The water-retaining and yield-increasing capacity of super-absorbent polymer (SAP) are essential for soil remediation in arid and semi-arid areas. Water availability is an increasing challenge to ...plant development and crop yield. During the growing seasons in 2021 and 2022, the present study was conducted to evaluate the effect of the addition of different amounts of SAP on the development and yield of green beans (Phaseolus vulgaris L. cv Bronco) under varying water deficit stresses, compared with the control treatment without SAP and water deficit stress. The results demonstrated that a 50% reduction in water requirement (WR) resulted in significant decreases in leaf fresh weight, specific leaf area, leaf total chlorophyll content, pod number, leaf free water content, pod fresh weight per plant, and yield. Decreases were also found in pod total chlorophyll content, carotenoids, dry matter and total protein, leaf proline content, and crude fiber content. Additionally, leaf water saturation deficit was significantly increased under the stress compared with the full irrigation at 100% WR. However, irrigation at 75% WR increased pod contents of ascorbic acid, total sugars, and leaf bound water. The current study also indicated that addition of SAP significantly enhanced the above-mentioned growth characteristics under irrigation at 50% and 75% WR. Treatment with SAP at 3 g/plant was the most effective in mitigating the adverse effects of water deficiency, especially at the irrigation rate of 75% WR. Pearson’s correlation analysis showed significantly positive correlations between the growth parameters, as well as pod yield, under water stress and SAP. This study provides a promising strategy for green bean cultivation by adding SAP to soil to alleviate water shortage stress.
Abstract Background Pulmonary hypertension (PH) is a common and morbid complication of left heart disease with 2 subtypes: isolated post-capillary pulmonary hypertension (Ipc-PH) and combined ...post-capillary and pre-capillary pulmonary hypertension (Cpc-PH). Little is known about the clinical or physiological characteristics that distinguish these 2 subphenotypes or if Cpc-PH shares molecular similarities to pulmonary arterial hypertension (PAH). Objectives The goal of this study was to test the hypothesis that the hemodynamic and genetic profile of Cpc-PH would more closely resemble PAH than Ipc-PH. Methods Vanderbilt University’s electronic medical record linked to a DNA biorepository was used to extract demographic characteristics, clinical data, invasive hemodynamic data, echocardiography, and vital status for all patients referred for right heart catheterization between 1998 and 2014. Shared genetic variants between PAH and Cpc-PH compared with Ipc-PH were identified by using pre-existing single-nucleotide polymorphism data. Results A total of 2,817 patients with PH (13% Cpc-PH, 52% Ipc-PH, and 20% PAH) were identified. Patients with Cpc-PH were on average 6 years younger, with more severe pulmonary vascular disease than patients with Ipc-PH, despite similar comorbidities and prevalence, severity, and chronicity of left heart disease. After adjusting for relevant covariates, the risk of death was similar between the Cpc-PH and Ipc-PH groups (hazard ratio: 1.14; 95% confidence interval: 0.96 to 1.35; p = 0.15) when defined according to diastolic pressure gradient. We identified 75 shared exonic single-nucleotide polymorphisms between Cpc-PH and PAH enriched in pathways involving cell structure, extracellular matrix, and immune function. These genes are expressed, on average, 32% higher in lungs relative to other tissues. Conclusions Patients with Cpc-PH develop pulmonary vascular disease similar to patients with PAH, despite younger age and similar prevalence of obesity, diabetes mellitus, and left heart disease compared with patients with Ipc-PH. An exploratory genetic analysis in Cpc-PH identified genes and biological pathways in the lung known to contribute to PAH pathophysiology, suggesting that Cpc-PH may be a distinct and highly morbid PH subphenotype.
The purpose of this study is to define and assess a new, renewable and sustainable energy supply system for islands and remote area where ocean thermal energy conversion (OTEC)/photovoltaic with ...hydrogen storage system is proposed. Components of this system are a turbine, generator, evaporator, condenser, pumps, photovoltaic panels, electrolyzer, hydrogen tanks, fuel cell and converter. To evaluate the proposed hybrid system, energy, exergy and economic analysis are employed. For OTEC, an optimization algorithm is applied to find the optimum working fluid (R134A, R407C, R410A, R717, R404A, and R423A), evaporation and condensation temperatures, and cold and warm seawater temperature differences between the inlet and outlet of evaporator/condenser. The results demonstrate that the maximum specific power of OTEC was achieved to be 0.3622 kW/m2 for R717 and 0.3294 kW/m2 for R423A working fluids. The overall energy efficiency for the hybrid renewable energy system was obtained 3.318%. The maximum energy loss was occurred by the turbine. The exergy efficiency of the hybrid system was obtained 18.35% and the payback period of the proposed system was obtained around 8 years. The unit electricity cost for the system was achieved as 0.168 $/kWh which is valuable compared to 0.28 $/kWh of the previous system.
•A hybrid OTEC/photovoltaic system with hydrogen storage system is presented.•Energy, exergy and economic analyses are carried out to assess the proposed system.•The energy efficiency for the proposed system was obtained as 3.318%.•The exergy efficiency of the hybrid system was achieved as 18.35.•The payback period of the hybrid renewable energy system was found to be 8 years.
