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In today's examination of optimal facade design for buildings, facade performance has a significant influence on cooling energy consumption. Cooling degree days (CDD) is regarded as a basic and straightforward approach for predicting building cooling energy consumption. Accurate seasonal trend forecasting of the CDD plays a vital role in estimating the energy demand for building cooling. Unfortunately, there are no published statistics or research on Turkey's CDD data for the following years. The monthly mean CDD data of Türkiye were created in this study utilizing data from the Turkish State Meteorological Service for the historical period from 1991 to 2022. The Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was used to model the CDD data, and a time series forecast was performed. The SARIMA model was used to forecast the future trend of CDD from 2023 to 2040. The monthly and yearly CDD statistics were presented as figures, while the yearly CDD maps for Türkiye were created using the ArcGIS software program. Finally, CDD zones that require more attention to the most appropriate facade design in terms of energy efficiency for buildings in Türkiye were identified and classified. [ABSTRACT FROM AUTHOR]

The impact of the climate and environmental problems experienced in the world with the Industrial Revolution has prominently begun to be felt today, and the consequences of climate change on the environment and public health have now become visible. The increase in greenhouse gas emissions resulting from human activities, which is the main cause of global climate change, caused the global surface temperature to be 1.1 °C higher between 2011 and 2020 compared to 1850–1900. In parallel with this global problem, the transition to clean energy has increased significantly with Russia's invasion of Ukraine, more aggressive energy and climate policies, technological developments, and increasing concerns about energy security. In this study, global climate change indicators, including land and sea surface air temperatures, sea level rise, sea ice extent, ocean heat content, surface humidity, and total column water vapor, are reviewed and updated in parallel with a comprehensive analysis of the progress in renewable energy. The results showed that if no measures are taken to reduce human-induced greenhouse gas emissions, the global average temperature will increase further in the coming years and the negative effects of other climate parameters will be felt even more. It has been emphasized that limiting human-induced global warming requires renewable and sustainable energy sources and net zero CO2 emissions and that the simultaneous adoption of emission reduction and adaptation strategies will be the most effective economic and technical solution to the global warming problem. [ABSTRACT FROM AUTHOR]

Purpose: Fasciotomy is a surgical procedure that involves the incision of fascial compartments in the body to relieve pressure, prevent tissue damage, and maintain blood flow. This study aimed to investigate the effectiveness of the Bogota Bag technique in closing fasciotomy wounds in patients with lower limb compartment syndrome. Methods: A prospective cohort study was conducted between October 2022 and October 2023 to document our experience in employing the Bogota Bag technique for fasciotomy closure. The study included the evaluation of medical files from fifteen patients aged 17 to 61. Results: The outcomes of the study present the initial series of limb fasciotomies treated with the Bogota Bag technique. Fifteen patients (14 male, 1 female) were included in the study. The average age of the patients was 34.73 ± 13.9 years and the average hospitalization was 8.33 ± 3.2 days. The average closure time of fasciotomy is 3.6 ± 1.4 days. Conclusion: This report makes a significant contribution as the first documented series of limb fasciotomies treated with the Bogota Bag technique. This method exhibits simplicity in execution, cost-effectiveness, and a low incidence of complications. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to analyze and compare gait patterns and deviations at long-term follow-up in children who received medial open reduction (MOR) before 18 months for unilateral or bilateral hip developmental dysplasia (DDH). Methods: A retrospective chart review was conducted on children who underwent MOR. The study population was divided into two groups: the unilateral group, including unilateral (five children with unilateral) and bilateral (five children with bilateral DDH). Ten healthy children were recruited for the control group. Spatiotemporal, kinematic, stiff-knee gait (SKG), and kinetic gait characteristics were analyzed. Results: Stance time was significantly shorter in both the unilateral (median [IQR]; 590 ms, [560.0–612.5] and bilateral (575 ms, [550–637.5]) groups than in the control group (650, [602.5–677.5]) (p < 0.001), whereas swing time did not differ substantially (p = 0.065) There was no considerable difference in the mean knee flexion at swing between the unilateral (31.6°, [30–36]) and control (30.11°, [27.8–33.6] groups (p > 0.05), but the bilateral group (28.5°, [24.9–32.1]) showed the lower values than the other groups (p < 0.001 for bilateral vs unilateral group; p = 0.008 bilateral vs unilateral group). All the SKG parameters significantly differed among the groups in multi-group comparisons (p < 0.001 for each parameter). Three children had borderline SKG, and two had not-stiff limbs in the unilateral group. In the bilateral group, four children had stiff limbs, and one had borderline SKG. Most kinetic gait parameters were not statistically different between groups (p > 0.05). Conclusion: This study has revealed notable deviations in gait patterns of children with DDH treated by MOR at long-term follow-up compared to healthy children's gait. MOR could negatively affect pelvic motion during gait due to impaired functions of the iliopsoas and adductor muscles, and SKG can be encountered secondary to iliopsoas weakness. [ABSTRACT FROM AUTHOR]

Climate change and natural resource depletion have reinvigorated the relevance of technical advancements in energy efficiency, which has been emphasized by the Paris Climate Accord that includes a framework for carbon neutrality. In that effort, the governments have been allocating a considerable amount of budget shares to research and development relating to energy efficiency and green energy. Thus, this study primarily examines the impact and effectiveness of research and development on energy efficiency and energy sources (renewables and fossil fuels) on CO2 emissions in the European region using a novel Method of Moments Quantile Regression (MMQR). Our data cover annual observations of 9 EU countries, including Austria, Denmark, Finland, France, Germany, Netherlands, Norway, Spain, and Sweden, based on the period 1990–2021. Our empirical findings confirm that the U-shaped Environmental Kuznets Curve occurs across all quantiles at 1% significance levels. The findings reveal that energy consumption increases CO2 emissions across the lower to higher quantiles. We discover that (i) R&D on renewable energy cuts CO2 emissions just at the median and higher quantiles, (ii) R&D on Fossil Fuels decreases CO2 emissions just at the lower quantile, and (iii) R&D on Energy efficiency lowers CO2 emissions at both the median and higher quantiles. Therefore, empirical findings suggest that policymakers should maintain their incentives for energy efficiency R&D and renewable R&D to achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

Today, air temperature (AT) is the most critical climatic indicator. This indicator accurately defines global warming and climate change, despite the fact that it has effects on different things, including the environment, hydrology, agriculture, and irrigation. Accurate and timely AT forecasting is crucial since it supplies more significant details that can create credibility for future planning. This study proposes innovative hybrid models that integrate a convolutional neural network (CNN) with a long short-term memory (LSTM) neural network and a gated recurrent unit (GRU) to perform one-day ahead AT predictions. For this purpose, the daily AT data obtained from 2012 to 2019 at the Adana and Ankara meteorological stations over Türkiye under Continental and Mediterranean climate conditions are used. The hybrid GRU-CNN and LSTM-CNN models are compared with various traditional statistical and machine-learning models such as feed-forward neural network, adaptive neuro-fuzzy inference system, autoregressive moving average, GRU, CNN, and LSTM. The success of the prediction models is evaluated utilizing various statistical criteria (MAE, RMSE, NSE, and R2) and visual comparisons. The results show that the proposed hybrid GRU-CNN and LSTM-CNN models in one day-ahead AT predictions yield the best results among all models with high accuracy. [ABSTRACT FROM AUTHOR]

