http://nopr.niscpr.res.in/handle/123456789/64720 Sun, 11 Oct 2026 11:00:27 GMT 2026-10-11T11:00:27Z http://nopr.niscpr.res.in/handle/123456789/64730 Title: The Potential and Utilization of Solar Energy for Water and Space Heating in Households in the Autonomous Province of Vojvodina (Serbia) Authors: Prvulovic, Slavica; Josimovic, Ljubisa; Juric, Slobodan; Tolmac, Jasna; Lambic, Miroslav Abstract: This paper investigates the potential and application of solar energy for space and water heating in the Autonomous Province of Vojvodina, Serbia. Vojvodina is characterized by a favorable geographical position, receiving between 2000–2400 hours of sunshine annually, which makes it a suitable region for solar energy utilization. In residential applications, solar thermal systems can meet up to 60–70% of the annual hot water needs of households in Vojvodina. The economic benefits include significant cost savings on energy bills and reduced dependency on conventional fuels. Installing this system in a four-member household would save around 2,400 kWh of electricity annually, valued at approximately 120 EUR. Environmentally, solar heating reduces greenhouse gas emissions and supports Serbia's climate commitments. The practical utility of this study lies in its potential to inform energy policy and promote sustainable energy practices, reducing dependence on non-renewable energy sources and contributing to environmental conservation. Page(s): 1051-1057 Tue, 01 Oct 2024 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/64730 2024-10-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/64729 Title: Calibration and Validation of Reproductive Stages of Wheat Varieties with CERES-Model under Sowing Environments in Irrigated Conditions of Jammu, India Authors: Gupta, Vikas; Gupta, Meenakshi; Sandhu, S S; Singh, Mahender; Bharat, Rajeev; Kour, Sarabdeep; Sood, K K; Gupta, Moni; Singh, A P Abstract: Impact of heat stress during March and April months have significant reflections on the occurrence of phenological stages of wheat. The current investigation aimed to analyze the impact of three varieties of wheat planted in three different environments (dates of sowing) and three levels of nitrogen to study their effects with respect to the occurrence of reproductive stages. Field experiments were conducted with three wheat varieties viz., HD 2967, RSP 561 and WH 1105, which were sown in 3 sowing environments/dates, viz., 25th October-early, 14th November-normal and 4th December-late and 3 levels of nitrogen, viz., 100, 125 and 150 kg/ha randomized in 3 replications under a split-split plot design in rabi season 2015–16 and 2016–17. The experiment was sown at the Agromet Research Farm of Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu (SKUAST-J), Chatha. Experimental conditions and results obtained from the experiments were used as a database for calibration of Crop Environment Resource Synthesis (CERES)-a Wheat model under Decision Support System for Agrotechnology Transfer (DSSAT) version 4.6 package for studying the effects of changing climatic conditions on wheat phenology. The varietal specific genetic coefficients were calibrated and validated for all the 3 wheat varieties. On comparison of the results obtained from the calibration and validation data of various wheat varieties; the researchers came to a very good and valid conclusion to predict the days of occurrence of various phenological stages of wheat. The parametres R2, d-stat, RMSE and nRMSE were used for comparing CERES-Wheat model results with the actual data and the values were excellent. Page(s): 1058-1065 Tue, 01 Oct 2024 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/64729 2024-10-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/64728 Title: Identification of Clay mineral deposits in the South-Eastern region of Uttar Pradesh, India, using Remote Sensing Authors: Karmakar, Debiprasad; Ghosh2, Swapankumar Abstract: Finding new mineral deposits is very important for the economic growth of a country. Recent advancements have made exploration of minerals very easy with the help of satellite imagery. Several inaccessible areas can be explored for the presence of deposits. The present article focuses on the analysis of the Landsat imagery of 2018 using datasets from the Landsat 8 (OLI/TIRS) satellite. The unsupervised classification with maximum likelihood algorithm is applied to bring out probable classes. It is found that the major classes are wasteland and forest followed by vegetation, water body, sand, builtup, limestone, kaolin and rocky land. The main objective is to find the kaolin rich zones which accounted for ~1.08% of the study area. To validate the findings, field survey have been carried out, 15 clay samples are collected from the study area in Ramgarh-Naudiha region of Sonbhadra district and have been characterised for mineral content. The mined mineral is finegrained, off-white, siliceous, ball clay which can be beneficiated to make it acceptable to Indian ceramic industries. The remote sensing study is useful in identifying clay mineral deposits in the study area in Sonbhadra district and brightens the hope of findings pristine mineral deposits in the other parts of the country also. Page(s): 1066-1074 Tue, 01 Oct 2024 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/64728 2024-10-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/64727 Title: Deep Learning based Bursty Traffic Discrimination and Management using Sandpile Model Authors: N A, Bharathi; Parthasarathi, Ranjani; Vetriselvi, V Abstract: The significant advancements in internet technologies and applications have resulted in a substantial increase in network traffic volume, presenting considerable challenges for network management. The management of bursty traffic, in particular, poses difficulties, as it can originate from both legitimate and malicious sources. To ensure the continuity of normal network operations, it is critical to distinguish between genuine and attack traffic, preventing the blockage of legitimate traffic. This study proposes a framework for detecting and managing bursty traffic within Software-Defined Networking (SDN) environments. A deep learning-based approach is applied to differentiate between Distributed Denial of Service (DDoS) and flash traffic, utilizing the BiLSTM algorithm for its high classification accuracy. This approach uses the Markov Modulated Poisson Process (MMPP) to generate flash traffic, which is then integrated with the CIC-DDoS2019 dataset. For traffic management, a drop mechanism is applied to DDoS traffic, while the Bak-Tang-Wiesenfeld (BTW) Sandpile load balancing algorithm is utilized for managing flash traffic. The proposed Sandpile-based load balancing approach significantly reduces round-trip time by 93% and packet loss by 98.4%, while improving bandwidth availability by 94.5%. Thus the proposed approach combines deep learning for precise traffic classification with a dynamic, self-organizing load-balancing mechanism, offering an efficient and novel solution for managing bursty traffic in real-time network environments. Page(s): 1075-1085 Tue, 01 Oct 2024 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/64727 2024-10-01T00:00:00Z