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The accurate identification and classification of rice species is critical for increasing crop productivity, quality, and diversity. Traditional rice classification methods involving manual inspection can be time-consuming, costly, and error-prone. This study addresses to address this challenge by exploring the potential of machine learning (ML) models for automated and accurate rice classification. The key objectives of this paper are threefold. First, the study evaluates the discriminative power of various morphological features extracted from rice grain images using feature selection methods. Second, it compares the performance of several ML models, including Artificial Neural Network (ANN), Categorical Boosting (CatBoost), Gradient Boosting (GBoost), k-Nearest Neighbours (k-NN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), in classifying two rice species (Cammeo and Osmancik). Third, the study implements explainable artificial intelligence (XAI) techniques, namely SHapley Additive exPlanation (SHAP) and Individual Conditional Expectation (ICE) plots, to provide transparency and interpretability into the inner workings and decision-making processes of the ML models. The findings indicate that the LR model achieved the highest classification accuracy, with a rate of 93.1%. Feature analysis identified Major Axis Length, Perimeter, Convex Area, and Area as the most influential features in distinguishing between rice species. This study highlights the successful application of advanced ML techniques in automating industrial rice classification, facilitating automated packaging and quality control processes without the need for human intervention. By improving the efficiency of rice classification and reducing reliance on manual labour, this approach offers significant benefits to both the agricultural industry and food production sectors.

Determining P and S wave arrival times while minimizing noise is a major problem in seismic signal analysis. Precise determination of earthquake onset arrival timing, determination of earthquake magnitude, and calculation of other parameters that can be used to make more accurate seismic maps are possible with the detection of these waves. Experts try to determine these waves by manual analysis. But this process is time-consuming and painful. In this study, a new method that enables the determination of P and S wave arrival times in noisy recordings is recommended. This method is based on the hybrid usage of empirical mode decomposition and Teager–Kaiser energy operator algorithms. The results show that the proposed system gives effective results in the automatic detection of P and S wave arrival times. Promisingly, the recommended system might serve as a novel and powerful candidate for the effective detection of P and S wave arrival time.

We aimed to determine the predictors of technical success and patency after percutaneous transluminal angioplasty (PTA) of de novo dysfunctional hemodialysis arteriovenous fistulas (AVF). We performed a retrospective analysis of first time PTA in 228 patients (129 men, 99 women; mean age, 56.8±14.6 years). Anatomical (location, length, grade, and number of stenoses) and clinical variables (sex, age, prior AVF, diabetes mellitus, AVF age, side, and location) were reviewed. A total of 330 stenoses were found in 228 patients. PTA was technically successful in 96.3% of the stenoses (n=319). Clinical success was achieved in 97.2% (n=321). Early dysfunction (within six months) was positively correlated with patient age (P < 0.001) and diabetes (P < 0.005). Older age (P < 0.001) and diabetes (P = 0.002) were associated with a lower primary patency rate. Patient age (P %lt; 0.001), presence of diabetes (P = 0.023), length of stenosis (P = 0.003), early recurrence (P = 0.003) and presence of residual stenosis (P = 0.014) were associated with a lower secondary patency rate. Patency of dysfunctional hemodialysis fistulas can be maintained safely with continuous follow-up and repeated interventions without shortening the venous segment by surgical revision. Percutaneous approach to hemodialysis access stenosis is an alternative to the conventional surgical approach and PTA is an effective treatment method for dysfunctional AVF.

Wind energy has an important place in renewable energy sources. Biggest challenges in wind energy production are the variability of the wind and difficulty of estimation of true wind speed. In this study, 25,777 records have been taken from wind measurements carried out at Mehmet Akif Ersoy University campus. Records include meteorological data such as wind speed at different heights/altitudes, wind direction, temperature, pressure and humidity. In order to estimate the wind speed that may occur at 61 m altitude, multilayer perceptron and radial basis function methods have been used. During the application phase, 100 artificial neural networks were trained and performance evaluations of these networks were done. The obtained results show that the wind speed at 61 m can be estimated with 99% accuracy using artificial neural network when other meteorological data are taken as input.