Construction and demolition waste consists of residue and discarded materials that result from construction, renovation, remodelling, and demolition activities. This waste typically includes various materials, such as concrete, wood, steel, bricks, drywall, asphalt, glass, plastics, and other construction-related materials. This research quantified the environmental consequences of managing earthquake-generated construction and demolition waste in Turkey using diverse techniques. The study employed a life cycle assessment to compute these impacts. First, the impacts of the currently used landfill process were calculated and then a scenario in which concrete is reused, recyclable wastes are separated and recycled, and the remaining waste is sent to a landfill was created. According to the findings, it is seen that if all the debris is sent to a landfill, 11.9 t CO2eq will be generated in the short term, while 18,428.3 t CO2eq can be saved, especially by reusing concrete. These findings may vary due to the involvement of different unforeseen processes, but they show that although it is often very difficult to utilize such wastes, especially those generated as a result of unusual disasters, such as earthquakes, very significant environmental gains can be achieved through appropriate treatment. [ABSTRACT FROM AUTHOR]

Soil-borne fungal infections reduce yields, and result in severe economic losses by destroying the root, stem, and vascular components of many plants. For disease management that is efficient, inexpensive, and has no adverse effects on the environment, the use of biocontrol materials is crucial. In this study, three different biocontrol agents were evaluated for their ability to reduce the stress that Fusarium oxysporum f.sp. lycopersici (FOL) causes in tomato plants. These agents included compost made from locally accessible green wastes, a strain of bacteria (Bacillus subtilis strain ®Serenade), and arbuscular mychorrizal fungi (®Mikostar). Using correlation and PCA analysis, the effects of these agents on FOL disease were further linked to nutrient intake, physicochemical properties of plants, and the rhizosphere. Five different applications were used in the experiment, which was carried out in controlled conditions utilizing a completely random experimental design: pathogen-inoculated positive control (A), non-inoculated negative control (B), A + compost (C), A + AMF (D), and A + Bacillus subtilis (E). There were statistically significant differences (p < 0.05) between groups in terms of the indicators of plant disease as well as the other parameters studied, according to ANOVA-Tukey analysis. Compost proved to be the most effective treatment among the three materials evaluated, reducing disease severity by up to 100%, followed by AMF (36.4%) and Bacteria (34.1%). The LAI, shoot fresh and dry weight, SPAD values, plant nutrient contents (N, Ca, Mg, Mn), and parameters assessed in the rhizosphere (B, Ca, Fe, K, Mg, Dehydrogenase, and CO2 respiration) were the highest in pots treated with compost, and all had statistically significant negative correlations with disease severity (p < 0.05). The results show that compost made from local green waste could be effective in reducing tomato FOL disease. [ABSTRACT FROM AUTHOR]

Awareness of age-related change (AARC) is a multidimensional concept that evaluates the experiences of aging. Accordingly, this study aimed to adapt the long (AARC-50) and short (AARC-10) forms of the AARC scale for the Turkish population and evaluate their psychometric testing. This study included 570 individuals aged 40–92 years. The data of this study were obtained online by the snowball method. Data for this study were collected using the personal information form, AARC scale, and SF-12 quality of life scale. The form was translated and back-translated, expert opinions were obtained, and criterion-related validity, factor, test–retest, and reliability coefficient analyses were performed. Notably, the Cronbach's alpha values of the two-factor 50- and 10-item scales were 0.923 and 0.717, respectively. The correlation of AARC with SF-12 varies between 0.035 and 0.528. In addition, the test–retest reliability score and content validity index were 0.90. Regarding the results of the factor analyses for the long and short forms, the long form demonstrated good results in terms of the chi-square divided by degrees of freedom and root mean square error of approximation, whereas the short form demonstrated good results in terms of goodness (comparative, normal, and adjustment goodness-of-fit index) and standardized root mean square residual. This study found that the AARC-50 and AARC-10 scales are valid and reliable tools for evaluation in the Turkish population. In the future studies, researchers can choose the appropriate form based on the conditions of their studies. [ABSTRACT FROM AUTHOR]

In recent years, high energy costs, increases in carbon emissions, and concerns about energy supply security have led countries to prioritize renewable energy sources in their sustainable energy policies. The selection and ranking of alternative renewable energy sources is a critical issue in establishing an effective energy policy and ensuring environmental improvement at the national and global levels. This study evaluates Turkey's best renewable energy options using the institutional fuzzy assessment based on distance to mean solution (IF-EDAS) method and aims to find Turkey's best renewable energy alternative. The decision model alternatively uses wind, solar, geothermal, biomass, wave, hydroelectric, and hydrogen energy options. According to the study's empirical findings, while the best alternative renewable energy source for Turkey is solar energy, the best criterion in terms of criteria evaluation is "Technology Maturity". The study also carried out sensitivity analysis, and the results were shared. [ABSTRACT FROM AUTHOR]

Summary: Anal fistulas represent an inflammatory condition with a connection formed between the anal canal and the perianal skin. Surgery aims to eradicate the fistula while preserving sphincter function. However, inadequate removal of fistula tracts often results in recurrence, and excessive division of the anal sphincter complex can result in fecal incontinence. Therefore, comprehensive preoperative evaluation of the fistula tract is crucial and contributes considerably to outcomes. MRI has emerged as a key method in patients with perianal fistula, but is expensive, uses intravenous contrast agent, and experience is needed to interpret images. Endoanal ultrasound (EUS) is cheaper and more practical than MRI, but is highly operator dependent and its role in suprasphincteric and complex fistulas is controversial. This study aimed to assess the usefulness of digital infrared thermal imaging (DITI) for evaluating of fistula-in-ano. DITI monitors infrared radiation emanated by a surface, in this case the perianal skin. The scientific rationale is based on the inflammatory state caused by perianal fistulization. Because one of the pathological findings of inflammation is increased local temperature due to altered blood flow, we hypothesized that by using a contrast agent colder than the human body, the perianal fistula tract could be visualized by DITI cameras. A protocol and simple diagnostic algorithm for evaluation of perianal fistulas are presented. [ABSTRACT FROM AUTHOR]

Wind turbine technology has advanced significantly during the past 10 years all around the world. To raise the turbine capacity factor, developers are building bigger, more dependable wind turbines with bigger hub heights and rotor diameters. Long-bladed, large-rotor, tall-tower, and low-specific power wind turbines with higher capacity factors (CFs) developed in this direction may become more important in the future energy systems since they can generate electricity in more economical conditions. The increase in nameplate capacity, hub height, and rotor diameter of onshore wind turbines around the world as well as in Turkey is, therefore, examined in this study. The impacts of this development in wind turbine size on the levelized cost of electricity, total installed cost, and CF are analyzed. Moreover, the effects of this expansion on turbine-specific power capacity, wind farm capacity, and the number of wind turbines per GW are thoroughly evaluated. To promote future advancements in large-scale wind turbine technology, these inspection results can be used to evaluate the technical and financial viability of turbines. They can also serve as a useful data source and a substantial advantage in the construction and development of new wind energy facilities. [ABSTRACT FROM AUTHOR]

