Artvin Çoruh University

Escalating global production and consumption are driving rapid growth in energy demand, increasing pressure on finite natural resources. In response, this study proposes a data-driven framework that integrates deep learning-based electricity demand forecasting with economy-wide input-output material footprint analysis to support long-term energy planning and policymaking. The innovative aspect of this framework is its ability to jointly assess future electricity generation and related material requirements within a single analytical structure. A comparative analysis is conducted for T & uuml;rkiye, Germany, and Spain, evaluating the material footprint of electricity generation across renewable and fossil-based energy sources under business-as-usual (BAU) and alternative energy development scenarios. The forecasting models demonstrate strong predictive performance, achieving Mean Absolute Percentage Error (MAPE) values of 1.39% for T & uuml;rkiye, 4.39% for Germany, and 3.90% for Spain, significantly outperforming conventional statistical methods. Scenario-based results indicate that sustainability-oriented pathways (ST and GCA) can reduce material requirements by approximately 20-30% compared to the BAU scenario, particularly for metal-intensive inputs such as iron and refined oil. The findings underscore the importance of integrating material footprint considerations into energy transition strategies and provide practical insights for policymakers seeking to balance energy security with resource sustainability. The study highlights the value of integrated analytical approaches in supporting more resilient and resource-efficient energy systems.

Utilizing data from the PISA 2022 Turkey sample, this study investigates the mechanisms linking mathematics self-efficacy to mathematics performance by examining the mediating roles of eight socio-emotional and cognitive factors: perseverance, curiosity, cooperation, empathy, assertiveness, stress resistance, emotional control, and self-efficacy in digital competencies. Theoretically, this study advances social cognitive research by demonstrating that the translation of academic self-beliefs into achievement is not a uniform process, but is rather differentially channelled-and sometimes hindered-by specific non-cognitive traits. Data were analysed using parallel multiple mediation and path analysis, incorporating plausible values to ensure robust parameter estimation. Results confirmed a significant positive direct effect of mathematics self-efficacy on performance, indicating partial mediation. The indirect pathways revealed complex dynamics: curiosity, empathy, assertiveness, and stress resistance emerged as significant positive mediators, whereas perseverance and cooperation exhibited significant negative indirect effects. Emotional control and self-efficacy in digital competencies showed no significant mediation. These divergent mediation effects point to highly specific policy implications. Because high self-efficacy enhances performance through traits like curiosity and stress regulation, but is suppressed by standard cooperative and perseverance-based pathways, educational interventions must move beyond generic confidence-building. Policymakers and educators should explicitly leverage the positive mediators through inquiry-driven instruction and stress management protocols, while critically redesigning how collaborative learning and repetitive persistence tasks are structured for high-efficacy students.

Coastal areas have historically been important and attractive areas for human settlements. The most important function of these areas is their proximity to water. Therefore, as the world population has increased, anthropogenic pressure on coastal areas has become an issue with high priority to be researched. This situation has necessitated the management of coasts with sustainable approaches. Türkiye, with its geographical location and climatic characteristics, has significant natural landscapes with tourism potential in the mediterranean basin. As stressed in European Landscape Convention in order to protect natural landscapes, identification is a first step to building necessary data inventories that help in planning stage. Therefore, determining landscape value of natural landscapes may help to build such inventories. In this context, this study was conducted to investigate the landscape value of beaches located on the Black Sea coast using expert-based evaluation technique. To this end, coastal sand formations that have received the Blue Flag label and are of a certain size in morphological terms were examined through field surveys. Accordingly, it was determined that the landscape value calculated for each beach is related to the composition and design of the vegetation cover, species diversity and density, and other services offered at the beaches. The highest Landscape Value (Lv) scores were calculated for Palm Beach, Cebeci, Kumcağız and Miliç beaches. It should also be stressed that Blue Flag beaches have great importance in terms of Southern Black Sea coastal management strategies.

The early childhood period is a critical stage in which the foundations of cognitive, social, and emotional development are established. Early numeracy skills acquired during this period are considered significant predictors of later academic success. Assessing children’s early numerical competencies is essential for both educators and researchers. However, the lack of reliable and valid teacher-report assessment tools makes the evaluation process challenging. The Teacher Rating Scale - Early Numeracy (TRS-EN) is a measurement tool based on teacher reports designed to evaluate children’s early numeracy skills. This study aims to translate and adapt the TRS-EN, originally developed in Finland and based on the English source version, into Turkish, and to provide evidence of its structural validity and reliability among Turkish preschoolers. The sample consisted of 331 children (48- to 72-month-olds). The results indicated that the structural validity of the TRS-EN was supported by a three-factor model: counting skills, numerical relational skills, and basic arithmetic skills. In addition, internal consistency for each factor was above α = 0.70 and the three-factor model was invariant for younger (48-60-month-old) and older (61-72-month-old) preschoolers. For test-retest reliability, there was a strong (r > .70, p < .001) correlation between TRS-EN scores over a one-month interval. The findings demonstrate that the TRS-EN is suitable for evaluating early numeracy skills among 48- to 72-month-old preschoolers and could therefore be recommended as a reliable tool to assess such skills.

This study investigated the effects of acidic (H3PO4, HNO3), alkaline (NaOH), thermal pretreatments under reflux condenserat, and hybrid thermochemical pretreatments on the biogas production efficiency of olive mill wastewater membrane filter sludge (OMW-MFS) generated from a portable membrane treatment system operated in a three-phase process. Thermal pretreatments were applied at 100 C-0 for 1h, 2h, and 3 h, while chemical pretreatments involved the addition of acidic and alkaline reagents at 10% (w/w) of the total solids content in the sludge. Anaerobic fermentation was conducted on the pretreated samples, revealing that the highest volumetric biogas and methane yields were achieved exclusively with nitric acid (HNO3) pretreatment. Over a 30-day period, biogas and methane production reached 217.46 mL and 173.85 mL, respectively. The biogas and methane production efficiencies were calculated as 1.023 mL g(-)& sup1; TS d(-1) and 0.818 mL g(-)& sup1; TS d(-1), respectively. These findings indicate that HNO3 pretreatment is an effective strategy to enhance biogas production from OMW-MFS and may guide future research.