• Energy Research
• Open Access close
• IN close

Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

# 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.

Project: High Resolution dynamically downscaled CMIP5 climate data over India - The datasets in this project are developed as a part of a project associated with the Indian Institute of Technology Delhi, India, and the National Mission for Clean Ganga, Government of India. We have dynamically downscaled a coarser resolution CMIP5 GCM (CESMv1) climate data using the Weather Research and Forecasting (WRF) model for the current (2006-2015) and future (2091-2100) RCP8.5 emission scenario to produce a 10km resolution dataset over India. This dataset is expected to be of massive value to fine-scale regional modelling based climate change adaptive and mitigative studies in fields of water resources, energy, agriculture, and forestry over India. This project is financially supported by the National Mission for Clean Ganga (NMCG), Ministry of Jal Shakti, Department of Water Resources, River Development and Ganga Rejuvenation, Government of India, through grant number TE-16015/02/2019/NMCG. Summary: The Bias Corrected CESMv1 data for mid-century (2041-2050) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature (t2m), relative humidity (rh), wind speed (wspd), total precipitation (prec), mean surface shortwave flux (sw), top-of-atmosphere outgoing longwave radiation (lw), mean surface latent (lhf) and sensible (shf) heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 30 files (10 variables x 3 temporal resolutions) packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x30, and 369x369x12 data points, respectively. The entire dataset is about 30 GB in size. The precipitation files in the older version contained hourly accumulated values for every day. This version contains the correct daily accumulated, monthly accumulated and monthly climatology precipitation data.

The Original data of environmental reporting practices has been obtained for the banks listed in the NSE Nifty Bank Index from 2016–2017 to 2020–2021. The content analysis technique was employed with the help of NVivo 14 . The financial data was collected from the CMIE Prowess database. The data was used to study the the effect of environmental reporting on the financial performance of banks in India. The article titled "Does environmental reporting of banks affect their financial performance? Evidence from India" has been published by using this raw original data. This raw data set will be useful for further research relating to environmental reporting and sustainability of banks in India. the academic researchers, bankers, corporate executives, and policy makers will be beneficiary of this data set.

AbstractBackgroundBinge alcohol exposure during adolescence results in long‐lasting alterations in the brain and behavior. For example, adolescent intermittent ethanol (AIE) exposure in rodents results in long‐term loss of functional connectivity among prefrontal cortex (PFC) and striatal regions as well as a variety of neurochemical, molecular, and epigenetic alterations. Interneurons in the PFC and striatum play critical roles in behavioral flexibility and functional connectivity. For example, parvalbumin (PV) interneurons are known to contribute to neural synchrony and cholinergic interneurons contribute to strategy selection. Furthermore, extracellular perineuronal nets (PNNs) that surround some interneurons, particularly PV+ interneurons, further regulate cellular plasticity. The effect of AIE exposure on the expression of these markers within the PFC is not well understood.MethodsThe present study tested the hypothesis that AIE exposure reduces the expression of PV+ and choline acetyltransferase (ChAT)+ interneurons in the adult PFC and striatum and increases the related expression of PNNs (marked by binding of Wisteria floribunda agglutinin lectin) in adulthood. Male rats were exposed to AIE (5 g/kg/day, 2‐days‐on/2‐days‐off, i.e., P25 to P54) or water (CON), and brain tissue was harvested in adulthood (>P80). Immunohistochemistry and co‐immunofluorescence were used to assess the expression of ChAT, PV, and PNNs within the adult PFC and striatum following AIE exposure.ResultsChAT and PV interneuron densities in the striatum and PFC were unchanged after AIE exposure. However, PNN density in the PFC of AIE‐exposed rats was greater than in CON rats. Moreover, significantly more PV neurons were surrounded by PNNs in AIE‐exposed subjects than controls in both PFC subregions assessed: orbitofrontal cortex (CON = 34%; AIE = 40%) and medial PFC (CON = 10%; AIE = 14%).ConclusionsThese findings indicate that, following AIE exposure, PV interneuron expression in the adult PFC and striatum is unaltered, while PNNs surrounding these neurons are increased. This increase in PNNs may restrict the plasticity of the ensheathed neurons, thereby contributing to impaired microcircuitry in frontostriatal connectivity and related behavioral impairments.

The impacts of increasing natural climate disasters are threatening food security in the Asia-Pacific region. Rice is Asia’s most important staple food. Climate variability and change directly impact rice production, through changes in rainfall, temperature and CO2 concentrations. The key for sustainable rice crop is water management. Adaptation can occur through shifts of cropping to higher latitudes and can profit from river systems (via irrigation) so far not considered. New opportunities arise to produce more than one crop per year in cooler areas. Asian wheat production in 2005 represents about 43 % of the global total. Changes in agronomic practices, such as earlier plant dates and cultivar substitution will be required. Fisheries play a crucial role in providing food security with the contribution of fish to dietary animal protein being very high in the region – up to 90 % in small island developing states (SIDS). With the warming of the Pacific and Indian Oceans and increased acidification, marine ecosystems are presently under stress. Despite these trends, maintaining or enhancing food production from the sea is critical. However, future sustainability must be maintained whilst also securing biodiversity conservation. Improved fisheries management to address the existing non-climate threats remains paramount in the Indian and Pacific Oceans with sustainable management regimes being established. Climate-related impacts are expected to increase in magnitude over the coming decades, thus preliminary adaptation to climate change is valuable.

AbstractThis study was conducted to address algal toxins using potassium permanganate through the control of biomass growth of algae under following conditions value 25±1 °C illumination intensity value 245microeinstein/m2/s, using the culture media Chu-10 Modified for the purpose of development algae. We treated algal toxins belonging to groups of Neurotoxins, Hepatotoxins, Pyriproxyfen, Emodin, Brevetoxins-10 (A) and Cytotoxins using concentrations of potassium permanganate represented by 2, 4, 8 and 16mg/l with alum concentration for each concentration of 30mg/l, as the removal rate reached to 100% of the toxin blooms in concentrations of 8 and 16mg/l respectively, through the examination of algal toxins mediated by GC–MASS compared to the standard, which diagnosed a range of algal toxins with C2H3C12NO formulas of synthetic C9H13NO2, C18H27NO3, C11H12N2O6, C11H17N3O, C10H17N3O, C9H15Br2NO, CH4N2O2, C11H17NO2, C13H9BrN2O3, C3H7NO4S, C20H29NO3, C15H10O5, C4H8O2 and C2H2Cl3NO the concentrations 2 and 4mg/l turned toxic compounds into non-toxic compounds represented by C7H6O2, C5H6N2O, C12H11ClO4, C6H6O2, C12H10O4, C10H17N, C4H6O2 and C5H6N2O. The results showed reduced primary productivity of algae chlorophyll a result of substance to stop chloroplast for vital activity through the influence of the concentration of potassium permanganate values 0.571, 1.142, 0.583 and 1.713mg/l respectively, compared to the standard of 114.2mg/l. As diagnosed types of Algae producing toxins are represented by Microcystis aeruginosa, Microcystis flosaquae, Oscillatoria amoena, Oscillatoria amphibian, Oscillatoria boryana, Oscillatoria limnetica, Oscillatoria perornata, Phormidium ambiguum, Lyngbya digueti, Lyngbya major, Lyngbya nordgaadii, Lyngbya spirulinoides, Nostoc carneum, Nostoc spongiforme, Anabaena augstumalis, Chroococcus indicus and Chroococcus minor, as the dry weight of live Algae producing toxins is 17.342g/l.