FRACTALITY OF MONTHLY EXTREME MINIMUM TEMPERATURE

Fractals ◽  
2003 ◽  
Vol 11 (02) ◽  
pp. 137-144 ◽  
Author(s):  
RICARDO DAVID VALDEZ-CEPEDA ◽  
DANIEL HERNÁNDEZ-RAMÍREZ ◽  
BLANCA MENDOZA ◽  
JOSÉ VALDÉS-GALICIA ◽  
DOLORES MARAVILLA
Keyword(s):  
Minimum Temperature ◽  
Missing Values ◽  
Fractal Dimensions ◽  
Data Set ◽  
Power Spectral ◽  
Extreme Minimum Temperature ◽  
Scale Range ◽  
Complete Series ◽  
Near Future

Interest in climate change has increased over the last 30 years due largely to global predictions associated with the greenhouse effect, which appear to lead to a substantial increase in planetary temperature. Implications of such results have led many scientists to examine climatic records from different regions of the world in order to understand temperature behavior. However, many researchers have noted that changes in temperature variability are also important in determining the future temperature distributions. In this context, we have analyzed a long-term record of monthly extreme minimum temperature registered at Guanajuato, Mexico. Data set was treated as a fractal profile to estimate the fractal dimension through variography (Dv) and power-spectral (Ds) approaches under two situations: (1) complete series, from January 1895 to December 1997 with 312 missing observations, and (2) partial series, from January, 1921 to April, 1963 with no missing values. In both cases, we obtained similar values for the two types of fractal dimensions meaning there is not a significant effect of missing values. The estimated fractal dimensions for the partial series (508 observations) are near 1.5 (Dv = 1.445 ± 0.06, Ds = 1.486 ± 0.155), which means monthly extreme minimum temperature is almost equally characterized by both short- and long-range variations. Evaluating through scaling arguments did not evidence multifractality in the scale range of two to 254 months. Then interpolation can make use of the fact that monthly extreme minimum temperature has a power-law spectrum. Interpolated data generated by this way may develop greater confidence in their capability to forecast near future climate.

ON THE APPLICATION OF METHODS USED TO CALCULATE THE FRACTAL DIMENSION OF FRACTURE SURFACES

Fractals ◽  
2001 ◽  
Vol 09 (01) ◽  
pp. 105-128 ◽  
Author(s):  
TAYFUN BABADAGLI ◽  
KAYHAN DEVELI
Keyword(s):  
Fractal Dimension ◽  
Fractal Dimensions ◽  
Natural Fracture ◽  
Data Sets ◽  
Data Set ◽  
Fracture Surfaces ◽  
Power Spectral ◽  
Data Points ◽  
2D Data ◽  
Fracturing Mechanism

This paper presents an evaluation of the methods applied to calculate the fractal dimension of fracture surfaces. Variogram (applicable to 1D self-affine sets) and power spectral density analyses (applicable to 2D self-affine sets) are selected to calculate the fractal dimension of synthetic 2D data sets generated using fractional Brownian motion (fBm). Then, the calculated values are compared with the actual fractal dimensions assigned in the generation of the synthetic surfaces. The main factor considered is the size of the 2D data set (number of data points). The critical sample size that yields the best agreement between the calculated and actual values is defined for each method. Limitations and the proper use of each method are clarified after an extensive analysis. The two methods are also applied to synthetically and naturally developed fracture surfaces of different types of rocks. The methods yield inconsistent fractal dimensions for natural fracture surfaces and the reasons of this are discussed. The anisotropic feature of fractal dimension that may lead to a correlation of fracturing mechanism and multifractality of the fracture surfaces is also addressed.

scholarly journals Global plant hardiness zones for phytosanitary risk analysis

2008 ◽  
Vol 65 (spe) ◽  
pp. 54-59 ◽  
Author(s):  
Roger D. Magarey ◽  
Daniel M. Borchert ◽  
Jay W. Schlegel
Keyword(s):  
Risk Analysis ◽  
Minimum Temperature ◽  
Control Measures ◽  
Daily Minimum Temperature ◽  
United States Department ◽  
Least Squares Regression ◽  
Data Set ◽  
Monthly Data ◽  
Extreme Minimum Temperature ◽  
Plant Hardiness

