Democratizing Energy, Energizing Democracy: Central Dimensions Surfacing in the Debate

This perspective piece sets to contribute to the academic and practitioner debates around energy democracy in the age of climate crisis. In tackling the present-day energy transition challenges in a democratic, equitable, just and sustainable manner, we argue that sound research shall take alternative currents to centralized access to and control of energy decision making at its core as well as exploring new and novel ways to deal with production and distribution issues. Critical research on new actors, materialities, values, worldviews, democracy, and justice on energy is well-situated to meet these challenges. Navigating value systems, exploring enabling or disabling material qualities, focusing on ruptures, continuities, and emerging new geographies all carry a promise in critical energy research. We contend that ‘normative, political and embodied’ research strategies must be used to defeat the far right’s the particularly mischievous approach to planetary futures.Peer ReviewedPostprint (published version

The effects of different growing media on flowering and corm formation of saffron (Crocus sativus L.)

The objective of this research was to determine the effects of different growing media on saffron growth and corm formation in greenhouse conditions. In the experiment four different treatments were used.These were (1) soil+sand (control), (2) soil+sand+manure, (3) soil+sand+manure applied as a double layer above and bottom of corm bed, and (4) soil+sand+manure+ nitfojips-K. The results revealed thateffects of the growing media on most of the characters were significant. Cow manure mixtures especially with double layers had a positive effect on the flower and stigma weight. Average flower weight per plant change between 0.157 - 0.240 g. The corm size did not change significantly intreatments 1, 2 and 3. However, in treatment 4, both corm weight and corm size were significantly lower than they were in the other treatments. The results suggested that the growing medium was one of the important factors for saffron flower and corm formation

Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat

The sunn pest, Eurygaster integriceps Put. (Hemiptera: Scutelleridae), also known as sting or cereal pest, is one of the most economically important pests of wheat in the world. In this study, a collapsiblenylon cloth cage experiments were conducted to determine the feasibility of using remote sensing techniques to detect stress in wheat caused by the density of sunn pests. The results show we candetect the amount of stress in wheat caused by different life stages of sunn pest with a hand-held radiometer. Normalized difference vegetation index (NDVI) based indices; NDVIsg, NDVId, NDVIr, andstructure insensitive pigment index (SIPI) were chosen out of 19 indices initially tested. The NDVI based vegetation indices derived from hyperspectral data, recorded by a hand held spectroradiometer, were used to determine the predicted indices using the initial number of Sunn Pest (NOSP). Overall, r2 values of all predicted indices calculated for 3rd instars were lower than those of 4th and adult stage. When r2was considered separately, predicted NDVIr index value (87.4) was the highest and predicted SIPI index value is lowest (80.7) in 3rd instars. The highest r2 value was obtained in adult stage of sunn pest isNDVIsg (96.9) compare with NDVId (95.5), NDVIr (92.4) and SIPI (94.2). It was also concluded that remote sensing could detect not only the different stages pest damage on wheat, but also the number of sunnpest stages density affect in controlled experiments

The effect of autumn and spring planting time on seed yield and protein content of chickpea genotypes

The objective of this study was to investigate the effects of autumn and spring plantings on seed yield and quality of chickpea genotypes. Fourteen chickpea genotypes were grown over the consecutive twogrowing seasons in northwest Turkey. The results showed that planting time had significant effects on the investigated traits (P < 0.05). Significant differences for yield were observed between autumn (2050kg ha-1) and spring (1588 kg ha-1) plantings. Line 99 - 59C was the highest yielding genotype both in autumn (2662 kg ha-1) and spring (2000 kg ha-1) plantings. Seed analysis revealed that crude proteincontent in spring planting (23.2%) was higher than in autumn planting (20.5%). The highest protein content (21.1%) was produced by genotype P-2 in autumn planting whereas line 97 - 73C had thehighest content (24.6%) in spring planting. In addition, yield was highly and positively correlated with C/N ratio (r = 0.20**) whereas it was negatively correlated with protein (r = -0.19**). As a result, plantingtime influenced yield, yield components and chemical composition of the genotypes. Autumn planting had advantages for higher seed yield and consequently higher amount of protein per harvested area

Assessment of the effect of salinity on the early growth stage of the common sunflower (Sanay cultivar) using spectral discrimination techniques

Salinity is one of the main limiting factors for agricultural production. This is especially true in arid and semi-arid regions of the world like Turkey. The objective of this study was to determine if the effect ofsalt concentration on the physiological and physiological features of the sunflower (Helianthus annuus L) could be measured using remote sensing techniques. Sunflower seedlings were grown undercontrolled conditions and irrigated with ½ Hoagland Solution containing three different concentrations of NaCl (salt) (0.0, 0.5, 1.0 and 1.5%). The results showed that plant growth decreased proportionallywith increasing levels of NaCl. Chlorophyll concentration and a Normalized Difference Vegetation Index (NDVI) were derived for the plants using a spectroradiometer. There was found to be a significant (r2 = 0.76) correlation between chlorophyll and NDVI values. Therefore, factors that can be derived through remote sensing such as NDVI and chlorophyll can be used to indirectly demonstrate the impact salinity has on sunflower plants. Therefore, agriculturalists can assess growth rate changes caused by salinity using remote sensing techniques

Ground truth deficiencies in software engineering: when codifying the past can be counterproductive

Many software engineering tools build and evaluate their models based on historical data to support development and process decisions. These models help us answer numerous interesting questions, but have their own caveats. In a real-life setting, the objective function of human decision-makers for a given task might be influenced by a whole host of factors that stem from their cognitive biases, subverting the ideal objective function required for an optimally functioning system. Relying on this data as ground truth may give rise to systems that end up automating software engineering decisions by mimicking past sub-optimal behaviour. We illustrate this phenomenon and suggest mitigation strategies to raise awareness

Negative Even Grade mKdV Hierarchy and its Soliton Solutions

In this paper we provide an algebraic construction for the negative even mKdV hierarchy which gives rise to time evolutions associated to even graded Lie algebraic structure. We propose a modification of the dressing method, in order to incorporate a non-trivial vacuum configuration and construct a deformed vertex operator for $\hat{sl}(2)$, that enable us to obtain explicit and systematic solutions for the whole negative even grade equations

Single Enteral Loading Dose of Phenobarbital for Achieving Its Therapeutic Serum Levels in Neonates

Aim To investigate whether therapeutic serum drug levels may be achieved with a single enteral loading dose of phenobarbital. Methods The study was performed at the Mersin University Hospital in Turkey between April 2004 and August 2006, and included 29 newborn babies with seizure. After the acute treatment of the seizure with midazolam at a dose of 0.1 mg/kg, phenobarbital was administered by orogastric route at a loading dose of 20 mg/kg. Serum phenobarbital concentrations were measured at 0.5, 3, 6, and 12 hours after the loading. Serum phenobarbital levels between 10- 30 μg/mL were considered as the therapeutic range. Results The serum phenobarbital levels reached therapeutic values in 9 (31%), 19 (66%), 21 (72%), and 23 (79%) patients at 0.5, 3, 6, and 12 hours after loading, respectively, while they did not reach therapeutic values in 6 patients (21%) after 12 hours. Four of the patients in whom there was no increase in serum phenobarbital levels had hypoxic- ischemic encephalopathy. Conclusion Enteral loading of phenobarbital can achieve therapeutic serum levels in the large majority of newborn babies with seizure and may be safely used in babies with the intact gastrointestinal tract

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio