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Proso millet (Panicum miliaceum L.) is a drought tolerant underutilised crop cultivated in rainfed subsistence agricultural systems. Proso millet yields were simulated using a calibrated Agricultural Production Systems Simulator (APSIM) model for 95 locations in Sri Lanka. The yield maps were generated according to the Inverse Distance Weighting (IDW) model using ArcMap 10.7.1. The database contains Proso millet yield maps for current climate and yield change under 5 hypothetical climate change scenarios; 1oC, 1.5 oC and 2 oC temperature increments, 25% rainfall increment, and 25% rainfall reduction compared to the baseline (1980-2009) climate.

# Hotter deserts and the impending challenges for the spiny-tailed lizard in India [https://doi.org/10.5061/dryad.x95x69psd](https://doi.org/10.5061/dryad.x95x69psd) This dataset encompasses all the data utilized for analysis within the manuscript. ## Description of the data and file structure \################################################################################################################################################## Field body temperature This datafile contains the data collected from thermal loggers on lizards from 15 March 2021 to 23 May 2021. \* Lizard_ID: Identifies individual lizards \* Date: It refers to the date on which the data was collected using the logger \* Time: It refers to the time on which the data was collected using the logger \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* Temp.body: Calibrated skin temperature of the lizard in °C \################################################################################################################################################### Operative temperature habitat This datafile contains the data collected from copper models in different microhabitats. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Burrow: Operative temperature in burrow in °C \* Open: Operative temperature in open in °C \**Cells with "null" suggest missing data as we removed copper models from the field on rainy/overcast days. \################################################################################################################################################### Operative temperature site This datafile contains the data collected from copper models in open microhabitat from two sampling sites. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Open_S: Operative temperature in open collected from Sam sampling site in °C \* Open_B: Operative temperature in open collected from Bedhiya sampling site in °C \################################################################################################################################################### Operative temperature size This datafile contains the data collected from copper models of two different sizes in the three microhabitats. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Open_ad: Operative temperature in open collected using an average adult STL sized copper model in °C \* Open_juv: Operative temperature in open collected using an average juvenile STL sized copper model in °C \* Bur_ad: Operative temperature in burrow collected using an average adult STL sized copper model in °C \* Bur_juv: Operative temperature in burrow collected using an average juvenile STL sized copper model in °C \################################################################################################################################################### TBAE This datafile contains the data collected from focal sampling and the data derived from operative temperatures using the TBAE algorithm. \* Date: It refers to the date on which the data was collected using the logger \* Time: It refers to the time on which the data was collected using the logger \* Activity_test: Data from focal sampling indicating whether the lizards were active (denoted as 1) or inactive (denoted as 0) \* Activity_algorithm: Data from TBAE algorithm indicating whether the lizards were active (denoted as 1) or inactive (denoted as 0) \################################################################################################################################################### Locomotor performace This datafile contains data collected by analysing performance run videos. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* body.temp: Cloacal temperatutre of the lizard in °C \* Speed(m/s): Speed of the lizard in m/s \################################################################################################################################################### Critical thermal limits This datafile contains the data collected in the laboratory about the critical thermal limits of STL. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* CTmax: Critical Thermal Maxima of the lizard in °C \* CTmin: Critical Thermal Minima of the lizard in °C \################################################################################################################################################### Preferred Temperature This datafile contains the data collected in the laboratory about the preferred temperature of STL. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* Q25: First quartile lower quartile 25th percentile of the preferrence data for the lizard in °C \* Median: Second quartile median 50th percentile of the preferrence data for the lizard in °C \* Q75: Third quartile upper quartile 75th percentile of the preferrence data for the lizard in °C \################################################################################################################################################### Scan Survey This datafile contains the data collected in the field field using scan sampling. \* Open.temp: Operative temperature in open in °C \* Burrow: Proportion of individuals inside burrows \* Basking: Proportion of individuals basking \* Foraging: Proportion of individuals foraging \* Mating: Proportion of individuals engaged in courtship/mating \* Territorial: Proportion of individuals engaged in territorial behavior (fight/intimidation) \* Total: Proportion of individuals active \################################################################################################################################################## Calibration This datafile contains the data collected to calibrate skin and copper model temperatures to cloacal temperatures. \* Lizard_ID: Identifies individual lizards \* Cloacal: Cloacal temperature of the lizard in °C \* Skin: Skin temperature of the lizard in °C \* Copper_model: Copper_model temperature in °C \################################################################################################################################################## ## Code/Software All data was analysed using R 4.1.0. Ectotherms are particularly vulnerable to climate change, especially those living in extreme areas, such as deserts, where species are already thermally constrained. Using the vulnerable herbivorous lizard, Saara hardwickii, as a model system, we used a multi-pronged approach to understand the thermal ecology of a desert agamid and the potential impacts of rising temperatures. Our data included field-based measures of operative temperatures, body temperatures, and activity, as well as lab-based measures of thermal limits, preferences, and sprint speed. As expected, the temperature dependence of locomotor performance and foraging activity was different, and in the worst-case global warming scenario (SSP5-8.5), potential sprint speed may decrease by up to 14.5% and foraging activity may decrease by up to 43.5% by 2099. Burrows are essential thermal refuges, and global warming projections suggest that S. hardwickii may be restricted to burrows for up to 9 hours per day by 2099, which would greatly limit critical activities, like foraging and seeking mating opportunities. Overall, we show that key information on thermal ecology, including temperature-sensitive behaviours in the wild, is necessary to understand the multiple ways in which increasing temperatures may influence ectothermic vertebrates, especially for species like S. hardwickii which are already vulnerable to environmental change. Detailed data collection methodology has been provided in the manuscript.