Plant hardiness zones are widely used for selection of perennial plants and for phytosanitary risk analysis. The most widely used definition of plant hardiness zones (United States Department of Agriculture National Arboretum) is based on average annual extreme minimum temperature. There is a need for a global plant hardiness map to standardize the comparison of zones for phytosanitary risk analysis. Two data sets were used to create global hardiness zones: i) Climate Research Unit (CRU) 1973-2002 monthly data set; and ii) the Daily Global Historical Climatology Network (GHCN). The CRU monthly data set was downscaled to five-minute resolution and a cubic spline was used to convert the monthly values into daily values. The GHCN data were subjected to a number of quality control measures prior to analysis. Least squares regression relationships were developed using GHCN and derived lowest average daily minimum temperature data and average annual extreme minimum temperatures. Error estimate statistics were calculated from the numerical difference between the estimated value for the grid and the station. The mean absolute error for annual extreme minimum temperature was 1.9ºC (3.5ºF) and 2/3 of the stations were classified into the correct zone.

Demographic gaps between Syrian and the European populations: What do they suggest?

2017 ◽  
Vol 7 (2) ◽  
pp. 207-230 ◽  
Author(s):  
Mustafa Murat Yucesahin ◽  
Ibrahim Sirkeci
Keyword(s):  
Transition Theory ◽  
World Population ◽  
Demographic Analysis ◽  
Demographic Differences ◽  
Syrian Crisis ◽  
The United Kingdom ◽  
Syrian Population ◽  
Displaced People ◽  
Near Future

Syrian crisis resulted in at least 6.1 million externally displaced people 983,876 of whom are in Europe while the rest are in neighbouring countries in the region. Turkey, due to its geographical proximity and substantial land borders with the country, has been the most popular destination for those fleeing Syria since April 2011. Especially after 2012, a sharp increase in the number of Syrian refugees arriving in Turkey was witnessed. This has triggered an exponential growth in academic and public interest in Syrian population. Numerous reports mostly based on non-representative sample surveys have been disseminated whilst authoritative robust analyses remained absent. This study aims to fill this gap by offering a comprehensive demographic analysis of the Syrian population. We focus on the demographic differences (from 1950s to 2015) and demographic trends (from 2015 to 2100) in medium to long term, based on data from World Population Prospects (WPP). We offer a comparative picture to underline potential changes and convergences between populations in Syria, Turkey, Germany, and the United Kingdom. We frame our discussion here with reference to the demographic transition theory to help understanding the implications for movers and non-movers in receiving countries in the near future.

Configurations to Superior Environmental Innovation Strategy: A Both–And Approach

2021 ◽  
pp. 108602662110316
Author(s):  
Tiziana Russo-Spena ◽  
Nadia Di Paola ◽  
Aidan O’Driscoll
Keyword(s):  
Climate Change ◽  
Value Chain ◽  
Critical Role ◽  
Well Being ◽  
Point Of View ◽  
Future Generations ◽  
Innovation Strategy ◽  
Environmental Innovation ◽  
Data Set

An effective climate change action involves the critical role that companies must play in assuring the long-term human and social well-being of future generations. In our study, we offer a more holistic, inclusive, both–and approach to the challenge of environmental innovation (EI) that uses a novel methodology to identify relevant configurations for firms engaging in a superior EI strategy. A conceptual framework is proposed that identifies six sets of driving characteristics of EI and two sets of beneficial outcomes, all inherently tensional. Our analysis utilizes a complementary rather than an oppositional point of view. A data set of 65 companies in the ICT value chain is analyzed via fuzzy-set comparative analysis (fsQCA) and a post-QCA procedure. The results reveal that achieving a superior EI strategy is possible in several scenarios. Specifically, after close examination, two main configuration groups emerge, referred to as technological environmental innovators and organizational environmental innovators.

scholarly journals FRI0412 DOES SEVERE ACUTE POSTOPERATIVE PAIN RESULT IN MORE LONG-TERM PAIN AFTER TOTAL HIP OR KNEE ARTHROPLASTY (THA OR TKA) FOR OSTEOARTHRITIS?

