Search

Total electron data (TEC) from GPS nowadays can be used as a tool for understanding the space weather phenomena. The development of prediction model for TEC is quiet crucial and challenging due to the dynamic behavior of the ionosphere, since it depends on different factors such as seasonal, diurnal and spatial variations, solar geomagnetic conditions etc. In this paper, an attempt is made for predicting the GPS derived TEC values for different GNSS stations over India using a hybrid method based on Ensemble empirical mode decomposition (EEMD) and Long Short-Term Memory (LSTM) deep learning method. The daily TEC time series data from the IISc Bangalore (Latitude 13.021, Longitude 77.570), Lucknow (Latitude 26.912, Longitude 80.956) and Hyderabad (Latitude 17.417, Longitude 78.551) stations over India during the period 2008 to 2015 of solar cycle 23 and 24 is used for analysis. The assessment of model performance for testing predicted output compared with LSTM and EMD-LSTM models, and their comparison results show that the hybrid EEMD-LSTM model presents better than the other models. [ABSTRACT FROM AUTHOR]

Abstract In the years since liberalization, state power has been rescaled in India's polity and this is both a cause and consequence of greater inter-state competition for footloose capital. In this context, some state governments are designing special regulatory tools to attract and concentrate investments by easing 'doing business' for private investors and leveraging land resources—primarily in their largest cities. Equally important, they ensure public investments are channelled to these select spaces and that resources are available for maintaining high standards of infrastructure. In Hyderabad, the 'Industrial Area Local Authority' (IALA) is a powerful tool that allows privatised management by state and non-state actors of recently constructed productive spaces, mainly concentrating globalised service sector activities, including by collecting local tax revenues and various development charges to be spent exclusively within the industrial area. In this way, governance and fiscal functions are outsourced to non-elected bodies, outside the purview of democratic institutions. By essentially replacing municipal authorities, the IALA framework produces new forms of governance at the same time that it generates fragmentation of the institutional fabric of urban spaces. In effect, it creates a tool for by-passing urban politics, or in the case of periurban spaces does not allow an urban politics to emerge. Building on recent research, this paper examines the implications of IALAs in Hyderabad on urban governance and local state sovereignty. Highlights • Illustrates how India's largest cities are increasingly perceived as territorial assets by rescaled subnational states • Governance and fiscal functions of Hyderabad's industrial areas are outsourced to non-elected bodies via regulatory tools • Such tools produce new forms of governance and generate fragmentation of the institutional fabric of urban spaces • Enclaves and exceptional governance reconfigure territorial organisation and undercut local sovereignty and accountability [ABSTRACT FROM AUTHOR]

The paper describes and analyses the Hadrami migration to and adaptations in Hyderabad, India. The various adaptive niches exploited by the Hadramis in Hyderabad before the merger with India in 1948 are outlined, particularly the ones relating to the military field. Hadrami officers and soldiers were a typical features of the Nizam's army. But this was to change with the Indian take-over in 1948. The second part of the paper outlines these changes as they relate to Hyderabad in general and to the new adaptive strategies adopted by the Hadramis in particular. The new evolving adaptations during the 1950s was based on petty trade and represented a downward turn in the fortunes of the Hadramis. During this period the Hadrami identity was fading and they tended to disappear among the other Muslims in the city. In the 1970s, however, labour migration to the Gulf helped improve the economic status of the Hadrami families, and it lead to a strengtheneing of the diasporic organisations that could facilitate the migration and which also strengthened the Hadrami diasporic identity. [ABSTRACT FROM AUTHOR]

This paper analyses the transferability of freight generation (FG) models to provide guidance on the direction of transfer, the degree of transfer, and how to determine the sample size required for new regions. A set of FG models are developed for four regions in India, i.e., Hyderabad, Jaipur, North Kerala, and Central Kerala. Models are assessed for naïve transferability across these regions to know the direction and degree of transfer. Using multidimensionality scaling, we identified new regions similar to the study regions based on geographical characteristics like population density, number of establishments, land value, road density, and seaport proximity. The identical regions are further grouped into four different clusters using the K-means clustering algorithm. In order to determine the sample size of a new region, the transferability results are interpolated to these geographically linked regions with the same demographics. This research is crucial for saving survey resources in terms of money and time. The policymakers and stakeholders can borrow the strategies already under implementation in a region with topographic similarity with the novel methodology proposed. Furthermore, industrialists can take the case study of the establishments in geographically similar regions in strategizing the resource and capacity allocation for efficient and responsive supply chain design. [ABSTRACT FROM AUTHOR]

