• Energy Research
• Open Access close
• Restricted close
• Open Source close
• IN close
• CA close
• AT close

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.

Contrasting physiological mortality with predator-induced mortality is of tremendous importance for the population dynamics of many organisms but is difficult to assess. I performed a meta-analysis using planktonic ciliates as model organisms to estimate the maximum physiological mortality rates (δmax) across pelagic ecosystems in relation to environmental and biotic factors. Data were compiled from published numerical response (NR) experiments and experimentally determined rates of decline (ROD). Variables reported are ciliate species and order, ciliate specific growth rates (rmax), prey species, temperature, habitat (marine vs freshwater), the coefficients of the numerical response experiments, and reported or calculated ciliate mortality rates. The median δmax of planktonic ciliates was 0.62 d−1 and did not differ between marine and freshwater species. Maximum ciliate mortality rates were species-specific and affected by their rmax, cell volume, and ability to encyst. Cyst-forming species had, on average, higher δmax than species unable to encyst. Maximum mortality rates of ciliates were positively related to rmax but appeared unaffected by temperature. I conclude that (i) in the ocean, physiological mortality is more critical for controlling ciliate population size than ciliate losses imposed by microcrustacean predation, but (ii) in many lakes, the opposite holds; (iii) cyst-formation is an effective ciliate trait to cope with the high mortality of motile cells upon starvation. The lack of a temperature effect on δmax deserves further study; if correct, planktonic ciliates may take advantage of rising ocean and lake temperatures, with important implications for the pelagic food web. I used ISI Web of Science and Google Scholar to search for experiments that measured growth and mortality rates of ciliates as a function of prey concentration (i.e. numerical responses). The search terms were “growth (rate)” or “numerical response” in combination with “ciliate*” to search for numerical response experiments and “starvation” or “starved” in combination with “ciliate*” to search for mortality experiments. In addition, I searched the literature cited in these publications for further datasets. I considered only planktonic ciliates. When studies did not report all parameters of the NR curve, the data were extracted from figures with DataThief III or WebPlotDigitizer (Version 4.6) and fitted with a modified Michaelis-Menten equation that included the threshold prey concentration (P’) as an additional parameter. Mortality rates obtained by ROD experiments used the δmax reported in the respective study or calculated δmax from the maximum rate of decline after digitizing the data from the original curves, as described above. The literature search yielded δmax reported from 41 studies investigating 56 species or strains in 81 NR experiments and 19 ROD experiments. The final dataset (n = 77) included 37 studies and 48 species. I analyzed the dataset using the R Statistical Software using the packages lme4, lmerTest, AICcmodavg, and MuMIn. # Physiological mortality rates of planktonic ciliates ## Description of the Data and file structure I used ISI Web of Science and Google Scholar to search for experiments that measured growth and mortality rates of ciliates as a function of prey concentration (i.e. numerical responses). The main dataset containing available experimental studies reporting ciliate species, experimental temperature, prey species, ciliate maximum growth rates, ciliate cell volumes, habitat of ciliate isolation, method of study and reported or calculated ciliate mortality rates are reported in the 'Dataset_v2.xlsx' file. This is the main document. Missing data codes: N.A. = not available; n/a = not applicable. More details about each column of the main document can be found in the 'Units_table.xlsx' file. Details on the references - i.e. authors, publication year, title, journal/book, volume and page/article numbers - used to compile this dataset can be found in 'References.xlsx'. ## Sharing/access Information The individual data were derived mainly from the ISI Web of Science. The data compilation is novel. Excel, R

With the extensive growth in technology, healthcare sector has benefitted a lot recently. Looking into the academic research and validation in the area of medical image processing and visualization, many platforms and the open-source resources are available. Insight toolkit (ITK) and visualization toolkit (VTK) are extensively used for medical image processing and 3D visualization respectively. Resources used to develop an application using ITK-VTK and same resources be used to deliver it to the users such as, clinicians, doctors etc. This can be achieved by using respective hardware and the infrastructure. In the proposed article, the infrastructure and resource used to build and deploy the application and remote access given to the users are elucidated.

