The Condition of Photovoltaic Modules under Random Operation Parameters

The paper presents an original method underlying an efficient tool for assessing the condition of photovoltaic (PV) modules, in particular, those made of amorphous cells. Significantly random changes in operational parameters characterize amorphous cell operation and cause them to be challenging to test, especially in working conditions. To develop the method, the authors modified the residual method with incorporated histograms. The proposed method has been verified through experiments that show the usefulness of the proposed approach. It significantly minimizes the risk of false diagnostic information in assessing the condition of photovoltaic modules. Based on the proposed methods, the inference results confirm the effectiveness of the concept for evaluating the degree of failure of the photovoltaic module described in the paper.

Synergy of Using Nadir and Limb Instruments for Tropospheric Ozone Monitoring

The satellite measurements in nadir and limb viewing geometry provide a complementary view of the atmosphere. An effective combination of the limb and nadir measurements can provide a new information about atmospheric composition. In this work, we present tropospheric ozone column datasets that have been created using combination of total ozone column from OMI and TROPOMI with stratospheric ozone column dataset from several available limb-viewing instruments (MLS, OSIRIS, MIPAS, SCIAMACHY, OMPS-LP, GOMOS). We have developed further the methodological aspects of assessment of tropospheric ozone using the residual method using simulations with the chemistry-transport model SILAM. It has been shown that the accurate assessment of ozone in the upper troposphere and the lower stratosphere (UTLS) is of high importance for detecting the ground-level ozone patterns. The stratospheric ozone column is derived from a combination of ozone profiles from several satellite instruments in limb-viewing geometry. We developed a method for the data homogenization, which includes the removal of biases and a-posteriori estimation (validation) of random uncertainties, thus making the data from different instruments compatible with each other. The high horizontal and vertical resolution dataset of ozone profiles is created via interpolation of the limb profiles from each day to 1° × 1° horizonal grid. A new kriging-type interpolation method, which takes into account data uncertainties and the information about natural ozone variations from the SILAM-adjusted ozone field, has been developed. To mitigate the limited accuracy and coverage of the limb profile data in the UTLS, a smooth transition to the model data is applied below the tropopause. This allows estimation of stratospheric ozone column with full coverage of the UTLS. The derived ozone profiles are in very good agreement with collocated ozonesonde measurements. The residual method was successfully applied to OMI and TROPOMI clear-sky total ozone data in combination with the stratospheric ozone column from the high-resolution limb profile dataset. The resulting tropospheric ozone column is in very good agreement with other satellite data. The global distributions of tropospheric ozone exhibit enhancements associated with the regions of high tropospheric ozone production. The main created datasets are (i) monthly 1° × 1° global tropospheric ozone column dataset using OMI and limb instruments, (ii) monthly 1° × 1° global tropospheric ozone column dataset using TROPOMI and limb instruments and (iii) daily 1° × 1° interpolated stratospheric ozone column from limb instruments. Other datasets, which are created as an intermediate step of creating the tropospheric ozone column data, are: (i) daily 1° × 1° clear sky and total ozone column from OMI and TROPOMI (ii) Daily 1° × 1° homogenized and interpolated dataset of ozone profiles and (iii) daily 1° × 1° dataset of ozone profiles from SILAM simulations with adjustment to satellite data. These datasets can be used in various studies related to ozone distributions, variability and trends, both in the troposphere and the stratosphere.

Invasive vs. Invasive, Parthenium hysterophorus as a Bio-Control Agent against Invasive Mealybugs

Parthenium hysterophorus has an antagonistic potential against mealybugs, which are hosted on wild Dalbergia sissoo. In the current study, an aqueous extract of Parthenium was evaluated against mealybugs on Dalbergia sissoo. A serial dose of 12.5, 25, 50, 100, 200, and 500 µg/mL of aqueous extract was prepared from all parts of the Parthenium plant. After 72 h at high doses, 200 µg/mL and 500 µg/mL aqueous extracts had high mortality of 76.67% and 73.33% via the residual method, respectively. Meanwhile at same dose after 72 h, the contact method had higher mortality percentages of 80% and 80% at 200 µg/mL and 500 µg/mL, respectively. After 48 h at a high dose (200 µg/mL and 500 µg/mL), the mortality of the mealybugs was highest via the contact method. The results show a mortality of 73.33% with both doses. Meanwhile the same doses, via the contact method, after 48 h had a 63.33% mortality rate. After 24 h via the contact method, at 200 µg/mL and 500 µg/mL the mortality of mealybug was 70% with both doses, whereas via the mortality rate via the residual method at 200 µg/mL and 500 µg/mL doses was 56.67% and 66.67%, respectively. These results indicate that Parthenium is a strong bio-control agent against mealybugs. Aqueous extracts could lead to a cost effective and environmentally friendly insecticidal for sustainable use in large scale forestry.

Detecting Noneffortful Responses Based on a Residual Method Using an Iterative Purification Process

The prevalence and serious consequences of noneffortful responses from unmotivated examinees are well-known in educational measurement. In this study, we propose to apply an iterative purification process based on a response time residual method with fixed item parameter estimates to detect noneffortful responses. The proposed method is compared with the traditional residual method and noniterative method with fixed item parameters in two simulation studies in terms of noneffort detection accuracy and parameter recovery. The results show that when severity of noneffort is high, the proposed method leads to a much higher true positive rate with a small increase of false discovery rate. In addition, parameter estimation is significantly improved by the strategies of fixing item parameters and iteratively cleansing. These results suggest that the proposed method is a potential solution to reduce the impact of data contamination due to severe low test-taking effort and to obtain more accurate parameter estimates. An empirical study is also conducted to show the differences in the detection rate and parameter estimates among different approaches.