Changes in soil quality as a result of climate change and grazing regimens have become an important issue for rangeland sustainability. Although there has been much research on the impacts of overgrazing on soil in highland rangelands, Turkey, there has been little on the effects of light grazing on the soils. This study aimed to examine select soil parameters in the lightly grazed (LGR) and ungrazed rangelands (UGR) to evaluate the sustainability of the rangeland soils under the light grazing. Soil penetration resistance (SPR) was measured at depths of 0–10, 10–20, and 20–30 cm. Soil samples were taken from the points where SPR measurements were made, and soil texture, soil bulk density (SBD), soil organic matter (SOM), soil organic carbon (SOC), and soil total nitrogen (TN) were determined. According to this study's findings, SOM, SOC, TN, SPR, and SBD levels were higher in LGR sites than in UGR sites. Although SPR was higher in the grazing site, it was discovered that this excess was smaller than the critical penetration resistance values. Because SPR is lower than the necessary resistance value for root development and SOM, SOC, and TN values are high, light grazing can be considered a sustainable approach. However, soil measurements should be conducted periodically to support sustainable rangeland management due to climate change. [ABSTRACT FROM AUTHOR]

Climate action-based assumptions and tradable characteristics underpinned the development of climate change futures contracts, which are related to carbon and climate markets. Therefore, this paper examines return and volatility spillover between climate change futures and carbon allowance futures using dynamic conditional correlation (DCC) and asymmetric dynamic conditional correlation (ADCC) models with daily and weekly frequency data. Considering the emergence of US market-based carbon futures and climate futures, this study explores bivariate optimal hedging strategies and optimal portfolio strategies. Using daily data, this study discovers unidirectional and positive return and volatility spillover from the carbon futures market to the climate change futures market, implying opportunities for diversification and hedging. The weekly analysis shows bidirectional and negative return spillover between the carbon futures market and the climate change futures market, implying opportunities for risk hedging. In addition, it also reveals unidirectional and positive volatility spillovers from the carbon futures market to the climate change futures market. The carbon market dominates the climate change futures market. The study also reveals that optimal portfolio strategies will be preferred over optimal hedging strategies. Therefore, this study offers practical implications for investors and portfolio managers. [ABSTRACT FROM AUTHOR]

Heating degree-days (HDD) is a unit to measure the duration of cold weather during a 24-h period. Accurate HDD forecasting is crucial in terms of determining the energy needed for the heating of buildings. This work aims to introduce a model for one month-ahead forecasting of HDD time series data and their short-term future projection. Seasonal autoregressive integrated moving-average (SARIMA) and long short-term memory (LSTM) neural network models are utilized for this aim. Monthly HDD time series data from 2007 to 2021 in different climatic regions of Turkey are supplied by the Turkish State Meteorological Service (TSMS). Seasonally non-stationary HDD data for both models are converted to the position of stationary time series. Akaike information criterion (AIC) and Bayesian information criterion (BIC) values are utilized to optimize the models. The evaluation metrics of root-mean-square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) are used to assess the forecast accuracy of the models. As a result, forecasts with approximate 95% confidence limits are made for the 60-month period (January 1, 2022 to December 31, 2026) from the proposed models. The results show that the LSTM and SARIMA models can provide future forecastings for HDD time series data with satisfactory precision. [ABSTRACT FROM AUTHOR]

This study evaluated microleakage from class II cavities filled with bulk-fill composite preheated to different temperatures, applied at different thicknesses, and with different polymerization modes. A total of 60 mesio-occlusal cavity were drilled into the extracted human third molars at 2 mm and 4 mm thickness. Preheated bulk-fill composite resin (Viscalor; VOCO, Germany) was applied to the cavities at 68 °C and 37 °C after the adhesive resin was applied, and cured using standard and high-power light-curing modes of a VALO light-curing unit. An incrementally applied microhybrid composite was used as the control. The teeth were subjected to 2000 cycles of heating to 55 °C and cooling to 5 °C with a 30-s hold time. Then, they were immersed in a 50% silver nitrate solution for 24 h and scanned with micro-computed tomography. Scanned data were processed using the CTAn software. Two (2D) and three (3D) dimensional analyses of leached silver nitrate were performed. The data's normality was assessed using the Shapiro–Wilk test before comparisons using a three-way analysis of variance. In both 2D and 3D analysis, bulk-fill composite resin preheated to 68 °C and applied at 2 mm thickness showed less microleakage. In the 3D analysis, restorations applied at 37 °C and 4 mm thickness in high-power mode showed significantly higher values (p < 0.001). Preheated bulk-fill composite resin can be applied at 68 °C and effectively cured at both 2 mm and 4 mm thickness. [ABSTRACT FROM AUTHOR]

Metformin hydrochloride (MET) (N,N-dimethylbiguanide) is an antidiabetic drug and widely used for treating worldwide disease, Type 2 diabetes. MET shows significant benefits for human health such as reducing high blood sugar and cholesterol/triglyceride levels, treating for polycystic ovary syndrome and having anticancer and antiaging effects. In this study, a facile colorimetric determination of metformin in drug preparations was carried out by environmentally friendly gamma-cyclodextrin (γ-CD) stabilized silver nanoparticles (CD-AgNPs). Gamma-cyclodextrin was used for the formation of AgNPs as both the metal salt − reducing and nanoparticle − stabilizing agent. As a visual optical probe, well-dispersed spherical yellow-colored CD-AgNPs (at an average particle size of 10 ± 2 nm) showing maximal surface plasmon resonance absorption at 405 nm was synthesized by one-step method. In the presence of metformin, dispersed AgNPs get aggregated, and a controlled destabilization of CD-AgNPs and core-centered growth of Ag(0) atoms (20 ± 2 nm) were observed a result of host-guest interaction between CD-AgNPs and metformin. Non-covalent hydrogen bonding between the hydroxyl groups of CD-AgNPs (in the outer cavity) and the guanidine groups of MET enables the CDs to form a host-guest inclusion complexation followed by aggregation leading to color change from yellow to orange and an increase of the absorbance at 550 nm. Inclusion complex formation was characterized by using FTIR, UV-Vis spectroscopy, DSC and STEM techniques. Under optimized conditions, the absorbance ratio at A550 nm/A405 nm was linearly correlated to the concentration of metformin in the range of 0.2–1.25 µM. The proposed assay showed high sensitivity with 42 nM detection limit. The method was selective toward possible interferents (potassium, carbonate, sulfate, chloride and sodium ions, urea, glucose) and also successfully applied to conventional drug samples including MET. The results obtained by proposed, reference and HPLC assays were analyzed statistically using the two-way ANOVA test to demonstrate their agreement with each other at 95% confidence level (P = 0.05, Fexp = 0.068, Fcrit(table) = 6.943, Fexp < Fcrit(table)). MET contents (mg) of drug samples in tablet form were determined with high accuracy (REC% 97.3-100.7) and reproducibility (RSD% 1.49–2.78). It is noteworthy that the developed CD-AgNP − based colorimetric method seems to be of higher or comparable sensitivity compared to other nanoparticle − based optical assays. [ABSTRACT FROM AUTHOR]

Fusarium oxysporum f.sp. radicis lycopersici (FORL) and Fusarium solani (F.S.) are common fungi responsible for crown root rot and vascular wilt diseases that highly impact the development of plants, causing significant yield losses. This study investigated changes in the hyperspectral reflectance of normal and Fusarium (FORL and F.S.)-infected tomato plants in a growth chamber at different disease stages (3, 10, 16, 23, 31 and 37 days after inoculation (DAI)) using a spectroradiometer as an alternative to traditional approaches for the early identification and classification of such diseases. Raw spectra, significant wavebands obtained with the RELIEF algorithm and various statistical features extracted from raw spectra were used to classify healthy and infected plants using three different classification algorithms (CAs): decision tree, cubic support vector machine and k-nearest neighbor models. At different stages of the disease, the spectral bands such as 508, 711, 540, 717, 536, 644 nm and 705, 1883, 525, 518, 444, 522 nm were the most effective in distinguishing FORL and F.S.-inoculated plants from healthy plants, respectively. While FORL caused general stress in the plants, F.S. also had a negative physiological effect. All CAs proved highly successful in distinguishing healthy and diseased plants, with maximum classification accuracy achieved as early as 3 DAI. CAs using statistical parameters as input had higher accuracies than other CAs. Healthy and diseased plant classification was significantly different between the different CAs (p < 0.05), while DAI, pathogen type and inputs of the classification did not exhibit significant differences in classification (p > 0.05) according to ANOVA. [ABSTRACT FROM AUTHOR]