Characterization of glacier changes in the surface area, terminus, equilibrium line altitude (ELA), elevation, and velocity was worked out for the Jankar Chhu Watershed (JCW) of Lahaul Himalaya using freely available satellite remote sensing data and the limited number of field observations. We studied changes using Corona (1971), Landsat (1993‒2017), Sentinel 2A (2016), the SRTM Digital Elevation Model (DEM; 2000), and the global TanDEM‒X DEM (2014). Our results showed that changes in glacier area (‒14.7 ± 4.3 km²), terminus (‒4.7 ± 0.4 m a¯¹), and ELA (~ 20 m rise) between 1971 and 2016 are smaller than previously reported. Glacier lake area increased by ~0.3 km² during 1971‒2016. An intricate pattern of mass changes across the JCW was observed, with surface lowering on an average of ‒0.7 ± 0.4 m a¯¹ which equates to a geodetic mass balance of ‒0.6 ± 0.4 m w.e. a¯¹ during 2000‒14. The computed glacier surface velocities (1971‒2017) reveal nearly stagnant debris-covered ablation zone but the dynamically active main trunk. The present study provides valuable insights into the recent multiparameter glacier variations, which are of critical importance to assess the future glacier dynamics on a regional scale in areas like the present one.

The cloud computing industry has set new goals for better service delivery and deployment, so anyone can access services such as computation, application, and storage anytime. Cloud computing promises new possibilities for approaching sustainable solutions to deploy and advance their services in this distributed environment. This work explores energy-efficient approaches and how cloud-based architecture can reduce energy consumption levels amongst enterprises leveraging cloud computing services. Adopting cloud-based networking, database, and server machines provide a comprehensive means of achieving the potential gains in energy efficiency that cloud computing offers. In energy-efficient cloud computing, virtualization is one aspect that can integrate several technologies to achieve consolidation and better resource utilization. Moreover, the Green Cloud Architecture for cloud data centers is discussed in terms of cost, performance, and energy consumption, and appropriate solutions for various application areas are provided.

This work has been published in Solar Energy Materials & Solar Cells 215 (2020) 110603 (DOI: 10.1016/j.solmat.2020.110603). Antimony selenide (Sb2Se3) based solar cell technology has experienced rapid development with demonstrated cell efficiency reaching ̴ 9.2% for devices with substrate configuration, hence necessitating more intense research investigation for further progress. Though the effect of crystallographic orientation in this non-cubic material on device performance is now well understood, the influence of composition and intrinsic defects remains debatable. In this work we describe the fabrication and device characteristics of Sb2Se3 solar cells designed in the substrate configuration of (SLG/Mo/Sb2Se3/CdS/i-ZnO+ITO). Notably, Sb2Se3 absorber layers with predominant (hk0) orientation were deposited in a single step by e-beam evaporation of pre synthesized bulk source material. As grown precursor Sb2Se3 thin films were subjected to reactive thermal annealing (RTA) treatment in the presence of Se source at different temperatures for enhancing their crystalline quality and balancing their stoichiometry. Analysis of the completed solar cells indicated improved efficiencies post RTA process, with the best performing devices exhibiting a power conversion efficiency (η) of ~ 4.34% for an absorber annealed at a temperature of 350 °C. The improved efficiency is ascribed to the observed changes in chemical composition of the absorber layer and the possible formation of related beneficial antisite defects. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 712949 (TECNIOspring PLUS)" and the Government of Catalonia's Agency for Business Competitiveness(ACCIÓ). This research was also supported by the Spanish Ministry of Science, Innovation and Universities under the WINCOST (ENE2016-80788-C5-1-R) project, and by the European Regional Development Funds (ERDF, FEDER Programa Competitivitat de Catalunya 2007-2013). Authors from IREC and the University of Barcelona belong to the SEMS (Solar Energy and Materials Systems) Consolidated Research Group of the 'Generalitat de Catalunya' (Ref. 2017 SGR 862). Ministry of New and Renewable Energy (MNRE) is very well acknowledged for funding the research activities at SRM IST through the project "Development of Lithium Ion Batteries and Computational Studies for Solar Absorber Layers, (Grant No. 31/03/2014-15/PVSE-R&D).

In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (ACRR) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model ACRR in terms of average run time, power consumption and average power required than any other state-of-art techniques.

The subject of power quality is gaining much interest nowadays. With the sophisticated sensitive electrical devices available in the market, power stations should ensure qualified and eminent power to the point of utilization. Bad voltage conditions in a power system can cause a malfunction at the point of utilization. Dynamic voltage restorer (DVR) is a reliable power electronic offsetting device which when engaged in the power system is effectual in the operation of compensating voltage sag/swell. In this research work, a DVR for offsetting multiple voltage disturbances in a power system is presented and a novel control strategy to trigger power switches in DVR is proposed. The proposed novel control methodology is compared with Synchronous Reference Frame (SRF) theory-controlled DVR. The simulation model is designed and the analysis with MATLAB/SIMULINK is presented

In this paper MHD rotating double diffusive and chemically reactive fluid flow past a vertical porous plate with thermal radiation and heat absorption/generation is studied. The non-dimensional governing equations involved in the present analysis are solved by using finite difference technique. The effects of various physical parameters on velocity, temperature and concentration along with skin friction, the rate of heat transfer in the form of Nusselt number and the rate of mass transfer in the form of Sherwood number are studied through the graphs and tables.