Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey’s mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10⁻¹⁴, 28.35 × 10⁻²⁸, and 2.5 × 10⁻¹⁴, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested

Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island

This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R2, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data

Molecular Simulations of Pathways and Kinetics for Protein-protein Binding Processes

Protein-protein binding processes are crucial for biological functions and characterizing these processes fully has been a challenge in biophysics. In this work I use weighted ensemble path sampling method coupled with molecular simulations of varying levels of detail to answer long standing questions regarding protein-protein binding. In Chapter 3, I investigate the effects of preorganization on association between an intrinsically disordered peptide fragment of tumor suppressor p53 and the MDM2 protein using flexible residue level models. I simulated the binding process between p53 and MDM2 with varying degrees of preorganization in p53 and determined that the association rate constant of p53 peptide does not depend on the extent to which the peptide is preorganized for binding MDM2. In Chapter 4, I apply simulations with flexible molecular models to directly compute the “basal” kon for the association of the two proteins barnase and barstar, in the absence of electrostatics. I simulated the binding process between exact hydrophobic analogues barnase and barstar and determined the extent with which the electrostatics enhance the basal kon. Finally, in Chapter 5, I have generated binding pathways of barnase and barstar using all-atom simulations with explicit solvent. This study not only enabled a more detailed characterization of the binding mechanism but also provided an opportunity to determine the role of solvent in the binding process. Water molecules are proposed to play a crucial role in binding of barnase and barstar since water molecules can be found at the binding interface in the crystal structure and they increase the interfacial complementarity. Overall, the work presented here demonstrates the power of the weighted ensemble strategy in making it practical to characterize binding processes that are otherwise unfeasible for standard simulation

Efficacy of seven Turkish diatomaceous earths against Callosobruchus maculatus (F.) (Coleoptera: Chrysomelidae: Bruchninae) on stored chickpea: Presentation

In this study, insecticidal efficacy of seven different local diatomaceous earths (DE) obtained from different deposits in Turkey together with two commercial DEs, Silicosec® (Biofa AG- Germany) and Desect® (Ep Naturals- America) against Callosobruchus maculatus (F.) (Coleoptera: Chrysomelidae: Bruchninae) an important pest of stored chickpea at five different concentrations (100, 300, 500, 1000 and 1500 ppm) was evaluated. The local DEs were coded as BGN, BHN, AG2N, AC2N, CB2N, CCN, FB2N. Mortality of the adults was assessed after 1, 3, 5 and 7 days of exposure, and consequently progeny (F1) production on treated chickpeas was recorded 42 days later. The tests were carried out under laboratory conditions of 25±1 °C, 55±5 % R.H. in a dark place. The most effective DEs after 1 day of exposure were CCN, AG2N and BHN causing 75%, 59%, 58% mortalities, respectively at 1500 ppm concentration. Silicosec®, Desect®, BGN, AC2N, applied at 1500 ppm concentration achieved 98-100% mortality of C.maculatus after 7 days of exposure, showing similar high insecticidal efficacy. The CCN, BHN, AG2N and CB2N caused 97-99% reduction in progeny (F1) production. Generally, increasing concentration significantly reduced the progeny production. In conclusion, this study has shown that three Turkish DEs, namelyCCN, AG2N and BHN highly toxic to C. maculatus after 3 days of exposurein comparison with commercial DEs Silicosec® and Desect®. These local DEs could be used in the management of pests of stored chickpea.In this study, insecticidal efficacy of seven different local diatomaceous earths (DE) obtained from different deposits in Turkey together with two commercial DEs, Silicosec® (Biofa AG- Germany) and Desect® (Ep Naturals- America) against Callosobruchus maculatus (F.) (Coleoptera: Chrysomelidae: Bruchninae) an important pest of stored chickpea at five different concentrations (100, 300, 500, 1000 and 1500 ppm) was evaluated. The local DEs were coded as BGN, BHN, AG2N, AC2N, CB2N, CCN, FB2N. Mortality of the adults was assessed after 1, 3, 5 and 7 days of exposure, and consequently progeny (F1) production on treated chickpeas was recorded 42 days later. The tests were carried out under laboratory conditions of 25±1 °C, 55±5 % R.H. in a dark place. The most effective DEs after 1 day of exposure were CCN, AG2N and BHN causing 75%, 59%, 58% mortalities, respectively at 1500 ppm concentration. Silicosec®, Desect®, BGN, AC2N, applied at 1500 ppm concentration achieved 98-100% mortality of C.maculatus after 7 days of exposure, showing similar high insecticidal efficacy. The CCN, BHN, AG2N and CB2N caused 97-99% reduction in progeny (F1) production. Generally, increasing concentration significantly reduced the progeny production. In conclusion, this study has shown that three Turkish DEs, namelyCCN, AG2N and BHN highly toxic to C. maculatus after 3 days of exposurein comparison with commercial DEs Silicosec® and Desect®. These local DEs could be used in the management of pests of stored chickpea

Centralized and Decentralized ML-Enabled Integrated Terrestrial and Non-Terrestrial Networks