In this paper, we examine the origins and evolution of and main influences on research on within- and between-firm relationships among directors and executives. We conduct a review of the literature supplemented with bibliometric analyses that allow us to examine the complex citation network of publications spanning several decades and multiple disciplines (i.e., management, sociology, economics, accounting, and finance). First, we use main path analysis to uncover the primary paths of knowledge codification and diffusion of the literature on relationships among directors and executives. Next, we conduct bibliographic coupling analysis to map out current themes and developments in the field. In so doing, we provide an integrative framework for research on social processes and relationships of the corporate elite and discuss changes in the field's emphasis on various issues, identifying key theoretical and empirical advances, and offer directions for future research. Scholars and practitioners have raised concerns about problems underlying the presence and use of intra- and inter-firm relationships among members of the corporate elite. Our findings provide a balanced view of the positive and negative influence of intra- and inter-firm relationships of corporate elite members on organizational outcomes. [ABSTRACT FROM AUTHOR]

Intensive technological developments, rapid population growth and urbanization, and excessive use of nitrogen fertilizers have caused water resources to be contaminated substantially by nitrates in Turkey. The accumulated information should be evaluated to draw a nationwide attention to the problem. The aim of this review article was to highlight the importance of nitrate (NO3) contamination and to discuss the measures to be taken to mitigate the contamination across the nation. Agriculture, especially chemical fertilizers used in irrigated agriculture, was the most important source of NO3 in groundwater. Also, the industrial and domestic discharges substantially contributed to NO3 in both groundwater and surface waters in many cases. The most severe and widespread groundwater (e.g., 344 mg NO3 L−1 in İzmir, 476 mg L−1 in Afyon, 477 mg L−1 in Antalya, and 948.0 mg L−1 in Konya) and surface water contaminations (e.g., 293.8 mg NO3 L−1 in İzmir, 63.3 mg L−1 in Eskişehir, 89.8 mg L−1 in Edirne, and 90.6 mg L−1 in Sakarya) occurred in the regions where intensive agriculture, industrial development, and rapid urbanization were clustered. Well-established irrigation and fertilizer management plans are critical for reducing fertilizer-related NO3 contaminations in the irrigated agriculture. Special attention should be given to the regions where industrially and domestically contaminated running water bodies are in contact with groundwater. Discharge of wastewaters to the streams, creeks, rivers, and lakes should be prevented. Well-designed studies are needed to evaluate potential health effects, including the risk of cancer, of NO3 in drinking water. [ABSTRACT FROM AUTHOR]

Cooling Degree Days (CDD) is a convenient technique obtained by summing the cumulative air temperature differences that show how much deviation from the temperature is required for human comfort in a summer season. It is a basic and relatively simple measure for predicting the cooling energy requirements of the buildings. Accurate estimation of the seasonal trend of the CDD values is a crucial policy tool in determining the energy request for cooling the buildings and is critical to better energy management by decision-makers in the country. In this regard, planners or users need to develop appropriate and precise methods that allow them to forecast their future values based on their CDD historical time series data. For this reason, in this study, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is utilized to forecast the CDD values in some regions with high cooling demand in Türkiye. To accomplish this, monthly CDD data from January 1991 to December 2022 are obtained from the provinces of Adana, Adyaman, Antalya, Siirt, and Şanlurfa. First, the CDD values are modeled using the SARIMA time series approach, and then the models are employed to predict the future trends of the CDD values from 2022 to 2031. Obtained results show that with the continuation of global warming at the current rate, CDD values in all selected provinces will increase slightly by 2031, which will cause a change in building energy consumption policies. [ABSTRACT FROM AUTHOR]

The areas under turfgrass increase with the increase in rapid urbanization rate. Therefore, accounting for their contribution to greenhouse gas emission to atmosphere is needed. Information in this area is rather limited. This study aimed to quantify and model two years long term (2016–2017) temporal variations in soil CO2 emissions from newly established turfgrass area under semiarid conditions and to assess the influence of irrigation on the soil CO2 respiration. The measurements were obtained from irrigated and non-irrigated plots with respect to meteorological variables (air temperature, soil temperatures at different depths, rainfall and relative humidity) and the field soil moisture data using classical and Stepwise MLR models. Potential use of available auxiliary meteorological and field soil moisture data for the estimation of future CO2 emissions were also investigated using Artificial Neural Network (ANN) models whose accuracy was tested using independent data sets. Two years of continuous measurements revealed that CO2 emissions highly varied seasonally from 318 to 2621 kg ha–1 week–1 for irrigated and from 390 to 1711 kg–1 ha–1 week–1 for non-irrigated plots, respectively, with 31% more emissions from irrigated plots compared to non-irrigated ones. Inclusion of the soil field moisture data did not significantly impact the results. ANN models were able to moderately successfully estimate CO2 emissions. [ABSTRACT FROM AUTHOR]

Students with emotional and behavioral problems have poor academic and behavioral outcomes, both short- and long-term. Psychotropic medications are commonly used as an intervention with this population and their use has increased significantly. This study investigated adolescent and family variables associated with medication use among high school students with elevated emotional and behavioral problems, including those with a label of emotional and behavioral disorders. Participants included 548 adolescents from a larger randomized controlled trial, 184 (33.6%) of whom were receiving medication. Hierarchical linear modeling revealed that special education status, externalizing and internalizing behavior symptoms and participant race were significant student predictors of medication use. Parenting stress, family structure, positive parenting, and poor monitoring were significant family predictors of participants' medication use. These findings have implications for current practices in schools pertaining to screening and access to interventions as well as future research focused on the parent and student decision making process for choice of interventions. [ABSTRACT FROM AUTHOR]

Along with the growth and globalization of the whole world economy since the First Industrial Revolution, the production and use of fossil fuels have led to increased CO2 emissions and, ultimately, significant environmental degradation. The impact of globalization, economic growth, and renewable energy sources on CO2 may show trends with different turning points in developing countries, and estimations may need to follow Fourier-type functions to capture the frequency domain. Therefore, the aim of this study is to examine the effects of renewable energy production, economic globalization, and economic growth on CO2 emissions for Turkey in the period 1971–2006 with Fourier autoregressive distribution lag (ADL) cointegration, DOLS, and Fourier-Granger causality tests. The originality of this study is the estimation of a model of CO2 emissions with a Fourier-type function for the first time. The findings indicate a negative relationship between renewable energy production and CO2 emissions and a positive relationship between economic globalization and economic growth and CO2 emissions. In addition, according to the empirical results, there exists a one-way causality relationship between economic globalization to CO2 and economic globalization to renewable energy production, and there is evidence of a bidirectional causality relationship between economic globalization and economic growth in this study. [ABSTRACT FROM AUTHOR]