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 805.2-805
Author(s):  
D. A. J. M. Latijnhouwers ◽  
C. H. Martini ◽  
R. G. H. H. Nelissen ◽  
H. M. J. Van der Linden ◽  
T. P. M. Vliet Vlieland ◽  
...  
Keyword(s):  
Postoperative Pain ◽  
Acute Pain ◽  
Missing Values ◽  
Rating Scale ◽  
Acute Postoperative Pain ◽  
Preoperative Pain ◽  
Mixed Effect ◽  
Total Hip ◽  
Chronic Postsurgical Pain

Background:Chronic pain is a frequently reported unfavourable outcome of total hip and knee arthroplasties (THA/TKA) (7-23% and 10-34%, respectively) in osteoarthritis (OA) patients (1), which is difficult to treat as underlying mechanisms are not fully understood. Acute postoperative pain has been identified as risk factor for development of long-term pain in other surgical procedures, such as mastectomy and thoracotomy (2). However, the effect of acute postoperative pain on development of long-term pain in THA and TKA patients is unknown.Objectives:To investigate if acute pain following THA/TKA in OA patients is associated with long-term pain and if acute pain affects the course of pain up to 1-year postoperatively.Methods:From a longitudinal multicenter study, OA patients scheduled for primary THA or TKA were included. Acute pain scores, using Numeric Rating Scale (NRS), were routinely collected as part of standard care (≤72 hours after surgery). In case of ≥2 NRS scores the two highest scores were averaged (n=160), else the single score was taken. Pain was dichotomized into severe (NRS≥5) and mild (NRS<5). Pain was assessed preoperatively, at 3 (only THA), 6 and 12 months postoperatively using HOOS/KOOS subscale pain. Separate mixed-effect models for THA and TKA patients were used, with dichotomized acute pain as fixed-effect and long-term pain as outcome, while adjusting for confounders (age, sex, BMI, preoperative pain, mental component scale of the SF12 (MCS-12), and duration of the surgery and hospitalization). We included an interaction between time of measurement and acute postoperative pain to analyse whether effect modification was present. Missing values in preoperative pain and MCS-12 were imputed using multiple imputation methods.Results:81 THA and 87 TKA patients were included, of whom 32.1% and 56.3% reported severe acute pain. The results did not show an associated between severe acute pain and long term pain (THA: β=2.0, 95%-CI:-10.9-7.0; TKA: β=3.8, 95%-CI:-10.6-2.9). Furthermore, It seems that there is no effect present of difference in severity of acute pain and the course of pain over time (THA 6-months: β=6.4, 95%-CI:1.9-10.9 and 12-months: β=0.2, 95%-CI:-4.4-4.8; TKA 12-months: β=3.2, 95%-CI:-0.5-6.8).Conclusion:We did not find an association between acute pain and the development of long-term pain nor that severity of acute pain affects the course of postoperative pain in THA and TKA patients. The fact that THA and TKA patients often experience chronic preoperative pain might be a possible explanation for this finding. Nonetheless, future studies including additional measures of acute pain and pain sensitization in patients with chronic preoperative pain are necessary to draw stronger conclusions.References:[1]Beswick AD, Wylde V, Gooberman-Hill R, Blom A, Dieppe P. What proportion of patients report long-term pain after total hip or knee replacement for osteoarthritis? A systematic review of prospective studies in unselected patients. BMJ open. 2012;2(1):e000435.[2]Katz J, Seltzer Ze. Transition from acute to chronic postsurgical pain: risk factors and protective factors. Expert review of neurotherapeutics. 2009;9(5):723-44.Acknowledgments:We would like to thank the study group that consists of: B.L. Kaptein, Leiden University Medical Center, Leiden; S.B.W Vehmeijer, Reinier de Graaf Hospital, Delft; R. Onstenk, Groene Hart Hospital, Gouda; S.H.M. Verdegaal, Alrijne Hospital, Leiderdorp; H.H. Kaptijn, LangeLand Hospital, Zoetermeer; W.C.M. Marijnissen, Albert Schweitzer Hospital, Dordrecht; P.J. Damen, Waterland Hospital, Hoorn; the NetherlandsDisclosure of Interests:None declared

EXPRESS: How Does Bonus Payment Affect the Demand for Auto Loans and Their Delinquency?

