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Modeling techniques can be powerful predictors of soil salinity across various scales, ranging from local landscapes to global territories. This study was aimed to examine the accuracy of soil salinity prediction model integrating ANNs (artificial neural networks) and topographic factors with different cell sizes. For this purpose, soil salinity was determined at 103 points in the east of Mashhad, Razavi Khorasan, Iran. The region was categorized into two distinct parts: study area (1) (with a steep topography) and study area (2) (with a flat topography). To explore the impact of terrain on salinity prediction accuracy, ANNs were trained using topographical factors as inputs across a range of cell sizes (30, 50, 90, 200, and 500 m). The model’s effectiveness was evaluated based on their Root Mean Square Error (RMSE) and coefficient of determination (R 2 ). Results indicated variability in model performance, with RMSE ranging from 0.324 to 0.461 and R 2 from 0.159 to 0.281 across the spectrum of cell sizes. Deeper analysis on different topographical influences showed that for the study area (1), a cell size of 30 m yielded the most accurate predictions (RMSE = 0.234 dS/m and R 2 = 0.515), whereas for the study area (2), a cell size of 50 m was optimal (RMSE = 0.658 dS/m and R 2 = 0.597). In general, the findings concluded that smaller cell sizes can enhance prediction accuracy in areas with complex and varied topography, while larger cell sizes can be more effective in flat areas. This study demonstrates the significance of incorporating terrain attributes and their optimal resolutions for accurate soil salinity prediction. Our findings underscore the importance of tailoring the resolution of input data to match the specific topographic features of the area, challenging the conventional notion that higher input resolution invariably yields better results in soil properties prediction. These insights provide valuable guidance for effective soil management and agricultural practices, as well as contribute to more informed decision-making in land management and environmental conservation.

Irregular growth in the surrounding lands is one of the most important issues for the city managers and programmers at various levels. Whereas nowadays study the process of land use changes to urban use plays the main role in long time decisions and programs, predicting the process of city growth and its modeling in future with precise methods for management and urban expansion control will be necessary more than other times. One of urban growth modeling is cellular automata model. This model has been used widely in urban studies because of its dynamic nature, ability of Integration with other models, ability to modify the model and required data availability. In this article, to maximize the efficiency of the cellular automata model and its constraints, the integration of the AHP automated cell model and cellular automata model have been used; and its accuracy has been evaluated. This article has been practical because its related principles has been collected in a documentary manner and has been used to analyses the issue in comparative and quantitative methods. Initially, the unplanned growth of Qazvin city has been investigated by Holdern and Shannon model. Then main parameters including distance from roads, land prices, distance from faults, distance from the rivers, soil gender, slope, permission to build land, topography, landscape, view to gardens and forest park as parameters involved in the development of Qazvin city are considered. The input data used in this research are Landsat tm and DEM images of the city of Qazvin in 1996 and 2016. Also, to evaluate the correctness of the model responses, the map of the developed regions in 2016 and the Kappa coefficient have been used. The Kappa coefficient is 92.3%, which is considered significant and appropriate and gave the fact that the Kappa number is acceptable. The Qazvin simulation was made in 2026. The results show that the proposed integrated model is suitable for studying urban growth.

The hydrothermal behavior in a helical double-tube heat exchanger is numerically estimated. A new type of swirl generator with two sections, including; outer curved blades and a semi-conical section with two holes in the inner section, is employed. Two geometrical factors, containing the length (L1) and the position of the swirl generator (S), are used for investigation. The calculations were performed by a commercial FVM code, ANSYS FLUENT 18.2. The numerical outcomes show that a shorter length of the swirl generator leads to a better hydrothermal behavior. Accordingly, the model with L1 = 100 mm at m˙ = 0.008 kg/s achieves the maximum thermal performance by about 17.65, 53.85, and 100% enhancement compared to the models L1 = 200, 300 mm, and without swirl generator. Among the different studied positions of the swirl generator, the maximum heat transfer coefficient and average Nusselt number in entire mass flow rates belong to the case with position S = 0.3π mm. Moreover, the thermal performance of the case with S = 0.3π mm is higher than cases with S = 0.1π mm, S = 0.5π mm, and without swirl generator by about 11.11, 53.84, and 100%, respectively.

