On the Physical Origin of the Semiannual 1 Component of Surface Air Temperature over 2 Mid-latitude and Subpolar Oceans

With the understanding that seasonal cycle of the temperature are forced principally by the annually evolving solar irradiance, many previous studies have defined seasonal cycle of surface air temperature (SAT) as the sum of yearly-period sinusoidal component and its harmonics, especially semiannual component. In mid-latitude and subpolar regions, the ratio between the semiannual and annual components of solar irradiance is negligibly small but that of the SAT over oceans is not, which remains to be understood. In this study, a simple energy budget model including main energy sources and sinks of oceanic mixed layer is designed to understand this puzzle. It is revealed that, when the oceanic mixed layer is prescribed as a layer of constant depth, the phase and amplitude of the modeled SAT is not consistent with that of the observation. However, when the annually changing heat capacity of the oceanic mixed layer is included, both the amplitude and phase of the modeled SAT share these of the observed SAT, proving that the semiannual component of SAT over mid-latitude and subpolar oceans is a result of the heat capacity-varying oceanic mixed layer in response to annually evolving solar irradiance.

Are male to female employment ratio and salary compensations in agricultural sector co-integrated?

This study has been conducted in order to identify whether there is the co-movements between Male to Female employment ratio and Salary compensations in agricultural sector of Pakistan. To analyze the possible co-movement between the Male to Female employment ratio and Salary compensations in agricultural sector of Pakistan, the time series data for the yearly period of 1990 to 2020 for agriculture sector are taken from the publically available source i.e. website of World Bank. The result indicated that there is a long term relationship exists in between Male to Female employment ratio and Salary compensations in agricultural sector of Pakistan.

THE ANALYSIS OF THE EFFECTIVENESS OF THE INDONESIAN NATIONAL POLICE’S INTELLIGENCE SPECIALIZED DEVELOPMENTAL EDUCATION USING KIRKPATRICK TRAINING EVALUATION MODEL

This study examines the effectiveness of the implementation of the Security Intelligence Specialized Developmental Education (Dikbangspes Intelkam) in the Indonesian National Police (INP)’s Security Intelligence Education Center in 2018 using Kirkpatrick training evaluation model. Conceptually, training center is important to improve the ability of human resources (HR) in the organization and in actual fact the INP’s security intelligence human resources constitute the critical and strategic assets as well as leading component in supporting the police organization especially in presenting information related to the investigation, security and promotion in order to support the main tasks and functions of the Police (Tupoksi) in the field of security and public order (Kamtibmas). This research was conducted with a mixed method, beginning with the collection and analysis of quantitative data then proceeded with the collection and analysis of qualitative data. The analysis used Kirkpatrick's training evaluation model, namely reaction, learning, behavior, and result. The quantitative analysis results show that the effectiveness of the INP’s Security Intelligence Specialized Developmental Education (Dikbangspes) is high; however, based on the qualitative analysis, the implementation of the Security Intelligence Specialized Developmental Education (Dikbangspes) in 2018 faces 3 (three) problems, namely: the attainment of educational goals has not yet optimal, the training management system having been developed in the yearly period has not yet had a strong reference, and the evaluation of educational outcomes has had no significant follow-up.

Evaluasi Anggaran Biaya Produksi Pada PT. Perkebunan Nusantara III Di Kota Medan

The purpose of this research is to evaluate the production cost budget at PT. Perkebunan Nusantara III This research is a study that uses a qualitative approach with a descriptive method in question is to use information data obtained at the time of the study and from the field in the form of data that is written or oral from the parties involved. The results obtained that PT. Perkebunan Nusantara III has evaluated the calculation of production costs periodically, based on reports based on production prices, selling prices and profit and loss by determining harvest costs, maintenance costs, factory overhead costs, processing costs, and depreciation costs. budget prepared by PT. Perkebunan Nusantara III is not yet perfect enough because there are still many significant deviations both beneficial and adverse. This is the responsibility of managers to conduct more in-depth evaluations to make the realization of costs so as not to occur too far away. Conclusions through field research that PT. Perkebunan Nusantara III has made a production cost budget with a yearly period. PT. Perkebunan Nusantara III has evaluated the calculation of production costs periodically, based on reports based on production prices, selling prices and profit and loss by determining harvest costs, maintenance costs, factory overhead costs, processing costs, and depreciation costs. The budget prepared by PT. Perkebunan Nusantara III is not yet perfect enough.