Non-terrestrial networks (NTNs) are a critical enabler of the persistent connectivity vision of sixth-generation networks, as they can service areas where terrestrial infrastructure falls short. However, the integration of these networks with the terrestrial network is laden with obstacles. The dynamic nature of NTN communication scenarios and numerous variables render conventional model-based solutions computationally costly and impracticable for resource allocation, parameter optimization, and other problems. Machine learning (ML)-based solutions, thus, can perform a pivotal role due to their inherent ability to uncover the hidden patterns in time-varying, multi-dimensional data with superior performance and less complexity. Centralized ML (CML) and decentralized ML (DML), named so based on the distribution of the data and computational load, are two classes of ML that are being studied as solutions for the various complications of terrestrial and non-terrestrial networks (TNTN) integration. Both have their benefits and drawbacks under different circumstances, and it is integral to choose the appropriate ML approach for each TNTN integration issue. To this end, this paper goes over the TNTN integration architectures as given in the 3rd generation partnership project standard releases, proposing possible scenarios. Then, the capabilities and challenges of CML and DML are explored from the vantage point of these scenarios.Comment: This work was supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. 5200030 with the cooperation of Vestel and Istanbul Medipol Universit

FAST K-MEANS COLOR IMAGE CLUSTERING WITH NORMALIZED DISTANCE VALUES

Image segmentation is an intermediate image processing stage in which the pixels of the image are grouped into clusters such that the data resulted from this stage is more meaningful for the next stage. Many clustering methods are used widely to segment the images. For this purpose, most clustering methods use the features of the image pixels. While some clustering method consider the local features of images by taking into account the neighborhood system of the pixels, some consider the global features of images. The algorithm of K-means clustering method, that is easy to understand and simple to put into practice, performs by considering the global features of the entire image. In this algorithm, the number of cluster is given by users initially as an input value. For the segmentation, if the distribution of the pixels on a histogram is used, the algorithm runs faster. The values in the histogram must be discrete in a certain range. In this paper, we use the Euclidean distance between the color values of the pixels and the mean color values of the entire image for taking advantage of the every color values of the pixels. To obtain a histogram that consists of discrete values, we normalize the distance value in a specific range and round the values to the nearest integer for discretization. We tested the versions of K-means with the gray level value histogram and the distance value histogram on an urban image dataset getting from ISPRS WG III/4 2D Semantic Labeling dataset. When comparing the two histograms, the distance value histogram that is proposed in this paper is better than the gray level value histogram

Evaluation of the effect of topical and systemic ozone application in periodontitis: an experimental study in rats

Objective: The goal of the present study was to determine the effect of systemic and topical ozone application on alveolar bone loss (ABL) by evaluating the effect of Hypoxia-inducible factor −1 alpha (HIF-1-α) and receptor activator of NF-kB ligand (RANKL)-positive cells on histopathological and immunohistochemical changes in a rat periodontitis model. Methodology: Thirty male Wistar rats were divided into three groups: 1) Group C (control group); 2) Group SO (systemic ozone group) and 3) Group TO (topical ozone group). Experimental periodontitis was induced with a 3/0 silk suture placed at the mandibular left first molars of rats, and the suture was removed 14 days later. Ozone gas was injected intraperitoneally (0.7 mg/kg) in SO group. Topical ozone application protocol was performed using an ozone generator at 80% concentration (4th grade) 90- degree probe for the duration of 30 s. Both ozone applications were carried out for two weeks at intervals of two days. Histomorphometric and immunohistochemical analysis were performed. Results:ABL was significantly lower in Group SO compared to Group C (p: 0.0052). HIF-1α- positive cells were significantly lower in Group TO than in Group C (p: 0.0043). RANKL-positive cells were significantly lower in Group SO and in Group TO compared to the control group (p: 0.0033, p: 0.0075, respectively). Conclusion: Both ozone applications decreased RANKL-positive cell counts, TO application decreased HIF-1-α positive cells counts, and SO application was found to be more effective in reducing ABL compared to control group

Efficacy of injectable platelet-rich fibrin in the erosive oral lichen planus: a split-mouth, randomized, controlled clinical trial

Objective: Our study compared the effects of injectable platelet-rich fibrin (i-PRF) with those of corticosteroids in the treatment of erosive oral lichen planus (EOLP). Methodology: This split-mouth study included 24 individuals diagnosed histopathologically with bilateral EOLP. One bilateral lesion was injected with i-PRF, whereas the other was injected with methylprednisolone acetate in four sessions at 15-day intervals. Visual analog scale (VAS) for pain and satisfaction, oral health impact profile scale-14, and the lesion size were used. Results: The intragroup comparisons showed a significant decrease in VAS-pain and lesion size in both the i-PRF group (from 81.88±17.74 to 13.33±18.34, and from 4.79±0.41 to 1.88±1.08, respectively) and the corticosteroid group (from 80.21±17.35 to 23.33±26.81, and from 4.71±0.46 to 2.21±1.35, respectively) in the 6th month compared to baseline (p<0.001). Moreover, VAS-satisfaction increased significantly in both the i-PRF group (from 26.67±17.8 to 85.63±16.24) and the corticosteroid group (from 28.33±17.05 to 74.38±24.11) in the 6th month compared to baseline (p<0.001). However, no significant difference in any value occurred in the intergroup comparisons. Conclusion: In patients with EOLP, both methods decreased pain and lesion size similarly, and both increased satisfaction. Therefore, the use of i-PRF may be considered an option in cases refractory to topical corticosteroid therapy. Biochemical and histopathological studies are required to reveal the mechanism of i-PRF action in EOLP treatment