In offshore stand‐alone microgrids, due to the lack of access to the main power grid, energy supply has always been vulnerable to many risks, such as uncertainty in fuel supply for diesel generators (DEGs) and unforeseen changes in renewable energy sources (RESs) and loads. As a result, this paper proposes a novel framework based on the hybrid renewable energy system (HRES) concept to optimize the capacity design and operation of HRES. The proposed framework is implemented on one of the Persian Gulf islands as a case study. This paper uses hybrid IGDT/stochastic optimization techniques for load and PV uncertainties and a novel wind‐based method for fuel procurement uncertainties. Using MIP modeling in GAMS, the paper shows that renewable and storage systems can reduce the total cost by 38% and the energy supply cost by 43% compared to conventional energy supply system. Accounting for uncertainties can boost the system robustness and reduce the expected total cost by 16% compared to the deterministic model.

Background The rate of cesarean section is increasing in all over the world with different drafts in various countries. This growth increases unpleasant outcomes of delivery. Recent studies explained the benefits of date palm fruit on labor process improvement. Date fruit can be considered as a factor for increasing vaginal delivery and also reducing the frequency of caesarean section in order to prevent its great complications. This systematic review has been designed to review clinical studies that investigate the effects of date palm fruit on labor outcomes (duration of labor stages, bishop score, and frequency of cesarean section) compared with routine cares. Methods This study was performed in 2019. Required data has been collected from electronic databases and manual searches. All randomized clinical trials evaluating the effects of date palm fruit on labor and delivery that were published from January 2000 to August 2019 in English and Persian languages, were incorporated in this systematic review. The methodological quality of the included studies was evaluated according to the risk of bias assessment of Cochrane handbook of systematic reviews, and were then reported using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Results Eight studies were included in the qualitative and quantitative synthesis. Meta-Analysis showed that date fruit consumption can significantly reduce active phase of labor (three trials with 380 participants; (MD = − 109.3, 95%CI (− 196.32, − 22.29; I 2  = 89%), P  = 0.01), and also it can significantly improve the bishop score (two trials with 320 participants; MD = 2.45, 95%CI (1.87, 3.04; I 2  = 0%), P  < 0.00001). Date fruit consumption had no effects on the duration of first, second, and third stages of labor, and the frequency of cesarean section. Conclusion Date can reduce the duration of active phase and improve the bishop score; however, due to from the low to mediate quality of the studies; it seems that the other studies are needed to prove these results better than this.

The stresses induced on plaque wall during stent implantation inside a stenotic artery are associated with plaque rupture. The stresses in the plaque–artery–stent structure appear to be distinctly different for different plaque types in terms of both distribution and magnitude. In this study, a nonlinear finite element simulation was executed to analyze the influence of plaque composition (calcified, cellular, and hypocellular) on plaque, artery layers (intima, media, and adventitia), and stent stresses during implantation of a balloon expandable coronary stent into a stenosed artery. The atherosclerotic artery was assumed to consist of a plaque and normal arterial tissues on its outer side. The results revealed a significant influence of plaque types on the maximum stresses induced within plaque wall and artery layers during stenting, but not when calculating maximum stress on stent. The stress on stiffer calcified plaque wall was in the fracture level (2.21 MPa), whereas cellular and hypocellular plaques play a protective role by displaying less stress on their wall. The highest von Mises stresses were observed on less stiff media layer. The findings of this study suggest a lower risk of arterial vascular injury for calcified plaque, while higher risk of plaque ruptures for cellular and hypocellular plaques.

This study developed a novel folding light shelf system that integrates reflectors, photovoltaic (PV) modules, and adaptive louvers that adjust based on solar altitude, aiming to improve daylight distribution, minimize glare, and reduce energy consumption in office buildings. The research employed an advanced optimization approach, utilizing Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Latin Hypercube Sampling, a highly effective method suitable for managing complex multi-objective scenarios involving numerous variables, to efficiently identify high-performance configurations with increased precision. Key design variables across all three components of the system included angle, width, distance, and the number of folds in the light shelf, along with the number of louvers. The proposed method successfully integrates PV technology into light shelves without compromising their functionality, enabling both daylight control and energy generation. The optimization results demonstrate that the system achieved up to a 15% improvement in useful daylight illuminance (UDI) and a 16% reduction in cooling energy consumption. Furthermore, the PV modules generated 509.5 kWh/year, ensuring improved efficiency and sustainability in building performance.