Fossil Carbon Fraction and Measuring Cycle for Sewage Sludge Waste Incineration

In this study, the fossil carbon contents of the two facilities were analyzed using 10 or more samples for each facility from June 2013 to March 2015. In addition, the optimal measurement period was calculated from the analyzed fossil carbon contents using a statistical method. As a result of the analysis, the fossil carbon contents were found to be less than 35%, indicating that the biomass content of sewage sludge was not 100%. The fossil carbon content could be representative of using yearly period measurements value. When calculating Green house gas (GHG) emissions from waste incineration, South Korea has been calculating only Non-CO2 emissions because it regarded the CO2 emitted in GHGs from sewage sludge (SS) incineration facilities as originating from biomass. However, biomass of the sewage sludge incineration facility is not 100%, so it is necessary to estimate the greenhouse gas emissions considering the fossil carbon content. Therefore, there is a need to increase the reliability of the greenhouse gas inventory by conducting further studies (such as CO2 concentration analysis) related to the calculation of CO2 emissions for the relevant facilities (sewage sludge incinerator).

The relation between management fees and the mutual funds` performance in Poland in 2015

Research background: The investor`s expectation of better performance in the case of more expensive mutual funds seems natural and fully justified. However, the rise of passive funds and their surprisingly good results, especially when taking into account their low fees, triggered the discussion. Recent years have brought more and more studies, conducted mostly for the American market, discrediting high-charging, aggressive funds. First analyses in Poland also indicate that the level of fees is not always linked with the fund’s performance. Purpose of the article: The purpose of the study is to investigate the relation be-tween the fees imposed by the mutual funds and the funds` performance. The idea is to verify, whether higher management fees are associated with top performance and whether it is rational to pay more for capital management. Methods: In the first step of the study, linearity and direction of the dependency was explored, using scatterplots and correlation analysis. In the second part, the linear regression was created to verify the strength of the relation. One-factor models have been built with the rate of return and standard deviation as independent variables for 1-, 3- and 5-year time horizons. Moreover, two-factor models, including both rate of return and risk has been created, to compare the significance of return and risk factor. Findings & Value added: The results indicated that more expensive Polish mutual funds in 2015 tended to perform worse in all tested time horizons — both in terms of lower rates of return and higher risk. Especially unexpected are the results of rates of return regression analysis — it turns out that within a sample 1% higher fee implied over 0.6% lower rate of return before fees (in yearly period). Nonetheless, the risk turned out to be more important, explaining the charges variability much better than the rate of return. Another interesting finding of the study is that merely two simple factors (return and risk) explain even as much as 60% of the management fee variability.

The relation between management fees and the mutual funds` performance in Poland in 2015

Research background: The investor`s expectation of better performance in the case of more expensive mutual funds seems natural and fully justified. However, the rise of passive funds and their surprisingly good results, especially when taking into account their low fees, triggered the discussion. Recent years have brought more and more studies, conducted mostly for the American market, discrediting high-charging, aggressive funds. First analyses in Poland also indicate that the level of fees is not always linked with the fund’s performance. Purpose of the article: The purpose of the study is to investigate the relation be-tween the fees imposed by the mutual funds and the funds` performance. The idea is to verify, whether higher management fees are associated with top performance and whether it is rational to pay more for capital management. Methods: In the first step of the study, linearity and direction of the dependency was explored, using scatterplots and correlation analysis. In the second part, the linear regression was created to verify the strength of the relation. One-factor models have been built with the rate of return and standard deviation as independent variables for 1-, 3- and 5-year time horizons. Moreover, two-factor models, including both rate of return and risk has been created, to compare the significance of return and risk factor. Findings & Value added: The results indicated that more expensive Polish mutual funds in 2015 tended to perform worse in all tested time horizons — both in terms of lower rates of return and higher risk. Especially unexpected are the results of rates of return regression analysis — it turns out that within a sample 1% higher fee implied over 0.6% lower rate of return before fees (in yearly period). Nonetheless, the risk turned out to be more important, explaining the charges variability much better than the rate of return. Another interesting finding of the study is that merely two simple factors (return and risk) explain even as much as 60% of the management fee variability.

A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.063 °C · y-1) and a widening of the synchronized period (2.9 d · y-1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. Straightforward incorporation of external drivers (e.g. land cover, basin size) should allow this modeling framework to be readily applied across multiple sites and at multiple spatial scales.

A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.063 °C · y-1) and a widening of the synchronized period (2.9 d · y-1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. Straightforward incorporation of external drivers (e.g. land cover, basin size) should allow this modeling framework to be readily applied across multiple sites and at multiple spatial scales.