Background: Osteocartilaginous deformities in cleft rhinoplasties may be restored with numerous techniques. However, the anteroposterior (AP) diameter lengths of the nostrils may still be unequal and should also be addressed. A technique was designed to balance nostril AP diameter lengths and apical shapes. Materials and Methods: Balance between AP nostril diameters was accomplished by reduction of the AP diameter of the nostril at the non-cleft side by medial crural reduction and augmentation of the AP diameter of the nostril at the cleft side by three-parted mini-flap reconstruction at the soft triangle. The cleft-side AP diameter length was divided by the non-cleft-side AP diameter length of the same preoperative, per-operative and 1-year postoperative base view photographs of each patient, and "nostril balancing ratio" was obtained. The more this ratio was near to "one," the more the AP nostril diameters were equal. Results: Seventy-eight unilateral-cleft rhinoplasty were performed between January/2019 and May/2022. Forty-two of them required nostril AP diameter equalization. Twenty-nine patients were female, thirteen were male. Thirty-three of them were operated for a primary cleft rhinoplasty. Nine of them for secondary cleft rhinoplasty. Mean age was 28 years (22–39 years). Mean follow-up was 25 months (6–40 months). The preoperative, per-operative and postoperative mean "nostril balancing ratios" were 0.714 (0.621–0.813), 0.743 (0.721–0.752) and 0.971 (0.943–0.976), respectively. Conclusion: Balancing AP diameter symmetry with MCO at the non-cleft side and three-parted mini skin flap reconstruction at the cleft side may provide the satisfactory results. Three mini-flaps at the soft triangle may remold the nostril apex in an oval shape, which may result in a better shape symmetry. Level of Evidence IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Heat index (HI) is a temperature that the human body feels or perceives, as opposed to the physical air temperature measured by a thermometer. The goal of this study was to create a monthly average HI map in the external environment for Türkiye using a mathematical model developed by AccuWeather, an artificial neural network (ANN), and an adaptive neuro-fuzzy inference system (ANFIS) approach. In creating Türkiye's HI map, measurable parameters such as hourly dry bulb temperature, relative humidity, wind speed, and atmospheric pressure data from 81 measuring stations were used. According to the simulations, due to the lack of measurable data, HI, which cannot be computed in each location, can be efficiently predicted using geographical inputs to ANN and ANFIS methods. The outcomes demonstrated that predicted HI values with the developed ANN and ANFIS models are in good agreement with the actual HI calculated values using the AccuWeather method for all cities, but the accuracy of the machine learning models varies depending on the city's measured data. Although MAE and RMSE values for generated ANN and ANFIS machine learning models are within acceptable ranges, ANN outperforms ANFIS for all cities tested during the estimation of HI values. ANN and ANFIS models are capable of correctly predicting HI values when the month of the year, latitude, longitude, and altitude values are provided. This eliminates the need for excessive testing and saves time, labor, and financial resources. [ABSTRACT FROM AUTHOR]

Individuals on the autism spectrum often experience pragmatic social conversation difficulties that include showing interest in their conversational partners. This may become particularly evident during adolescence when conversation with peers is the primary medium for social interaction. This study used a multiple baseline design across participants to investigate the effects of a brief intervention package on the partner-focused conversation of three adolescents with autism. Results showed increased partner-focused questions and comments for all participants. Social validity assessments indicated that the intervention led to meaningful improvements in peer conversations. [ABSTRACT FROM AUTHOR]

We intended to compare the doctors with a convolutional neural network (CNN) that we had trained using our own unique method for the Lateral Pillar Classification (LPC) of Legg–Calve–Perthes Disease (LCPD). Thousands of training data sets are frequently required for artificial intelligence (AI) applications in medicine. Since we did not have enough real patient radiographs to train a CNN, we devised a novel method to obtain them. We trained the CNN model with the data we created by modifying the normal hip radiographs. No real patient radiographs were ever used during the training phase. We tested the CNN model on 81 hips with LCPD. Firstly, we detected the interobserver reliability of the whole system and then the reliability of CNN alone. Second, the consensus list was used to compare the results of 11 doctors and the CNN model. Percentage agreement and interobserver analysis revealed that CNN had good reliability (ICC = 0.868). CNN has achieved a 76.54% classification performance and outperformed 9 out of 11 doctors. The CNN, which we trained with the aforementioned method, can now provide better results than doctors. In the future, as training data evolves and improves, we anticipate that AI will perform significantly better than physicians. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to evaluate the efficacy of percutaneous treatment in hydatid cysts (HCs) with at least one diameter larger than 10 cm. Materials and methods: 58 CE1 or CE3a HCs with at least one diameter larger than 10 cm which were treated with catheterization between September 2016 and December 2021 were retrospectively analyzed. Results: Mean age was 40 ± 17.7 (18–80). Majority of HCs were in the liver (89.6%). Median follow-up was 28 months. Technical success rate was 100%; however, a second procedure was needed in 13 cysts due to recollection (n = 4), infection (n = 6), and recurrence (n = 3). Conclusions: Giant HCs can be effectively treated with catheterization with low complication rates. [ABSTRACT FROM AUTHOR]

Underground water resources are one of the most valuable vital resources for mankind. Groundwater is used as drinking water and for agricultural irrigation. However, in recent years, it has been exposed to dangerous pollution, mainly due to man-made reasons. The study area is located in the Upper Mesopotamian region, where dry agriculture has been practiced since ancient times, which has semi-arid characteristics and where important civilizations lived. In this direction, the changes in groundwater quality were investigated with seasonal, annual samples taken from selected wells in Mardin Kiziltepe Plain in Upper Mesopotamia region and representing the plain in general and were subjected to water quality classifications. Statistical analyses were carried out on EC and NO3- parameters, which are important in determining the quality of groundwater. The results obtained were interpreted, evaluated in terms of drinking and agricultural uses, and it was observed that there were no non-standard values. In addition, the study area will be opened for irrigation in the near future within the framework of GAP, the largest integrated irrigation project in Turkey. This study, which is the first scientific research to be carried out before intensive irrigation, will be the first memory that will provide a very important data set for the region and will be recorded. In addition, the results of the study will be the basis for the comparison of the research to be carried out after the transition to irrigated agriculture depending on the GAP and the pre-irrigation data. [ABSTRACT FROM AUTHOR]

Turkey has rapidly become one of the top markets for onshore wind energy with a 10 GW onshore wind power installation, a significant accomplishment for the nation. It means that Turkey's wind energy capacity has more than tenfold increase in the last 10 years. Despite the fact that Turkey has no offshore wind farms, research has indicated a significant and untapped energy potential. This work provides a comprehensive techno-economic analysis of the Samandag Offshore Wind Farm (OWF) project in Turkey. To undertake an economic feasibility study under different Feed-in Tariffs (FiT) and discount rates, a discounted cash flow economic model is utilized. This study takes into consideration the economic indicators utilized in decision-making processes from the perspective of an OWF investor. The planned OWF project is shown to be economically viable only provided specific techno-economic prerequisites are satisfied. Samandag City has a lower levelized cost of energy (LCOE), which is roughly $69.97/MWh, according to the findings. However, the findings show that the net present value (NPV) is very sensitive to discount rates and FiT. The goal of this research is to contribute scientifically to the development of offshore wind energy in Turkey. [ABSTRACT FROM AUTHOR]

