Seasonal Variation Factors Research Articles

Abnormal Value Treatment and Seasonable Adjustment Method for Medium and Long-term Load Data

Since most of the data used for power demand early warning, forecasting and analysis is the original power data collected directly from the power system, the data cannot be directly applied to the specific analysis because of having two problems. First of all, because various errors will occur in the power data collecting and transmitting processes, some random factors may cause the power consumption data to fluctuate drastically in a short period. These errors or problem data that do not conform to the overall change rule of the power sequence may lead to wrong analysis results. Secondly, because the electricity sequence has obvious seasonal characteristics, and the inherent variation of the load sequence is often obscured by the seasonal variation factors, using the original data for analysis directly often fails to discover the inherent regularity of the electricity consumption data. Based on these two reasons, the original power data should be conducted with abnormal value processing and seasonal adjustment before the power demand early warning, forecasting and analysis.

Effect of Seasonal and Tree Girth Size Variation on Cupressus torulosa D. Don Leaves Essential Oil Composition Growing in Uttarakhand

:Leaves essential oils of Cupressus torulosa D. Don of different girth sizes grown in Chakrata region (Uttarakhand) were investigated for yield (%) and composition in four different seasons. The essential oils were obtained by hydro-distillation and analyzed by GC-FID and GC-MS. The yield of oils (% v/w) at different seasons varied from 0.36 to 1.38 %, highest in monsoon season. A total twenty-four components were identified, representing 77.06-90.08 % of the total oil. Analyzing data using SPSS, afforded the seasonal variation factors to use the relative amount (%) with different tree girth stages. The oil was dominated by varied composition of monoterpenoids (69.81-87.77 %). In monsoon season, the major compound was terpinen-4-ol, ranged from 13.66-22.94 %. The marker constituent of post-monsoon was found to be limonene (18.44-35.94 %). The maximum limonene content (35.94 %) was noted in medium sized trees, followed by big trees (21.62 %) and small trees (18.44 %). Conversely, in winter, α-pinene (15.96 and 17.92 %) was identified as the major compound in small and medium trees, respectively, while sabinene (16.57 %) in big trees. Terpinen-4-ol (20.96 %) was again found as the major component in small trees during summer, while α-pinene was found to be the marker constituent in medium (24.41 %) and big sized trees (20.40 %), respectively. The results clearly indicated the occurrence of marker constituents in different seasons have high diverse potential in perfumery, cosmetic, food, and pharmaceutical industries, if the best season for harvesting is taken into account.

A Stab in the Dark?: A Research Note on Temporal Patterns of Street Robbery.

Objectives:Test the influence of darkness in the street robbery crime event alongside temperature.Methods:Negative binomial regression models tested darkness and temperature as predictors of street robbery. Units of analysis were four 6-hr time intervals in two U.K. study areas that have different levels of darkness and variations of temperature throughout the year.Results:Darkness is a key factor related to robbery events in both study areas. Traversing from full daylight to full darkness increased the predicted volume of robbery by a multiple of 2.6 in London and 1.2 in Glasgow. Temperature was significant only in the London study area. Interaction terms did not enhance the predictive power of the models.Conclusion:Darkness is an important driving factor in seasonal variation of street robbery. A further implication of the research is that time of the day patterns are crucial to understanding seasonal trends in crime data.

Applying partial correlation method to analyzing the correlation between ionospheric NmF2 and height of isobaric level in the lower atmosphere

On the basis of a multiple linear regression model performed for ionospheric NmF2, partial correlation method is first applied to investigating the relation between NmF2 and h (the height of isobaric level) in the lower atmosphere over Wuhan, China during 1957–2004. The results show that partial correlation method can eliminate the influences of solar and geomagnetic activities as well as the seasonal variation factors and reveal the true correlation between NmF2 and h in the lower atmosphere. A weak positive correlation between NmF2 and h is found in the middle stratosphere. In addition, by comparing the partial correlation coefficients between NmF2 and its influence factors, we find that NmF2 is mainly affected by solar activity and the seasonal variation factors, and weakly affected by geomagnetic activity, but hardly affected by h in the lower atmosphere. The study suggests that partial correlation method is a helpful tool for investigating the correlation between ionospheric parameter and its influence factors.