Aksaray Üniversitesi Kurumsal Akademik Arşivi
DSpace@Aksaray, Aksaray Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Attitudes and preferences of cat and dog owners towards pet food quality attributes in Türkiye
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Erzurum, Onur; Kayar, Tamer
In recent years, pet owners have been making significant efforts to ensure the well-being of their animals. One straightforward approach to enhance their welfare involves greater attention to nutrition. Consequently, pet owners have become increasingly sensitive regarding the food they provide for their pets. The aim of this study is to examine this sensitivity by investigating the extent to which cat and dog owners prioritize quality attributes and ingredients when selecting pet food. For this purpose, a survey was conducted with pet owners (n = 519) who visited veterinary clinics, using a random sampling method. Of the participants, 51.25% were male and 48.75% were female, with the highest participation observed in the 18–34 age group (60.50%). Among the respondents, 64.93% owned cats, while 35.07% owned dogs. Additionally, 66.67% of pet owners reported using both wet and dry food. When selecting pet food, 42.58% of participants stated that they relied on recommendations from veterinarians. “Pet preference (palatability)” clearly emerged as the most important selection criterion, receiving the highest average score (3.90) and highlighting its decisive influence on purchasing decisions as owners appeared to prioritize what their pets liked most. In contrast, “visual appeal of the food” received the lowest score (2.47). A significant difference (p < 0.001) was found regarding whose recommendation influenced the purchase decision. Moreover, a negative correlation was identified between price sensitivity and other selection criteria, suggesting that price was not a primary driver in most participants’ decisions and became less important as quality-related factors gained priority. Overall, pet preference (palatability) stood out as the dominant driver of purchasing decisions.
Deep signals: enhancing bottom temperature predictions in Norway’s Mjøsa Lake Through VMD- and EMD-Boosted machine learning models
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Oruç, Sertaç; Hınıs, Mehmet Ali; Selek, Zeliha; Tuğrul, Türker
In this study, we benchmark various machine learning techniques against a synthetic but physically based reference time series (model-simulated (ERA5-Land/FLake) bottom-temperature series) and assess whether decomposition methods (VMD and EMD) improve forecast accuracy using Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), and Long Short-Term Memory (LSTM) with the monthly average data of Mjøsa, the largest lake in Norway, between 1950 and 2024 from the ERA5-Land FLake model. A total of 70% of the dataset was used for training and 30% was reserved for testing. To assess the performance several metrics, correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), Performance Index (PI), RMSE-based RSR, and Root Mean Square Error (RMSE) were used. The results revealed that without decomposition, the GPR-M03 combination outperforms other models (with scores r = 0.9662, NSE = 0.9186, KGE = 0.8786, PI = 0.0231, RSR = 0.2848, and RMSE = 0.2000). Considering decomposition cases, when VMD is applied, the SVM-VMD-M03 combination achieved better results compared to other models (with scores r = 0.9859, NSE = 0.9717, KGE = 0.9755, PI = 0.0135, RSR = 0.1679, and RMSE = 0.1179). Conversely, with decomposition cases, when EMD applied, LSTM-EMD-M03 is explored as the more effective combination than others (with scores r = 0.9562, NSE = 0.9008, KGE = 0.9315, PI = 0.0256, RSR = 0.2978, and RMSE = 0.3143). The results demonstrate that GPR and SVM, coupled with VMD, yield high correlation (e.g., r ≈ 0.986) and low RMSE (~0.12), indicating the ability to reproduce FLake dynamics rather than as accurate predictions of measured bottom temperature.
