The University of Birjand (Persian: دانشگاه بیرجند) is the largest and oldest public university in the east of Iran. In 2018, the university was ranked 300–350 in Asia University Ranking and +1000 in World University Ranking by Times Higher Education. The University of Birjand also achieved a top rank in Times Higher Education Young University Rankings, 2019. It is also one of the 33 Iranian universities listed in the 2020 Times Higher Education World University Rankings for engineering and technology. The university has a multilingual website (Persian, English, Pashtu, Spanish, Arabic, French, and Turkish).
Fraudulent activities within banking transactions pose a significant challenge for the banking sector, occurring either individually or as part of an organized scheme. It is always difficult to identify such illegal activities. Despite the development of various models and algorithms to tackle this issue, the intricate and diverse nature of fraud patterns presents difficulties in detecting all suspicious transactions. Researchers have suggested using graph theory to consider the interactions between transactions in order to overcome this challenge. Another challenge is the increased false positive error when investigating individual transactions that exhibit behavior similar to high-risk behavior. To improve the understanding of the transaction process, the use of sequence-based approaches has been proposed. In this article, a model that combines graph and sequence theory was developed to detect organized fraud. The first phase of the model involved extracting network features from the transaction graph and applying a hidden Markov chain to capture the sequential nature of the transactions. In the second phase, fraud detection was performed using a combination of the support vector machine and the improved honey badger metaheuristic algorithm. This algorithm aims to enhance fraud detection efficiency by adjusting the parameters of the support vector machine. The proposed model was evaluated on three datasets—two real-world datasets and one benchmark dataset. Performance was assessed using precision, accuracy, recall, F1-score, ROC-AUC, and PR-AUC metrics. On average, the method achieved an F1-score of 92
This study investigates the effectiveness of ensemble combining techniques in improving the accuracy of satellite-based gridded precipitation estimations in the Sirjan watershed, Iran. For this purpose, four satellite-based products (CHIRPS, MSWEP, PERSIANN-CDR, and PERSIANN-CCS-CDR) were analyzed over 25 years (1996–2020). Various ensemble combining techniques, including Simple Model Averaging (SMA), Weighted Averaging Model (WAM), Multi-Model Super Ensemble (MMSE), and Modified MMSE (M3SE), were implemented to generate new estimations of precipitation based on satellite-based products. The performance of the ensemble combining techniques was evaluated through a series of comparative tests. The findings indicate that the MMSE and M3SE significantly improve the accuracy of precipitation estimates compared to individual products, increasing the accuracy rate up to 48
A one-step ultrasonic spray pyrolysis technique was used to fabricate a nano-crumpled nitrogen and sulfur co-doped ZnO-CuO. Zinc acetate, copper acetate, and thiourea with various molar ratios were dissolved in deionized water and utilized as the starting precursor solution. The deposited nanocomposites were characterized by using FESEM, XRD, EDX, UV–vis spectroscopy, PL, and EIS. The photocatalytic performance of the synthesized nanocomposites was evaluated through the photodegradation of methylene blue. Nearly total (98.4
Accurate runoff prediction plays a crucial role in water resource management, flood control, and hydropower generation. This study proposes a novel hybrid regression approach for runoff prediction by integrating clustering techniques as a preprocessing phase with regression algorithms. Initially, the dataset is divided into distinct clusters to capture underlying patterns in the runoff data. Subsequently, a regressor model is trained on each cluster to enhance predictive performance. The proposed methodology is evaluated using real-world hydrological datasets, and its effectiveness is compared against baseline models. Experimental results demonstrate that clustering-based modeling improves prediction quality, as indicated by key performance metrics such as RMSE and R2. The findings suggest that the hybrid regressor method can significantly enhance the reliability of runoff predictions, offering valuable insights for hydrological forecasting and water management applications.
Metallography is one of the most effective techniques for microstructural characterization of metals and alloys. The present study examined the microstructure, morphology, and distribution of the principal precipitated phases in Inconel 792 superalloy. To achieve precise microstructural analysis, several etching solutions were applied: acetic acid (CH3COOH), nitric acid (HNO3), hydrochloric acid (HCl), Marble’s reagent, and sulfuric acid (H2SO4, employed as a surface activator. Optical microscopy images showed that the as-cast Inconel 792 exhibits a dendritic grain structure with no preferred orientation. Carbides, γ′ precipitates, and γ/γ′ eutectic regions were clearly visible in these micrographs. Phase identification was corroborated by complementary techniques, including X-ray diffraction (XRD), uniaxial tensile testing, and scanning electron microscopy (SEM). Etchants based on acetic acid (CH3COOH), nitric acid (HNO3), and hydrochloric acid (HCl) effectively revealed the precipitated phases. Marble’s reagent proved particularly suitable for delineating the dendritic structure.