2021 ◽  
pp. 002224372110092
Author(s):  
Zhenling Jiang ◽  
Dennis J. Zhang ◽  
Tat Chan
Keyword(s):  
Consumer Products ◽  
Data Set ◽  
Financial Decisions ◽  
Consumer Panel ◽  
Loan Demand ◽  
Bonus Payments ◽  
Base Salary ◽  
Long Term Impact ◽  
Shed Light

This paper studies how receiving a bonus changes the consumers’ demand for auto loans and the risk of future delinquency. Unlike traditional consumer products, auto loans have a long-term impact on consumers’ financial state because of the monthly payment obligation. Using a large consumer panel data set of credit and employment information, the authors find that receiving a bonus increases auto loan demand by 21 percent. These loans, however, are associated with higher risk, as the delinquency rate increases by 18.5 −31.4 percent depending on different measures. In contrast, an increase in consumers’ base salary will increase the demand for auto loans but not the delinquency. By comparing consumers with bonuses with those without bonuses, the authors find that bonus payments lead to both demand expansion and demand shifting on auto loans. The empirical findings help shed light on how consumers make financial decisions and have important implications for financial institutions on when demand for auto loans and the associated risk arise.

scholarly journals The Kynurenic Acid Analog SZR72 Enhances Neuronal Activity after Asphyxia but Is Not Neuroprotective in a Translational Model of Neonatal Hypoxic Ischemic Encephalopathy

2021 ◽  
Vol 22 (9) ◽  
pp. 4822
Author(s):  
Viktória Kovács ◽  
Gábor Remzső ◽  
Tímea Körmöczi ◽  
Róbert Berkecz ◽  
Valéria Tóth-Szűki ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Kynurenic Acid ◽  
Neuronal Damage ◽  
Brain Regions ◽  
Hypoxic Ischemic Encephalopathy ◽  
Hippocampal Field ◽  
Power Spectral ◽  
Density Values ◽  
Ischemic Encephalopathy

Hypoxic–ischemic encephalopathy (HIE) remains to be a major cause of long-term neurodevelopmental deficits in term neonates. Hypothermia offers partial neuroprotection warranting research for additional therapies. Kynurenic acid (KYNA), an endogenous product of tryptophan metabolism, was previously shown to be beneficial in rat HIE models. We sought to determine if the KYNA analog SZR72 would afford neuroprotection in piglets. After severe asphyxia (pHa = 6.83 ± 0.02, ΔBE = −17.6 ± 1.2 mmol/L, mean ± SEM), anesthetized piglets were assigned to vehicle-treated (VEH), SZR72-treated (SZR72), or hypothermia-treated (HT) groups (n = 6, 6, 6; Tcore = 38.5, 38.5, 33.5 °C, respectively). Compared to VEH, serum KYNA levels were elevated, recovery of EEG was faster, and EEG power spectral density values were higher at 24 h in the SZR72 group. However, instantaneous entropy indicating EEG signal complexity, depression of the visual evoked potential (VEP), and the significant neuronal damage observed in the neocortex, the putamen, and the CA1 hippocampal field were similar in these groups. In the caudate nucleus and the CA3 hippocampal field, neuronal damage was even more severe in the SZR72 group. The HT group showed the best preservation of EEG complexity, VEP, and neuronal integrity in all examined brain regions. In summary, SZR72 appears to enhance neuronal activity after asphyxia but does not ameliorate early neuronal damage in this HIE model.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

scholarly journals Principals between exploitation and exploration: Results of a nationwide study on ambidexterity of school leaders

2020 ◽  
pp. 174114322094570
Author(s):  
Marcus Pietsch ◽  
Pierre Tulowitzki ◽  
Colin Cramer
Keyword(s):  
Dynamic Environments ◽  
Management Research ◽  
Data Set ◽  
Term Survival ◽  
Leadership And Management ◽  
Nationwide Study ◽  
Efficiency Control ◽  
Exploitation And Exploration ◽  
Discontinuous Innovation

Both organizational and management research suggest that schools and their leaders need to be ambidextrous to secure prosperity and long-term survival in dynamic environments characterized by competition and innovation. In this context, ambidexterity refers to the ability to simultaneously pursue exploitation and exploration and thus to deliver efficiency, control and incremental improvements while embracing flexibility, autonomy and discontinuous innovation. Using a unique, randomized and representative data set of N = 405 principals, we present findings on principals’ exploitation and exploration. The results indicate: (a) that principals engage far more often in exploitative than in explorative activities; (b) that exploitative activities in schools are executed at the expense of explorative activities; and (c) that explorative and ambidextrous activities of principals are positively associated with the (perceived) competition between schools. The study brings a novel perspective to educational research and demonstrates that applying the concept of ambidexterity has the potential to further our understanding of effective educational leadership and management.

Sign in / Sign up

Export Citation Format

Share Document