Economic development may have distinct implications for the environment based on gender-differentiated employment in various sectors of the economy. This is the first major attempt to incorporate gender in environmental modeling using sector-wise female employment status and gender parity index. In this study, we investigate the environment-gender nexus through the Environmental Kuznets Curve (EKC) hypothesis in 36 Asian countries for the period 1991–2017 by panel data estimations. We find in all estimations through panel FMOLS that (a) per capita GDP positively affects CO2 emissions and (b) per capita CO2 is affected by per capita GDP squared negatively, thus confirming EKC for panel Asian data with 971 observations. The estimations of the paper also confirm the EKC hypothesis through panel data models in which GDP and GDP squared variables. Our findings suggest male and female labor market participation impacts the environment differently, particularly in the agricultural sectors. We also find that an increased schooling ratio (gender parity index) negatively impacts environmental degradation. The findings of the present study are expected to give insights into policymakers for understanding the theoretical and statistical association between environmental quality and male/female labor force in agricultural and industrial sectors for 36 Asian countries. [ABSTRACT FROM AUTHOR]

One of the primary trends in the technological growth of offshore wind turbines is the movement toward larger-rotor machines with higher hub height. Recently, offshore wind turbine sizes, especially hub height, rotor diameter, and nameplate capacity, have increased dramatically and are expected to increase further. This growth in offshore wind turbine size enhances the turbine power coefficient for the same wind speeds significantly and also led to the reductions of levelized cost of electricity (LCOE) and total installed cost (TIC) of the turbines. This work examines the growth in the nameplate size and capacity of offshore wind turbines installed in Europe through the years as new installations and total installations. Effects and results of this growth in nameplate size and capacity on the LCOE, TIC, wind farm capacity, turbine-specific power capacity, turbine capacity factor (CF), sea surface area needed per GW, and the number of turbines per GW are examined and discussed in detail. As a result, this study aims to contribute to the literature and provide technical and innovative information to turbine manufacturers by presenting various aspects of offshore wind turbine technology development in Europe. The rapid technological developments in this sector show that the average CF has increased from 42 to 44% in the last decade, and the LCOE value has reduced from 0.156 USD/kWh to 0.096 USD/kWh. [ABSTRACT FROM AUTHOR]

Global warming is pressuring policymakers to change climate policies in shifting the global economy onto a net-zero pathway. While financial assets are responsive to policy changes and development, climate change policies are becoming increasingly unpredictable, making policy decision less certain. This study investigates connectedness and spillover effects of US climate policy uncertainty on energy stocks, alternative energy stocks, and carbon emissions futures. We analyzed spillover and connectedness before and after the Paris Agreement. We employed monthly frequency data from August 2005 to March 2021 and applied DY (2012) method and MGARCH approach. We found that world energy stocks and carbon emissions futures are connected to US climate policy uncertainty. Uncertainty in climate policy and world energy stocks act as information transmitters in return spillover, while global alternative energy and carbon market are shock receivers. On volatility spillover, climate policy uncertainty, energy stocks, and carbon emissions future are shocks transmitters, while alternative energy stocks are receivers. We observe increase in connectedness following the Paris Agreement suggesting strengthened global efforts in tackling climate change. DCC and ADCC estimations revealed spillover effects of climate policy on futures returns and volatilities of world energy stocks and carbon emissions futures and the shocks could be transmitted through to the energy sector. During period of uncertainty in US climate policy, carbon allowances can potentially serve as a safe haven for energy stocks and provide downside protection for alternative energy stocks, hence hedging against climate transition risks. [ABSTRACT FROM AUTHOR]

The use and importance of renewable energy sources (RES) have been increasing every passing year as fossil fuels will soon be depleted. Within this context, solar-photovoltaic (PV) is the most preferred energy type among RES. The PV has uncertain power output as its output depends on solar radiation, which is heavily influenced by environmental factors, so the prediction of solar radiation plays a crucial role in integrating these plants into the electricity grid. For the short-term 1-h-ahead solar radiation prediction, four time-series methods were implemented in this study: long short-term memory (LSTM) network, multilayer perceptron (MLP), and adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), and fuzzy c-means (FCM). Root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE) were used as statistical error criteria. The obtained results by the LSTM, MLP, ANFIS-FCM and ANFIS-GP models were assessed by comparing with the actual data. Considering the testing procedure, the best MAE values were found to be 53.37 W/m2, 58.45 W/m2, 61.68 W/m2, and 78.17 W/m2 for the LSTM, ANFIS-FCM, MLP, and ANFIS-GP, respectively. Results showed that the LSTM model in 1-h-ahead solar radiation prediction yielded the best results among all four models with high accuracy. [ABSTRACT FROM AUTHOR]

In this study, relationship between interface state and dislocation densities of Ag/TiO2/n-InP/Au Schottky barrier diodes with 60-Å and 120-Å TiO2 interface thickness is investigated. Samples are grown using the sputtering method and all investigations are made at room temperature. Interface state density (Nss) is gained from current–voltage (I-V) characteristics. Structural dislocation densities are gained from high-resolution X-ray data. Different dislocation densities of TiO2 interfaces and their contribution to interface state density are discussed. [ABSTRACT FROM AUTHOR]

In this study, an average waste composition was determined using the Provincial Environmental Status Reports of 81 provinces in Turkey for 2019, and the life cycle effects of these wastes were examined through seven waste management scenarios prepared in accordance with EU directives. First, the reports were examined and the waste compositions of the provinces were determined. Then, scenarios consisting of landfill, incineration, compost and recycle were applied using SimaPro software and short, medium- and long-term effects were calculated. The results show that the scenarios involving the incineration method have a high impact on global warming and consequently on human health, ecosystem quality and resources. The landfill method is observed to be more harmful in the long term. Although recycling puts a significant negative pressure on the cost of resources, it provides significant environmental benefits. In addition, recycling has a huge positive impact, especially in land and mineral resource use. Similarly, by including the compost method in the disposal process, significant gains are obtained, especially in the short and medium term. Consequently, the environmental benefits of recycling and compost processes have been clearly calculated and it is recommended to expand these two processes. [ABSTRACT FROM AUTHOR]

Objectives: Like other bones, the mandible and cervical vertebrae are affected by several systemic diseases. The aim of this study is to evaluate the effects of osteoporosis (OP), diabetes mellitus (DM), and dialysis-indicated advanced chronic kidney disease (CKD), which are the most effective systemic diseases on the bone metabolism, on the trabecular microstructure of the mandible and cervical vertebrae using cone beam computed tomography (CBCT). Methods: 81 patients who signed our informed consent form are involved in the study. 18 of them were diagnosed with osteoporosis, 18 of them with diabetes mellitus, 18 of the patients had dialysis-indicated CKD, and 27 of them were in the control group without any systemic diseases. Nine patients in the control group, patients with CKD and patients with DM were men, and nine were women. All patients with osteoporosis and 18 of the patients in the control group were women. Using CBCT images, microstructural parameters of trabecular thickness (Tb.Th), trabecular spacing (Tb.Sp) and volume fraction (bone volume/total volume, BV/TV) were measured. Results: Tb.Th and BV/TV values were higher in the control group, while Tb.Sp was higher in the osteoporosis group. The difference in BV/TV parameters was statistically significant (p = 0.02). In the DM group, Tb.Th and BV/TV values were lower and Tb.Sp values were significantly higher than in the control group (p = 0.001). In patients with advanced CKD, Tb.Th and BV/TV values were lower, while Tb.Sp values were higher than in the control group. Differences in Tb.Sp parameters were statistically significant (0.004). Conclusion: Systemic diseases affect bone tissue at different levels, and to evaluate these effects, cortical and trabecular bone parts must be investigated separately, and findings must be combined with patients' clinical symptoms. CBCT is suitable for microstructural evaluation of trabecular bone and the mandible carries valuable data for this purpose. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Residual gait deviations in children treated by medial open reduction for developmental dysplasia of the hip at long-term follow-up: a comparison with healthy controls" published in the journal International Orthopaedics. The correction states that the correct affiliation for Kübra Önerge should be affiliation 2, which is the Physiotherapy and Rehabilitation Department at İstanbul Kültür University in Istanbul, Turkey, and not affiliation 3. The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The authors of the article are Mehmet Demirel, Halenur Evrendilek, N. Ekin Akalan, Fuat Bilgili, Emre Meriç, Shavkat Kuchimov, and Kübra Önerge. [Extracted from the article]