Siderophores are low molecular weight secondary metabolites produced by microorganisms under low iron stress as a specific iron chelator. In the present study, a rhizospheric bacterium was isolated from the rhizosphere of sesame plants from Salem district, Tamil Nadu, India and later identified as Bacillus subtilis LSBS2. It exhibited multiple plant-growth-promoting (PGP) traits such as hydrogen cyanide (HCN), ammonia, and indole acetic acid (IAA), and solubilized phosphate. The chrome azurol sulphonate (CAS) agar plate assay was used to screen the siderophore production of LSBS2 and quantitatively the isolate produced 296 mg/L of siderophores in succinic acid medium. Further characterization of the siderophore revealed that the isolate produced catecholate siderophore bacillibactin. A pot culture experiment was used to explore the effect of LSBS2 and its siderophore in promoting iron absorption and plant growth of Sesamum indicum L. Data from the present study revealed that the multifarious Bacillus sp. LSBS2 could be exploited as a potential bioinoculant for growth and yield improvement in S. indicum.

Production of biodiesel from yellow grease (waste cooking oil and waste animal fats) is fast emerging as a promising alternative to address the twin challenges before the biodiesel industry today-fluctuation in prices of vegetable oil and the food versus fuel debate. Yellow grease has a high percentage of free fatty acids (FFA) and proves to be an unsuitable feedstock for biodiesel production from commercially viable alkali-catalyzed production systems due to saponification problems.“Green” methodologies based on heterogeneous solid acid catalyzed reactions have the potential to simultaneously promote esterification and transesterification reactions of yellow grease to produce biodiesel without soap formation and offer easy catalyst separation without generation of toxic streams. This paper presents kinetic studies for the conversion of model yellow grease feeds to biodiesel using a heteropolyacid supported on alumina (HSiW/Al2O3) using a batch autoclave. Three model yellow grease feeds were prepared using canola oil with added FFA such as palmitic, oleic and linoleic acid. A pseudo homogeneous kinetic model for the parallel esterification and transesterification was developed. The rate constants and activation parameters for esterification and transesterification reactions for the model yellow grease feeds were determined. The rate constants for esterification are higher than the transesterification rate constants. The kinetic model was validated using the experimental biodiesel data obtained from processing a commercial yellow grease feed. The kinetic model could be used to design novel processes to convert various low-value waste oils, fats and non-food grade oils to sustainable biodiesel. Keywords: Yellow grease, Canola oil, Free fatty acids, Heteropolyacid, Kinetics for esterification and transesterification

Second-generation bioethanol can be produced from various lignocellulosic biomasses such as wood, agricultural or forest residues. Lignocellulosic biomass is inexpensive, renewable and abundant source for bioethanol production. The conversion of lignocellulosic biomass to bioethanol could be a promising technology though the process has several challenges and limitations such as biomass transport and handling, and efficient pretreatment methods for total delignification of lignocellulosics. Proper pretreatment methods can increase concentrations of fermentable sugars after enzymatic saccharification, thereby improving the efficiency of the whole process. Conversion of glucose as well as xylose to bioethanol needs some new fermentation technologies to make the whole process inexpensive. The main goal of pretreatment is to increase the digestibility of maximum available sugars. Each pretreatment process has a specific effect on the cellulose, hemicellulose and lignin fraction; thus, different pretreatment methods and conditions should be chosen according to the process configuration selected for the subsequent hydrolysis and fermentation steps. The cost of ethanol production from lignocellulosic biomass in current technologies is relatively high. Additionally, low yield still remains as one of the main challenges. This paper reviews the various technologies for maximum conversion of cellulose and hemicelluloses fraction to ethanol, and it point outs several key properties that should be targeted for low cost and maximum yield.

The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.

<abstract> <p>The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.</p> </abstract>