• The proposed model predicts residential load demand for future lockdown tenure of COVID-19 pandemic. • The load prediction model used Recurrent Neural Network with Long Short Term Memory (RNN-LSTM) technique with real-time residential load data collected during lockdown period from the installed smart meters. • Load predictions were carried out for two different residential load circles with different data scaling techniques and parameter data and the results were compared. • The results obtained RNN-LSTM method is compared with a benchmarking method of Auto Regressive Integrated Moving Average (ARIMA) to highlight the prediction superiority of the proposed. • The predicted model yields a correlation factors of 96%, 92% without temperature data and 90%, 91% with temperature for two residential colonies under consideration. Covid-19 pandemic and resulting lockdown has created a wide impact on social life, including sudden rise in residential load demand. Utilities, for better load scheduling and economic operations, rely on different prediction models among which neural networks proved to be more appropriate. For such unforeseen situations, the non-availability of prior predictions elevated the utility challenges. Moreover, the stringency of lockdowns caused due to mutated COVID-19 virus, necessitates accurate lockdown load predictions. This paper proposes a Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM) model, trained to produce such predictions for two areas of residential sector. The model uses real-time residential load data from the year 2020, with and without weather parameters. The correlation factor (R) of proposed method 0.9683 outperformed the ARIMA's value 0.703. The model is evaluated with correlation factors of 0.9683 and 0.9235 without temp; 0.90361 and 0.913662 with temperature for Apurupa and Jyothi colonies respectively located in Hyderabad, India. In addition, the error metrics namely, Mean absolute percentage error (MAPE) and Mean absolute error (MAE) are 2.0464 and 138.576 for Apurupa colony; 0.015 and 201.648 for Jyothi colony respectively. However, the prediction error metrics increased slightly with temperature data. The proposed framework will assist utilities for effective load predictions during situations such as pandemic lockdown. [ABSTRACT FROM AUTHOR]

• A new Functional link broad learning system is used for solar irradiance prediction. • Large number of mapped feature nodes is created from expanded cluster of inputs. • Random weights and biases and suitable activation functions are used for these nodes. • Mapped feature nodes are used as inputs to enhancement nodes for FLBLS. • A new hybrid optimization technique is used for optimization of FLBLS parameters. A new machine learning approach for forecasting short-termsolar irradiance is presented in this paper considering different weather conditions and time horizon in a microgrid environment. Using historical solar irradiance, a short-term forecasting model known as Robust optimized functional link broad learning system (FLBLS) is developed. Here the original input comprising delayed historical solar irradiance samples is initially expanded by exponential trigonometric functions and the expanded features are then used to create a large number of feature nodes using random weights and biases. Further the newly created features are fed as inputs to the enhancement nodes forming a broad learning system (BLS). This newly formed flat FLBLS facilitates incremental learning in fast remodelling of the network if needed for efficient performance without a retraining process unlike deep neural networks using several hidden layers and filters, etc. The proposed approach is designed on the basis of the standard random vector functional link network (RVFLN), however unlike the traditional RVFLN that applies inputs directly and set up the enhancement nodes, in our proposed approach, initially the inputs are expanded and map the expanded inputs to generate a set of mapped features. It reduces the lengthy process unlike deep neural networks that involves the back propagation algorithm for error minimization. The proposed FLBLS approach simplifies the learning process with the proper choice of feature nodes as it can be implemented to a flat network or a simpler network such as ELM that requires only the simple calculation of the output weight and obtain the final output where retraining is not required for adding extra feature nodes or enhancement nodes like deep neural architectures. Also, the minimum variance strategy with the training samples is useful in rejecting the effects of noise and outliers in the system and make the network robust which gets reflected in computing output weights using generalized least square. The minimum variance strategy further enhances the performance of the proposed system. To improve the prediction accuracy, the FLBLS parameters are optimized using an efficient metaheuristic chaotic firefly-sine cosine levy flight algorithm. Also, to reject the effects of noise and outliers and make the network robust a minimum variance strategy is used with the training samples that gets reflected in computing output weights using generalised least squares. The suggested method is experimented rigorously and analysed with different time frames designed for 30 min and 60 min ahead solar irradiance prediction. The obtained results from experiments confirm that the proposed FLBLS approach outperforms other randomized neural network forecasting methods as well many state-of-the-art methods using solar irradiance data of Hyderabad, India collected from National Renewable Energy Laboratory (NREL). This approach exhibits with the lowest errors in different case studies among all the compared methods. This FLBLS designed approach is an efficient approach for the purpose of solar irradiance prediction with highly nonlinear data. [ABSTRACT FROM AUTHOR]