Accurate forecasting of wind speed (WS) data plays a crucial role in planning and operating wind power generation. Nowadays, the importance of WS predictions overgrows with the increased integration of wind energy into the electricity market. This work proposes machine learning algorithms to forecast a one-hour ahead short-term WS. Forecasting models were developed based on past time-series wind speeds to estimate the future values. Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fuzzy c-means, ANFIS with Grid Partition, ANFIS with Subtractive Clustering and Long Short-Term Memory (LSTM) neural network were developed for this purpose. Three measurement stations in the Marmara and Mediterranean Regions of Turkey were selected as the study locations. According to the hourly WS prediction, the LSTM neural network based on the deep learning approach gave the best result in all stations and among all models applied. Mean Absolute Error values in the testing process were obtained to be 0.8638, 0.9603 and 0.5977 m/s, and Root Mean Square Error values were found to be 1.2193, 1.2573 and 0.7531 m/s from the LSTM neural network model for measuring stations MS1, MS2, and MS3, respectively. In addition, the analyzes revealed that the best correlation coefficient (R) results among the algorithms in the test processes were obtained to be 0.9498, 0.9147, and 0.8897 for the MS1, MS2, and MS3 measurement stations, respectively. In this regard, it is shown that the LSTM method gave high sensitive results and mainly provided greater performance than the ANFIS models for one hour-ahead WS estimations. [ABSTRACT FROM AUTHOR]

In this study, we examine the volatility of exchange rates to exogenous monetary shocks by dividing the volatility into subsections of "interdependence" and "contagion" during FED's easing for the period from 2010:11 to 2018:7. Conventional methods are neither suitable to differentiate those components nor to identify the co-movements concerning time and frequency analysis, which are critical for timing in asset management and policymaking. Therefore, wavelet analysis is introduced to differentiate such components. The contribution of the study is threefold: First, this study compensates for the lack of research capturing the volatility structure of exchange rates during exogenous shocks. Second, we show both the long-term and short-term impact with associated frequency ranges. Third, we show that identifying the impact of a shock in different components and frequency ranges can provide valuable insights for the timing of market preventions and asset allocation. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to determine the effect of fibular fixation on alignment and fracture healing of tibia, and ankle functional outcomes in the treatment of distal third tibiofibular diaphyseal fractures. Methods: Consecutive 111 patients (33 females and 78 males) with distal third tibiofibular diaphyseal fracture who met the inclusion criteria were included in the study. Patients were divided into two groups as those who underwent fibular fixation with tibia intramedullary nailing (study group) and those who did not (control group). Groups were compared in terms of demographic features, trauma and fracture characteristics, functional and radiological outcomes. Results: No significant difference was observed between the groups in terms of demographic features, trauma characteristics, complications, and follow-up time (p > 0.05). Surgery time was significantly lower in the control group (p = 0.001). Ankle joint range of motion, AOFAS score, OMAS score, and full weight-bearing time were significantly better in the fibular fixation group (p = 0.023, p = 0.001, p = 0.001 and p = 0.039, respectively). Significantly better coronal alignment and sagittal alignment were found in the fibular fixation group (p = 0.001 and p = 0.001, respectively). Patients who underwent fibular fixation had significantly better radiological outcomes in terms of fibular rotation angle and ankle arthrosis (p = 0.000 and p = 0.022, respectively). Conclusion: Fibular fixation not only contributes to fracture union, early full weight-bearing, and alignment but also improves ankle functional outcomes in the distal third tibiofibular fractures treated with intramedullary nailing for tibia. Level of evidence: Level III, retrospective study. [ABSTRACT FROM AUTHOR]

Because hurricanes, droughts, floods, and heat waves are all important factors in measuring environmental changes, they can all result from changes in atmospheric air temperature and relative humidity (RH). Besides, climate, weather, industry, human health, and plant growth are all affected by RH. Accurately and consistently forecasting RH is a challenge due to its non-linear nature. The present study tried to predict one day ahead of RH in determined provinces from different climatic regions of Turkey (Ankara, Erzurum, Samsun, Diyarbakır, Antalya, and Bilecik) using long short-term memory (LSTM) and adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM)–based machine learning models. As evaluation criteria, root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were employed. The outcomes from the forecasting models were also validated using observed data. During the testing stage, the smallest MAE and RMSE values were discovered to be 5.76% and 7.51%, respectively, in Erzurum province, with an R value of 0.892 when using the LSTM method. Moreover, the smallest MAE and RMSE values were obtained to be 5.95% and 7.67%, respectively, in Erzurum province with an R value of 0.887 using the ANFIS model according to the daily RH prediction. The results indicate that both the LSTM and ANFIS approaches performed satisfactory performance in daily RH prediction, with the LSTM and ANFIS approaches producing nearly identical results. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) contain two or more benzene rings and are categorized as general environmentally harmful pollutants. PAHs occur because of various combustion and pyrolysis processes with different environmental and anthropogenic sources. The Kizilirmak River is one of the major important water sources used for drinking water and irrigation purposes in Turkey. There are important industrial facilities around the river and PAH pollution is important in terms of environmental health. This study was carried out to determine the residues of PAHs in water and sediment samples according to month and to identify the pollution sources. Thus, water and sediment samples were collected from five different stations in the Kirikkale basin of the Kizilirmak River every 15 days for a year. In this way, 120 water and 120 sediment samples were collected over a year and analyzed in terms of 16 priority PAHs according to the Environmental Protection Agency. The monthly average of the water and sediment samples was calculated. Analyses were carried out with high-performance liquid chromatography based on solid phase extraction. GC–MS was used for confirmation. Acenaphthene, acenaphthylene, benzo[b]fluoranthene, benzo[a]pyrene, indeno[1,2,3-cd]pyrene, and dibenzo[a,h]anthracene were not found in the water samples. The total PAH levels in water and sediment were detected in the range of 0.04 to 1.545 μg/L and 43.15–386.115 μg/kg, respectively. PAHs found in water and sediment samples had pyrogenic and petrogenic origin and pollution changed significantly between autumn and winter. As a result, precautions should be taken in terms of preventing environmental pollution. [ABSTRACT FROM AUTHOR]

