Multiband Optical Photometry and Bolometric Light Curve of the Type Ia Supernova 2004S

We present $\rm BVR_{c}I_{c}$ broad band CCD photometry of the Type Ia supernova SN 2004S, which appeared in the galaxy MCG-05-16-021, obtained during 2004 February 12 to March 22. Multiband and bolometric light curves constructed using our data as well as other available data are presented. The time of B band maximum and the peak magnitudes in different bands are obtained using the fits of light curve and colour templates. We clearly see a strong shoulder in $\rm R_{c}$ band and a second maximum in $\rm I_{c}$ band. SN 2004S closely resembles SN 1992al after maximum. From the peak bolometric luminosity we estimate the ejected mass of $\rm ^{\rm 56}Ni$ to be 0.41 $\rm M_{\odot}$.Comment: 8 pages, 5 figures, accepted for publication in MNRA

Development of Smart Weighing Lysimeter for Measuring Evapotranspiration and Developing Crop Coefficient for Greenhouse Chrysanthemum

Not AvailableThe management of water resources is a priority problem in agriculture, especially in areas with a limited water supply. The determination of crop water requirements and crop coefficient (Kc) of agricultural crops helps to create an appropriate irrigation schedule for the effective management of irrigation water. A portable smart weighing lysimeter (1000 × 1000 mm and 600 mm depth) was developed at CPCT, IARI, New Delhi for real‐time measurement of Crop Coefficient (Kc) and water requirement of chrysanthemum crop and bulk data storage. The paper discusses the assembly, structural and operational design of the portable smart weighting lysimeter. The performance characteristics of the developed lysimeter were evaluated under different load conditions. The Kc values of the chrysanthemum crop obtained from the lysimeter installed inside the greenhouse were Kc ini. 0.43 and 0.38, Kc mid‐1.27 and 1.25, and Kc end‐0.67 and 0.59 for the years 2019– 2020 and 2020–2021, respectively, which apprehensively corroborated with the FAO 56 paper for determination of crop coefficient. The Kc values decreased progressively at the late‐season stage because of the maturity and aging of the leaves. The lysimeter’s edge temperature was somewhat higher, whereas the center temperature closely matched the field temperature. The temperature difference between the center and the edge increased as the ambient temperature rose. The developed smart lysimeter system has unique applications due to its real‐time measurement, portable attribute, and ability to produce accurate results for determining crop water use and crop coefficient for greenhouse chrysanthemum crops.Not Availabl

Optical Observations of GRB 050401 Afterglow : A case for Double Jet Model

The afterglow of GRB 050401 presents several novel and interesting features : [1] An initially faster decay in optical band than in X-rays. [2] A break in the X-ray light curve after $\sim$ 0.06 day with an unusual slope after the break. [3] The X-ray afterglow does not show any spectral evolution across the break while the R band light curve does not show any break. We have modeled the observed multi-band evolution of the afterglow of GRB 050401 as originating in a two component jet, interpreting the break in X-ray light curve as due to lateral expansion of a narrow collimated outflow which dominates the X-ray emission. The optical emission is attributed to a wider jet component. Our model reproduces all the observed features of multi-band afterglow of GRB 050401. We present optical observations of GRB 050401 using the 104-cm Sampurnanand Telescope at ARIES, Nainital. Results of the analysis of multi-band data are presented and compared with GRB 030329, the first reported case of double jet.Comment: 8 pages, 2 figures and 4 tables. To appear in MNRA

Changing aspects in the management of splenic injury patients: Experience of 129 isolated splenic injury patients at level 1 trauma center from India

Background: The spleen is most the commonly injured solid organ in abdominal trauma. Operative management (OM) has been challenged by several studies favoring successful non-OM (NOM) aided by modern era interventional radiology. The results of these studies are confounded by associated injuries impacting outcome. The aim of this study is to compare NOM and OM for isolated splenic injury in an Indian Level 1 Trauma Center. Materials and Methods: This is a retrospective analysis of prospective database. Results: A total of 1496 patients were admitted with abdominal injuries. One hundred and twenty-nine patients admitted with diagnosis of isolated splenic injury from January 2009 to December 2016 were included in the study. RTIs, followed by falls from height, were the most common mechanisms of injury. Ninety-two (71.3%) patients with isolated splenic trauma were successfully managed nonoperatively. Thirty-seven (28.7%) required surgery, of which three were due to the failure of NOM. Three patients in the nonoperative group underwent splenectomy later, giving an overall success rate of 96.8% for NOM. Patients with isolated splenic trauma requiring OM had higher grade splenic injury (Grade 4/5), higher blood transfusion requirements (P < 0.001), and prolonged Intensive Care Unit and hospital stay in comparison to patients in the nonoperative group. No patient died in the NOM group; two patients died in the splenectomy group due to hemorrhagic shock and acute respiratory distress syndrome, respectively. Conclusion: Although NOM is successful in most patients with blunt isolated splenic injuries, careful selection is the most important factor dictating the success of NOM