The Indian economy struggles with electricity supply deficits and low quality supply. Although several initiatives including demand side management measures have already been implemented, consumers from different backgrounds suffer from various drawbacks of quality supply. This paper explores the valuation of electricity quality from the perspective of domestic consumers in Hyderabad, India. We conducted a discrete choice experiment with 798 urban households. For analysis, we apply a scale-adjusted latent class model to identify heterogeneity in preferences and in variance-scale. The results confirm the hypothesis of highly heterogeneous household preferences and reveal limited preparedness of domestic users to pay for improved electricity quality and renewable energy. Further, most respondents prefer state owned distribution companies to private enterprises or cooperative societies. We argue that the estimated preferences, implying demand and willingness to pay for single attributes of electricity quality, can help policy makers to adequately incorporate consumers' interests into decision making. The results further indicate that domestic tariff hikes should not be used to finance extension of renewable energies or infrastructure investment to improve reliability in supply. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration. [Copyright &y& Elsevier]

Abstract: In this paper life cycle energy (LCE) demand of a residential building of usable floor area about 85.5m2 located at Hyderabad (Andhra Pradesh), India is evaluated under different envelopes and climates in Indian context. The house is studied with conventional (fired clay) and alternative wall materials (hollow concrete, soil cement, fly ash and aerated concrete) under varying thickness of wall, and insulation (expanded polystyrene) on wall and roof. The house is modelled for five different climatic zones of India, i.e. hot and dry, warm and humid, composite, cold and moderate. Study suggests that alternative wall materials alone (without insulation) reduce LCE demand of the building by 1.5–5%. Aerated concrete (AC), as wall material, has better energy performance over other materials. LCE savings are significant when insulation is added to external wall and roof. It varies from 10% to 30% depending on the climatic conditions. Maximum LCE savings with insulation are observed for warm and humid climate and least for moderate climate. For same thickness of insulation, LCE savings are much more with roof insulation than wall insulation. But wall insulation is found to be preferable to a thicker wall. It is also observed that there is a limit for thickness of insulation that can be applied on external walls and roof from life cycle point of view. This limit is found to be about 10cm for composite, hot and dry, warm and humid, and cold climates and 5cm for moderate climate. [Copyright &y& Elsevier]

Abstract: Highly sophisticated balloon-borne scientific payloads have stringent requirement on the telemetry and command system. The development and fabrication of the on-board TT&C package for telemetry, tracking, command, safety and ranging for these experiments is done in-house at the National Balloon Facility (NBF) at Hyderabad. In the last few years, we have made major improvements both in the ground station and the on-board sub-systems, thereby improving the data quality, data handling speed and the general flight control along with aviation safety. The new system has telemetry data rate up to 1Mbps. A reduction in weight, power and cost of the reengineered on-board integrated package has also lead to the ease of operation during field tests prior to launch and at remote recovery sites. In this paper, we describe the details of the new control package, its flight performance and our plans for portable S-band telemetry and telecommand system to cater to the balloon flights from Antarctic station and long duration balloon flights. [ABSTRACT FROM AUTHOR]