Background: Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior risk factor, and demographic data from 1652 participants, split evenly between 826 control subjects and 826 glaucoma patients. Methods: We extracted structural data from control and glaucoma patients’ electronic health records (EHR). Three distinct machine learning classifiers, the Random Forest and Gradient Boosting algorithms, as well as the Sequential model from the Keras library of TensorFlow, were employed to conduct predictive analyses across our dataset. Key performance metrics such as accuracy, F1 score, precision, recall, and the area under the receiver operating characteristics curve (AUC) were computed to both train and optimize these models. Results: The Random Forest model achieved an accuracy of 67.5%, with a ROC AUC of 0.67, outperforming the Gradient Boosting and Sequential models, which registered accuracies of 66.3% and 64.5%, respectively. Our results highlighted key predictive factors such as intraocular pressure, family history, and body mass index, substantiating their roles in glaucoma risk assessment. Conclusions: This study demonstrates the potential of utilizing readily available clinical, lifestyle, and demographic data from EHRs for glaucoma detection through deep learning models. While our model, using EHR data alone, has a lower accuracy compared to those incorporating imaging data, it still offers a promising avenue for early glaucoma risk assessment in primary care settings. The observed disparities in model performance and feature significance show the importance of tailoring detection strategies to individual patient characteristics, potentially leading to more effective and personalized glaucoma screening and intervention.

This study investigates the diurnal, seasonal, monthly and temporal variation of land surface temperature (LST) and surface urban heat island intensity (SUHII) over the Isfahan metropolitan area, Iran, during 2003–2019 using MODIS data. It also examines the driving factors of SUHII like cropland, built-up areas (BI), the urban–rural difference in enhanced vegetation index (ΔEVI), evapotranspiration (ΔET), and white sky albedo (ΔWSA). The results reveal the presence of urban cool islands during the daytime and urban heat islands at night. The maximum SUHII was observed at 22:30 p.m., while the minimum was at 10:30 a.m. The summer months (June to September) show higher SUHII compared to the winter months (February to May). The daytime SUHII demonstrates a robust positive correlation with cropland and ΔWSA, and a negative correlation with ΔET, ΔEVI, and BI. The nighttime SUHII displays a negative correlation with ΔET and ΔEVI.

Background The incidence of acute aortic dissections is 3–6 patients per 100,000 in a year, with a high mortality rate of 40% at the initial diagnosis and increasing to 90% in an hour. There are several known risk factors for acute aortic dissection; however, the most common risk factor is systemic hypertension. Different conditions have been reported to be associated with resistant hypertension, including hyperaldosteronism. Case presentation A 57-year-old Persian man came to our clinic with occasional claudication after 30 m distance walking, left leg pain, and symptoms of chronic limb ischemia, including a cold left leg with a shiny appearance. He had a past medical history of recently diagnosed resistant hypertension and a past surgical history of a femoropopliteal bypass and a balloon angioplasty. His computed tomography angiography of the abdominopelvic cavity and lower limbs revealed a dissection of the infrarenal aorta at the bifurcation of common iliac arteries, occlusion of the left external iliac artery, and dissection of the left common iliac artery. In addition, a mass measuring 6 cm × 5 cm × 2 cm was identified in the patient’s left adrenal gland. The ostium of the false lumen was in the distal part of dissection so we decided to use an antegrade approach to repair the dissection. He underwent simultaneous surgeries for aneurysmal repair and adrenalectomy. Conclusion A vast systematic search of literature in Scopus, Web of Science, PubMed, and Google Scholar was carried out to identify cases of hyperaldosteronism relating to vascular dissection that were either treated with surgery or medication. Our results support the theory suggesting that hyperaldosteronism can be considered a risk factor for vascular dissection despite its effects on hypertension.