grouped together with higher shoot biomass regardless of the level of water supply (Table 2). Overall, the limitation caused by the restriction in water supply caused losses between 8% and 29%in total dry matter; this negative effect was only not observed for genotypes 61, 89, 130 and 189.
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Genetic variability and expression of agro-morphological traits among genotypes of Coffea arabica being promoted by supplementary irrigation.

Genetic variability and expression of agro-morphological traits among genotypes of Coffea arabica being promoted by supplementary irrigation.

The experiment was developed in competition field, installed in a region where Arabica coffee (C. arabica L.) is typically cultivated, located in the countryside of the municipality of Alegre, Espírito Santo State, Southeast Region of Brazil (20°52'07''S and 41°28'43''W). The area has elevation of 642 m over sea level, the average air temperature of the region during the study was 20.85°C and annual accumulated rainfall was 1290 mm, with the rainy season from October to April and the dry season from May to September.

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