Abstract: The present study deals with the impact of extensive anthropogenic activities associated with festivities and agricultural crop residues burning in the Indo-Gangetic Plains (IGP) on satellite-derived reflectance during November 2007. Intense smoke plumes were observed in the IRS-P4 OCM satellite data over IGP associated with agricultural crop residue burning during the study period. Terra-MODIS AOD and CALIPSO LIDAR backscatter datasets were analysed over the region to understand the spatial and temporal variation of the aerosol properties. Ground-based measurements on aerosol optical properties and black carbon (BC) mass concentration were carried out during 7–14 November 2007 over urban region of Hyderabad, India. Top of the Atmosphere (TOA) reflectance estimated from IRS-P4 Ocean Color Monitor (OCM) data showed large variations due to anthropogenic activities associated with crop residue burning and fireworks. Atmospheric corrections to OCM satellite data using 6S radiative transfer code with inputs from ground and satellite measurements could account for the variations due to differential aerosol loading. Results are discussed in this paper. [Copyright &y& Elsevier]

Abstract: This report explores how vulnerability to HIV/AIDS applies to women in the reproductive age range living in the slum areas of Delhi and Hyderabad. The paper is based on a qualitative study of AIDS awareness levels conducted during the summer of 2006. It offers insightful narratives from a sample of 32 women, providing an in depth view of their vulnerability to HIV/AIDS due to their precarious socioeconomic conditions and low AIDS awareness. The women cited lack of education, low empowerment in expressing and accessing information related to sexual matters, and poverty as key factors to vulnerability. [Copyright &y& Elsevier]

Abstract: Calandria tube is a large diameter, extremely thin walled zirconium alloy tube which has diameter to wall thickness ratio as high as 90–95. Such tubes are conventionally produced by the ‘welded route’, which involves extrusion of slabs followed by a series of hot and cold rolling passes, intermediate anneals, press forming of sheets into circular shape and closing the gap by TIG welding. Though pilgering is a well established process for the fabrication of seamless tubes, production of extremely thin walled tubes offers several challenges during pilgering. Nuclear fuel complex (NFC), Hyderabad, has successfully developed a process for the production of Zircaloy-4 calandria tubes by adopting the ‘seamless route’ which involves hot extrusion of mother blanks followed by three-pass pilgering or two-pass pilgering schedules. This paper deals with standardization of the seamless route processes for fabrication of calandria tubes, comparison between the tubes produced by 2-pass and 3-pass pilgering schedules, role of ultrasonic test charts for control of process parameters, development of new testing methods for burst testing and other properties. [Copyright &y& Elsevier]

Abstract: Research explored the regulatory regime, both ‘on paper’ and ‘in practice’, for private unaided schools serving low-income families (‘budget’ private schools), in Hyderabad, Andhra Pradesh, India. Interviews were conducted with school managers, teachers, parents, and senior government officials and politicians. A Supreme Court Judgement rules out profit-making in the schools, although, in practice, interviewees said this was widely ignored. At the state level, extensive regulations govern all aspects of a school''s work. In practice, government officials reported, however, that only four regulations need to be met, but none of the recognised schools met more than two of these. In practice, there may be too few inspectors and a culture of bribery and corruption. A possible way forward is described, of private self-regulation, based on findings concerning parental accountability. [Copyright &y& Elsevier]

• Street trees impact liveability and work for vendors in Hyderabad. • Loss of trees affects vendors' right to the city. • Gentrification encloses trees in public spaces and increases inequity. • Street vendors' requirements need consideration from planners. Urban street trees provide critical ecosystem services such as shade and contribute to the health and well-being of urban residents - especially those who spend much of the day outdoors. Our paper focusses on one of the most vulnerable groups in a city: street vendors, who spend much of their time on streets, with or without trees. Most contemporary visions for green cities ignore street vendors and fail to consider their uses and expectations of public green spaces. Drawing from in-depth interviews with 75 street vendors in 11 market streets in the hot Indian city of Hyderabad, we discuss their perceptions of the importance of trees for their daily work and livelihood and describe the challenges that street vendors face in accessing shade in a hot city. In several instances, "development" of urban areas has led to gentrification, with the felling of trees and displacement of street vendors, bringing into question their 'right to the city'. We argue that street vendors are largely left out of considerations of urban ecological planning, despite being one of the groups most affected by the availability of wooded streets in cities. Ecologists, urban researchers, planners and landscape designers must explicitly consider greening strategies from the perspective of street vendors for inclusive, sustainable urban development. [ABSTRACT FROM AUTHOR]