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Not AvailableCrop geometry plays a vital role in ensuring proper plant growth and yield. Check row planting allows adequate space for weeding in both direction and allowing sunlight down to the bottom of the crop. Therefore, a light detection and ranging (LiDAR) navigated electronic seed metering system for check row planting of maize seeds was developed. The system is comprised of a LiDAR-based distance measurement unit, electronic seed metering mechanism and a wireless communication system. The electronic seed metering mechanism was evaluated in the laboratory for five different cell sizes (8.80, 9.73, 10.82, 11.90 and 12.83 mm) and linear cell speed (89.15, 99.46, 111.44, 123.41 and 133.72 mm·s −1 ). The research shows the optimised values for the cell size and linear speed of cell were found to be 11.90 mm and 99.46 mm·s −1 respectively. A light dependent resistor (LDR) and light emitting diode (LED)-based seed flow sensing system was developed to measure the lag time of seed flow from seed metering box to bottom of seed tube. The average lag time of seed fall was observed as 251.2 ± 5.39 ms at an optimised linear speed of cell of 99.46 mm·s −1 and forward speed of 2 km·h −1 . This lag time was minimized by advancing the seed drop on the basis of forward speed of tractor, lag time and targeted position. A check row quality index (ICRQ) was developed to evaluate check row planter. While evaluating the developed system at different forward speeds (i.e., 2, 3 and 5 km·h −1 ), higher standard deviation (14.14%) of check row quality index was observed at forward speed of 5 km·h −1 .Not Availabl

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Not AvailableThe management of water resources is a priority problem in agriculture, especially in areas with a limited water supply. The determination of crop water requirements and crop coefficient (Kc) of agricultural crops helps to create an appropriate irrigation schedule for the effective management of irrigation water. A portable smart weighing lysimeter (1000 × 1000 mm and 600 mm depth) was developed at CPCT, IARI, New Delhi for real-time measurement of Crop Coefficient (Kc) and water requirement of chrysanthemum crop and bulk data storage. The paper discusses the assembly, structural and operational design of the portable smart weighting lysimeter. The performance characteristics of the developed lysimeter were evaluated under different load conditions. The Kc values of the chrysanthemum crop obtained from the lysimeter installed inside the greenhouse were Kc ini. 0.43 and 0.38, Kc mid-1.27 and 1.25, and Kc end-0.67 and 0.59 for the years 2019–2020 and 2020–2021, respectively, which apprehensively corroborated with the FAO 56 paper for determination of crop coefficient. The Kc values decreased progressively at the late-season stage because of the maturity and aging of the leaves. The lysimeter’s edge temperature was somewhat higher, whereas the center temperature closely matched the field temperature. The temperature difference between the center and the edge increased as the ambient temperature rose. The developed smart lysimeter system has unique applications due to its real-time measurement, portable attribute, and ability to produce accurate results for determining crop water use and crop coefficient for greenhouse chrysanthemum crops.Not Availabl

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.Validerad;2022;Nivå 2;2022-07-05 (sofila);Funder: , G.B. Pant University of Agriculture and Technology, India; Gola Barrage gauge station Haldwani–Kathgodam, India; Portuguese Foundation for Science and Technology (PTDC/CTA-OHR/30561/2017, WinTherface)</p

Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity

Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water moves through the soil. On the other hand, its measurement is difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for its estimation. Despite significant development over the years, the PTFs showed poor performance in predicting Ks. Using Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTF), i.e. a combination of GA with Multilayer Perceptron (MLP-GA) and Support Vector Machine (SVM-GA), were proposed in this study. We compared the performances of four machine learning algorithms for different sets of predictors. The predictor combination containing sand, clay, Field Capacity, and Wilting Point showed the highest accuracy for all the ML-PTFs. Among the ML-PTFs, the SVM-GA algorithm outperformed the rest of the PTFs. It was noticed that the SVM-GA PTF demonstrated higher efficiency than the MLP-GA algorithm. The reference model for hydraulic conductivity prediction was selected as the SVM-GA PTF paired with the K-5 predictor variables. The proposed PTFs were compared with 160 models from past literature. It was found that the algorithms advocated were an improvement over these PTFs. The current model would help in efficient spatio-temporal measurement of hydraulic conductivity using pre-available databases.Validerad;2022;Nivå 2;2022-05-11 (sofila)</p