Purpose To present our experience with redirecting the outflow of mature arteriovenous fistulae (AVFs) in patients with cannulation and/or suboptimal flow problems by percutaneous intervention using the Amplatzer Vascular Plug II (AVP II). Methods We retrospectively reviewed patients who presented with difficulty in cannulation and/or suboptimal flow in the puncture zone of the AVF and who underwent intervention using the AVP II to redirect the outflow through a better cannulation zone from March 2009 to November 2012. The mean survival rate of all AVFs was estimated, and the effects of patient age, sex, and AVF age on the AVF survival time were determined. Results In total, 31 patients (17 male and 14 female) with a mean age of 57.8 years (range, 20–79 years) were included. In 2 patients, the AVF failed within the first 15 days because of rapid thrombosis. In 9 patients, the new AVF route was working effectively until unsalvageable thrombosis developed. One of the 31 patients died 9 months before the last radiologic evaluation. The new AVF route was still being used for dialysis in the remaining 19 patients. The mean AVF survival rate was 1,061.4 ± 139.4 days (range, 788–1,334 days). Patient age, sex, and AVF age did not affect the survival time. Conclusion We suggest that the AVP II is useful for redirecting the outflow of AVFs with cannulation problems and suboptimal flow. Patency of existing AVFs may be extended, thereby extending surgery-free or catheter intervention-free survival period.

Background/aims Vascular access dysfunction caused by stenosis is a major complication for hemodialysis (HD) patients. However, physiopathology of late arteriovenous fistula (AVF) stenosis is still under investigation. The aim of the present study was to evaluate the association between plasma soluble EPCR (sEPCR) with serum soluble E-selectin (sE-selectin) concentration and late AVF stenosis in HD patients. Methods Plasma sEPCR and serum sE-selectin concentrations were measured in 94 HD patients. Using these data, we studied the association of sEPCR and sE-selectin with the presence and degree of AVF stenosis using ultrasonography and fistulogram. Results Fifty-one patients have AVF stenosis, and the others ( n  = 43) have patent AVF. The degree of AVF stenosis was correlated with serum sE-selectin levels ( r  = 0.351, p  = 0.01), but not sEPCR ( r  = 0.075, p  = 0.702). The median level of sE-selectin was statistically higher in the group of AVF stenosis than in the group of patent AVF [463.2 pg/ml (275.4–671.4) vs. 162.5 pg/ml (96.7–285.3), p  = 0.001]. Increased sE-selectin levels [OR (OR) = 6.356, p  = 0.015] and high levels of LDL (OR = 4.321, p  = 0.044) were independent predictors of late AVF stenosis in the multivariate model. Conclusions sE-selectin and the LDL were the most important predictors of late AVF stenosis. In addition, sE-selectin correlated with the degree of AVF stenosis. We suggested that atherosclerosis might be contributing factor for development of late AVF stenosis.

Figure 2 Brain diffusion magnetic resonance imaging (MRI) showed a bilaterally enlarged superior ophthalmic vein [Figure 3] and brain-neck computed tomography (CT) venography demonstrated a bilateral superior ophthalmic vein with dolicoectatic appearance. Proptosis, chemosis, dilated conjunctival veins, blood in Schlemm's canal, uncontrollable elevated intraocular pressure, and retinal hemorrhages are major symptoms and signs of CCFs.

Determining P and S wave arrival times while minimizing noise is a major problem in seismic signal analysis. Precise determination of earthquake onset arrival timing, determination of earthquake magnitude, and calculation of other parameters that can be used to make more accurate seismic maps are possible with the detection of these waves. Experts try to determine these waves by manual analysis. But this process is time-consuming and painful. In this study, a new method that enables the determination of P and S wave arrival times in noisy recordings is recommended. This method is based on the hybrid usage of empirical mode decomposition and Teager–Kaiser energy operator algorithms. The results show that the proposed system gives effective results in the automatic detection of P and S wave arrival times. Promisingly, the recommended system might serve as a novel and powerful candidate for the effective detection of P and S wave arrival time.

A 66-year-old woman presented with back pain and arm claudication. Severe stenosis of the left proximal subclavian artery was detected incidentally by thorax computed tomography. Doppler ultrasonography and phase contrast magnetic resonance angiography (PCMRA) evaluation revealed partial subclavian steal. The stenosis was successfully treated with percutaneous stenting. Imaging findings are described and a brief review of the literature emphasizing the role of PCMRA in diagnosing partial steal is discussed.