Crushed cartilage is used in rhinoplasties and crushing carry the risk of devitalization. The most infero-posterior part of the septal cartilage has a rough surface compared with the smooth surface of the remaining parts. This cartilage may be more convenient for crushing with lesser pressure requirements, increasing the viability. Twenty-six patients underwent septorhinoplasty and the infero-posterior part of the septal cartilage was harvested. The rough cartilage was utilized in nine patients (excluded from the study). Seventeen patients were included in the study. The mean age of the patients was 28 (19–37y). Two pairs of grafts were utilized for histological study. In fifteen patients crushing experiment was performed. The smooth-surfaced cartilages were the control group, the rough-surfaced cartilages were the study group. For each case, grafts were reduced to the same dimensions and placed over millimetric-paper and photographed. The grafts were crushed simultaneously. The crushed cartilages were also placed on millimetric-paper and photographed. The data were evaluated statistically. The mean surface increment ratio was 2.26 ± 0.28 for the control and 2.94 ± 0.25 for the study group. The difference was statistically significant (p < 0.0048). In two specimen, after applying hematoxylin & eosin stain, the extracellular matrix of the rough cartilage specimen stained darker due to more abundant proteoglycan content. The rough septal cartilage widens more compared with the smooth cartilage under the same crushing pressure. Thus, the rough cartilage requires lesser pressure for widening which may increase the viability. The rough septal cartilage contains more proteoglycan which may explain its softness. [ABSTRACT FROM AUTHOR]

Atmospheric pressure (AP), which is an indicator of weather events, plays an important role in climatology, agriculture, meteorology, atmospheric and environmental science, human and animal life, and Earth's living ecosystem. In this regard, accurate AP forecasting plays a crucial role in today's life as it provides critical information about future weather events. In this study, four different machine learning techniques such as long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means, ANFIS with subtractive clustering, and ANFIS with grid partition (GP) were used for one-hour-ahead AP forecasting. To achieve this, the hourly AP data measured between 2012 and 2019 at the seven measurement stations (Adana, Ankara, Gumushane, Denizli, Kirklareli, Sanliurfa, and Van) in different climate regions of Turkey were obtained. The estimation accuracy was verified by four performance criteria: R, RMSE, MAPE, and MAE. As a result, the highest relative R-value of 0.9986 and the lowest error values of RMSE = 0.2905 hPa, MAPE = 0.0230%, and MAE = 0.2040 hPa for one-hour-ahead AP forecasting were obtained from the ANFIS-GP model. [ABSTRACT FROM AUTHOR]

Due to the commissioning of floating wind units, the latest technological developments, significant growth, and improvements in turbines, developments in offshore wind power capacity are estimated to increase faster than in the last two decades. The total installed offshore wind power capacity, which is currently 35 GW, is predicted to be approximately 382 GW by 2030 and approximately 2002 GW by 2050. For this reason, attempts are proposed to lower levelized cost of electricity (LCOE) for offshore wind power generation more than for other energy sources. In this study firstly, the global growth in the nominal capacity and size of offshore wind turbines over the last twenty years is examined. Then, the effects of this increase in nominal capacity and size on the LCOE, total installation cost (TIC), and turbine capacity factor are investigated. In parallel with this development, the changes in distance to shore and water depth for installation offshore wind power plants are reviewed according to the years. In addition, the effects of this global growth on wind farm capacity, turbine-specific power capacity, number of turbines per GW, and area needed per GW are investigated and discussed in detail. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study is to evaluate the adhesion of Streptococcus mutans on the surface of CAD/CAM materials with various surface treatments. Methods: Vita Enamic, Lava Ultimate, and Cerasmart materials were used in this study. A total of 90 samples were prepared (n = 10). After various finishing and polishing procedures (non-polished, manual polished, and glazed), surface roughness (SR) measurements, surface free energy (SFE), and elemental and topographic analysis with FIB-SEM/EDX were used to evaluate the samples' surface properties. To improve bacterial adhesion, CAD/CAM materials were covered with sterile artificial saliva containing mucin for pellicle formation and incubated for 1 h at 37 °C. Bacteria were then inoculated into the pellicle-coated specimens, and incubation was performed at 37 °C for 24 h. Bacterial adhesion was determined as × 105 CFU/mL and monitored using FIB-SEM analysis. The Kolmogorov–Smirnov test was used to statistically analyze the normality of the distribution; the groups were then compared using one-way ANOVA and Tukey's test. Results: The SR of the control group was statistically higher in all materials (p < 0.05). There were no statistically significant differences in SR between all materials in the non-polished and manual polished groups (p > 0.05). The Vita Enamic control group exhibited the highest SFE value. The highest S. mutans adhesion was observed in non-polished (p < 0.05). Vita Enamic samples had higher CFU than other groups. Conclusions: Non-polished surfaces showed higher SR and bacterial adhesion. Polishing processes affected the surface properties and bacterial adhesion. Clinical relevance: Care must be taken in polishing restorations to minimize the risk of bacterial adhesion and recurrent caries. Trial registration: In this study, the materials used for dental treatments are in vitro evaluated. Due to that, this study is not registered to clinical trials. [ABSTRACT FROM AUTHOR]

This work presents machine learning techniques to estimate the aerodynamic coefficients of a 40° swept delta wing under the ground effect. For this purpose, three different approaches including feed-forward neural network (FNN), Elman neural network (ENN) and adaptive neuro-fuzzy interference system (ANFIS) have been used. The optimal configuration of these models was compared with each other, and the best accurate prediction model was determined. In the generated machine learning models, the lift CL and drag coefficients CD of the delta wing under the ground proximity of h/c = 0.4 were predicted by using the data of actual CL and CD of the delta wing under the ground proximities of h/c = 1, 0.7, 0.55, 0.25 and 0.1. In FNN, ENN and ANFIS models, the angle of attack α and ground distance h/c were utilized as input parameters, CL and CD as output parameters, separately. Although all three models estimate the CL and CD of the delta wing under h/c = 0.4 with very high accuracy, the ENN method predicts the CL and CD with much higher accuracy than the FNN and ANFIS models. For the estimation of CL, while optimal configuration of ENN resulted in 1.0709% MAPE, 0.00595 RMSE and 0.00504 MAE, the best configurations of FNN and ANFIS end up with the results of 1.172% and 1.1028% MAPE, 0.00786 and 0.0071 RMSE, 0.00593 and 0.0054 MAE, respectively. Thus, results show that the developed FNN, ENN and ANFIS models can be accurately employed to forecast the aerodynamic coefficients of the delta wing under ground effect without the need of for many experimental measurements that causes extra time, labor and experimental costs. [ABSTRACT FROM AUTHOR]

Since the early twentieth century, disinfecting water has been an essential process to prevent the introduction of harmful organisms, especially pathogenic organisms. Due to the reaction between anthropogenic contaminants present in water and the chemicals used to disinfect water, compounds known as disinfection by-products (DBPs) are formed during the water disinfection process. Chlorination, the most dominant water disinfection method, produces DBPs that have drawn a lot of attention and health concerns. The most commonly used removal technology for trichloromethane (TCM) is adsorption, and the use of activated carbon, iron oxides, and nanoparticles has been widely investigated. Studies have found that using nano-zero valent iron with nano-catalytic metals (Cu, Ni etc.) to synthesize bimetallic nanoparticles increases the removal of organic pollutants. The current study investigates the adsorption of trichloromethane (TCM) by synthesized Fe/Cu bimetallic nanoparticles. The response surface methodology (RSM) was used to investigate the effect of independent variables on the removal of TCM. According to the CCD results, TCM concentration and reaction time were determined as the most effective parameters. The lowest TCM concentrations have low removal efficiencies, while the lowest TCM concentration (50 µg/L) can be removed up to 60%. The highest TCM concentration can be 500 µg/L to achieve a removal below the limits with 500 mg/L Fe/Cu concentration and 24 min of reaction time. Life cycle assessment (LCA) was applied to Fe/Cu nanoparticle synthesis, and results indicated that the highest environmental impact was from the mixture of reactant stage. [ABSTRACT FROM AUTHOR]