Tarsus Üniversitesi Kurumsal Akademik Arşivi
DSpace@Tarsus, Tarsus 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
Functionality modulation of starch from lotus rhizome using single and dual physical modification
(Elsevier, 2024) Dhull, Sanju Bala; Antika, Chandak; Gökşen, Gülden; Chawla, Prince; Al Obaid, Sami; Ansari, Mohammad Javed
The effects of ultrasonication (US) assisted by pre- and post-treatment of heat-moisture treatment (HMT) on physicochemical, rheological, pasting, digestive, and thermal properties of lotus rhizome (LR) starch were investigated in this study. All treatments decreased the swelling power, amylose content, and peak viscosity except for the ultrasonicated sample when compared with native LR starch. All treatments showed similar diffraction patterns with different intensities. FTIR spectra characteristic peaks did not emerge or disappear after single and dual modifications. Storage modulus (G′) is greater than loss modulus (G″) for all LR starch gel samples demonstrating their elastic character. Moreover, ΔHgel (253.1–303.7 J/g) increased in all treatments. Dual modification (HMT & US) significantly enhanced resistant starch and reduced SDS in LR starches. These results could be beneficial for promoting ultrasound processing for potential uses in the food industry and starch production.
Evaluating the simultaneous electrochemical determination of antineoplastic drugs using LaNiO3/g-C3N4@RGH nanocomposite material
(Elsevier, 2024) Bouali, Wiem; Erk, Nevin; Sert, Buse; Harputlu, Ersan
A novel electrochemical sensor based on LaNiO3/g-C3N4@RGH nanocomposite material was developed to simultaneously determine Ribociclib (RIBO) and Alpelisib (ALPE). Ribociclib and Alpelisib are vital anticancer medications used in the treatment of advanced breast cancer. The sensor exhibited excellent electrocatalytic activity towards the oxidation of RIBO and ALPE, enabling their simultaneous detection. The fabricated sensor was characterized using various techniques, including energy dispersive X-ray (EDX), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XR), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS), which confirmed the successful synthesis of the LaNiO3/g-C3N4@RGH composite material. Electrochemical characterization revealed enhanced conductivity and lower resistance of the modified electrode compared to the bare electrode. The developed sensor exhibited high repeatability, reproducibility, stability, and selectivity toward RIBO detection. Furthermore, the sensor displayed high sensitivity with low detection limits of 0.88 nM for RIBO and 6.1 nM for ALPE, and linear ranges of 0.05–6.2 μM and 0.5–6.5 μM, respectively. The proposed electrochemical sensor offers a promising approach for simultaneously determining RIBO and ALPE in pharmaceutical formulations and biological samples with recovery data of 98.7–102.0 %, providing a valuable tool for anticancer drug analysis and clinical research.
Energy poverty and health in Turkey: Evidence from Longitudinal data
(Elsevier, 2024) İpek, Egemen; İpek, Özlem
This study critically examines the effect of energy poverty on health in Turkey between 2018 and 2021 using the Income and Living Conditions Survey Longitudinal Micro Data Set. It considers the multidimensional structure of household energy poverty and its individual effects on health, resulting in several significant findings. Firstly, the multidimensional energy poverty index at the household level is obtained as a continuous variable using principal component analysis, considering subjective and objective indicators of energy poverty. Secondly, the impact of energy poverty on health at the individual level and the effect of several socioeconomic variables, including unobserved heterogeneity, are estimated with the random effects ordered logit model. Finally, commonly used measures of energy poverty in the literature and their health impacts are compared. The robustness analysis results show that the model's goodness of fit is highest when the multidimensional energy poverty index is constructed using principal component analysis. In addition, the analysis results show that unobserved heterogeneity across individuals significantly impacts health. These results indicate that decentralized policies should be implemented to increase policy effectiveness in combating energy poverty.
Dualities over the cross product of the cyclic groups of order 2
(American Institute of Mathematical Sciences, 2024) Dougherty, S.T.; Şahinkaya, Serap
We determine the number of symmetric dualities on the s-fold cross product of the cyclic group of order 2, which is the additive group of the finite field F2s. We show that the ratio of symmetric dualities over all dualities goes to 0 as s goes to infinity.We also prove a surprising result that given any two binary codes C and D of the same length n with |C||D| = 2n, then viewing them as groups there is a symmetric duality M with CM = D, which also relates their weight enumerators as additive codes in a group via the MacWilliams relations. Using this theorem we show that any additive code in this setting can be viewed as an additive complementary dual code of length 1 with respect to some duality.
Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration
(Elsevier, 2024) Bakırcı, Murat
This study highlights the evolving landscape of object detection methodologies, emphasizing the superiority of deep learning-based approaches over traditional methods. Particularly in intelligent transportation systems-related applications requiring robust image processing techniques, such as vehicle identification, localization, tracking, and counting within traffic scenarios, deep learning has gained substantial traction. The YOLO algorithm, in its various iterations, has emerged as a popular choice for such tasks, with YOLOv5 garnering significant attention. However, a more recent iteration, YOLOv8, was introduced in early 2023, ushering in a new phase of exploration and potential innovation in the field of object detection. Consequently, due to its recent emergence, the number of studies on YOLOv8 is extremely limited, and an application in the field of Intelligent Transportation Systems (ITS) has not yet found its place in the existing literature. In light of this gap, this study makes a noteworthy contribution by delving into vehicle detection using the YOLOv8 algorithm. Specifically, the focus is on targeting aerial images acquired through a modified autonomous UAV, representing a unique avenue for the application of this cutting-edge algorithm in a practical context. The dataset employed for training and testing the algorithm was curated from a diverse collection of traffic images captured during UAV missions. In a strategic effort to enhance the variability of vehicle images, the study systematically manipulated flight patterns, altitudes, orientations, and camera angles through a custom-designed and programmed drone. This deliberate approach aimed to bolster the algorithm's adaptability across a wide spectrum of scenarios, ultimately enhancing its generalization capabilities. To evaluate the performance of the algorithm, a comprehensive comparative analysis was conducted, focusing on the YOLOv8n and YOLOv8x submodels within the YOLOv8 series. These submodels were subjected to rigorous testing across diverse lighting and environmental conditions using the dataset. Through tests, it was observed that YOLOv8n achieved an average precision of 0.83 and a recall of 0.79, whereas YOLOv8x attained an average precision of 0.96 and a recall of 0.89. Furthermore, YOLOv8x also outperformed YOLOv8n in terms of F1 score and mAP, achieving values of 0.87 and 0.83 respectively, compared to YOLOv8n's 0.81 and 0.79. These outcomes of the evaluation illuminated the relative strengths and weaknesses of YOLOv8n and YOLOv8x, leading to the conclusion that YOLOv8n is well-suited for real-time ITS applications, while YOLOv8x exhibits superior detection capabilities.