Phylogeography of scarturus williamsi and climate change impacts: genetic diversity and projected habitat loss in anatolia
(2025) Helvacı, Zeycan; Çolak, Ercüment
Scarturus williamsi (Williams’ jerboa) is a medium-sized, semi-fossorial rodent endemic to steppe ecosystems across Anatolia, Iran, and Azerbaijan, with specialized habitat requirements in semi-arid continental environments. This study integrates a mitochondrial DNA analysis with species distribution modeling to assess the species’ evolutionary structure and vulnerability to future climate change. The phylogeographic analysis and species distribution modeling reveal the evolutionary history and climate vulnerability of Scarturus williamsi across Anatolia and adjacent regions. The mitochondrial DNA analysis of 98 individuals demonstrates exceptional haplotype diversity (Hd = 0.9896), with 90 unique haplotypes and complete regional isolation, indicating pronounced population structuring across five evolutionary lineages: Central Anatolia, Eastern Anatolia, Aegean, Black Sea, and Azerbaijan–Iran. The Iran–Azerbaijan lineage exhibits the deepest evolutionary divergence, while Eastern Anatolia functions as the primary Anatolian refugium and Central Anatolia as the secondary refugial center. The strong isolation by distance (r = 0.735, p < 0.001) across ~2500 km explains 54.0% of the genetic variation, with the hierarchical structure reflecting greater Iran–Turkey isolation than intra-Turkish differentiation. The species distribution modeling identifies the Mean Temperature of Driest Quarter (bio9) and the Mean Diurnal Range (bio2) as primary habitat determinants, with bimodal preferences reflecting highland versus steppe adaptations. Climate projections reveal severe vulnerability with habitat losses of 63.69–98.41% by 2081–2100 across emission scenarios. SSP3-7.0 represents the most catastrophic scenario, with a severe habitat reduction (98.41% loss), while even optimistic scenarios (SSP1-2.6) project a 60–70% habitat loss. All scenarios show accelerating degradation through mid-century, with the steepest losses occurring between 2041 and 2080. Projected eastward shifts face constraints from the Anatolian Diagonal, limiting the climate tracking capacity. Despite occupying open landscapes, S. williamsi exhibits exceptional sensitivity to climate change, with Anatolian refugial areas representing critical diversity centers facing substantial degradation. Results provide baseline genetic structure and climate vulnerability information for understanding climate impacts on S. williamsi and Irano–Anatolian steppe fauna.
Overcoming career plateau: the role of aı in shaping women’s career paths in finance
(Science Publishing Corporation Inc., 2025) Karademir, Metin
This study explores the relationship between artificial intelligence (AI) and career plateaus among women working in corporate finance roles in Istanbul. Semi-structured interviews were conducted with seven professionals occupying junior, mid-level, and managerial positions. The interviews focused on everyday applications of AI, including performance dashboards, promotion lists, learning recommendations, and financial forecasting and reporting. Perceptions of both opportunities and challenges were examined, together with their influence on feelings of career stagnation or progress. A reflexive thematic analysis was applied. The findings suggest that AI helps make employee contributions more visible and supports performance discussions with shared evidence. Personalized learning recommendations were described as valuable, particularly for early-career employees seeking direction. However, several difficulties emerged. Some aspects of work, such as coordination, mentoring, and handling crises, were often overlooked by AI metrics. The constant presence of scoring mechanisms was reported to create pressure and anxiety. In addition, decision-making processes sometimes slowed down when AI outputs were mediated by unclear committee structures. More positive results were observed when AI was used in an advisory role, supported by transparent human review and regular bias checks. Career planning was more effective when personal circumstances such as mobility, language, and caregiving responsibilities were recognized. Although based on a small and non-random sample, the study offers evidence from Istanbul and highlights pathways for broader future research.
Sediment-based microbial fuel cell sensor for detecting boron mine effluent pollution in aquatic habitats
(Elsevier Inc., 2025) Türker, Onur Can; Yakar, Anıl
The effluent from boron (B) mines poses a significant threat to aquatic ecosystems, necessitating ongoing monitoring of B pollution. Conventional monitoring techniques necessitate expensive chemicals, equipment, and intricate procedures. This experiment involved the design of a sediment microbial fuel cell (SMFC) as a cost-effective, user-friendly, and self-sustaining early warning sensor for detecting B mine effluent pollution in aquatic ecosystems. The main operational principle of the SMFC-based sensor relies on the attenuation of the electrical signal caused by the suppression of bacterial metabolic activity due to B mine effluent. This sensor is integrated with an innovative power management system that signals B mine pollution via LED illumination. Statistical analysis indicated that the SMFC sensor effectively detected B mine effluent pollution at concentrations below 250 mg/L. The maximum bioremediation rate for B was 31 %, meaning that it was retained on the anode surface of the sensors and influenced bacterial metabolism, thereby producing voltage signals for the SMFC sensor. The abundance of microbes varied with concentrated B mine effluent, with Acinetobacter being the predominant bacterial genus in the sensor matrix. This research utilizes B pollution detection and introduces a novel methodology for the advancement of environmental sensors.