Assessment of solar potential over a location of interest is introduced as an important step for the successful planning of solar energy systems (photovoltaic or thermal). Due to the absence of ...meteorological stations and sophisticated solar sensors, solar data may be unavailable for every point of interest. Hence, empirical and intelligence methods are developed to estimate solar irradiance data. In this study, the idea of artificial intelligence methods is employed to predict the daily global solar radiation. The developed models are: group method of data handling (GMDH) type neural network, multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized with particle swarm optimization algorithm (ANFIS-PSO), ANFIS optimized with genetic algorithm (ANFIS-GA) and ANFIS optimized with ant colony (ANFIS-ACO). The data are collected from 12 stations in different climate zones of Iran. The input variables of the models are including month, day, average air temperature, maximum air temperature, minimum air temperature, air pressure, relative humidity, wind speed, top of atmosphere insolation, latitude and longitude. The results demonstrated that although the developed models can successfully predict the global horizontal irradiance, the GMDH model outperforms the other developed models. The values of root mean square error (RMSE), determination coefficient (R2) and mean square error (MSE) for the GMDH model were 0.2466 (kWh/m2/day), 0.9886 and 0.0608 (kWh/m2/day), respectively.
•Daily global solar radiation is predicted using artificial intelligence methods.•The forecasting models are MLFFNN, GMDH, ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-ACO.•The results show the GMDH neural networks outperforms the other developed models.•The PSO algorithm performs better than the GA and ACO to optimize the ANFIS model.
Purpose
To develop and evaluate an automated deep learning model to predict the anatomical outcome of rhegmatogenous retinal detachment (RRD) surgery.
Methods
Six thousand six hundred and sixty-one ...digital images of RRD treated by vitrectomy and internal tamponade were collected from the British and Eire Association of Vitreoretinal Surgeons database. Each image was classified as a primary surgical success or a primary surgical failure. The synthetic minority over-sampling technique was used to address class imbalance. We adopted the state-of-the-art deep convolutional neural network architecture Inception v3 to train, validate, and test deep learning models to predict the anatomical outcome of RRD surgery. The area under the curve (AUC), sensitivity, and specificity for predicting the outcome of RRD surgery was calculated for the best predictive deep learning model.
Results
The deep learning model was able to predict the anatomical outcome of RRD surgery with an AUC of 0.94, with a corresponding sensitivity of 73.3% and a specificity of 96%.
Conclusion
A deep learning model is capable of accurately predicting the anatomical outcome of RRD surgery. This fully automated model has potential application in surgical care of patients with RRD.
A unique feature of human soft tissue liposarcoma is a stable (12;16)(q13;p11) translocation observed mainly in myxoid and roundcell liposarcomas. This translocation results in FUS/CHOP fusion ...transcripts with a corresponding oncogenic protein. We hypothesised that genes downstream of FUS/CHOP might serve as attractive candidates for novel tumour associated antigens. Among a panel of analysed genes, only pentraxin related gene (PTX3) demonstrated high expression in liposarcomas as compared to normal tissues. The analysis of RNA and protein expression demonstrated concordant results. However, the level of RNA and protein overexpression did not correlate in all cases. Finally, PTX3 expression was not related to presence of a FUS/CHOP fusion transcript within the liposarcoma tissues.
PTX3 has been associated with adipocyte differentiation and now, additionally, is characterised by a markedly increased expression in human soft tissue liposarcoma. This finding mandates further research efforts to clarify the exact role of PTX3 in liposarcoma oncogenesis.
•Developing an intelligent approach for modeling of geothermal organic Rankine cycle.•The intelligent methods are ANFIS optimized with PSO (ANFIS-PSO) and MLP-PSO.•Intelligent methods are employed ...for thermodynamic and economic modeling of the system.•Intelligent methods have shown an excellent modeling ability.
Geothermal energy is a renewable resource that is constantly available. The low geothermal well operating lifetime is the main challenge in using this type of renewable energy. This problem can be covered by the aid of solar system (hybrid system). For complicated renewable energy systems, finding the optimum design parameters and operating conditions require to develop experimental apparatus or sophisticated thermodynamic models. Hence, in this study, artificial intelligence (AI) approach is proposed for modeling the geothermal organic Rankin cycle (GORC) equipped with solar thermal unit. Indeed, the current study depicts how AI methods can meticulously simulate the operation of a complicated renewable energy system. The developed intelligent methods are adaptive neuro-fuzzy inference system (ANFIS) optimized with particle swarm optimization (PSO) algorithm (ANFIS-PSO) and multilayer perceptron (MLP) neural network optimized with PSO algorithm (MLP-PSO). The models are composed based on the main design parameters of the geothermal system that are solar radiation, well temperature, working fluid mass flow rate, turbine output pressure, surface area of the solar collector and preheater inlet pressure. The intelligent models use the mentioned input variables to predict the net power output, energy efficiency, exergy efficiency and levelized cost of energy (LCOE) of the GORC. Energy, exergy and economic analyses are carried out for the low global warming potential (GWP) refrigerants. It was found out that although the intelligent models can meticulously predict the targets, ANFIS-PSO performs better than MLP-PSO for modeling the GORC with solar system. Root mean square error of this model for prediction of power generation, energy efficiency, exergy efficiency and LCOE was 12.023 (kW), 3.587 ×10-4, 3.278 ×10-4 and 1.332 ×10-4, respectively.
Determining the optimal sizing of a solar power tower system (SPTS) with a thermal energy storage system is subject to finding the optimum values of design parameters including the solar multiple ...(SM), design direct normal irradiance (DNI) and thermal storage hours. These design parameters are determined for each station separately and have remarkable effects on the thermo-economic performance of the system. This paper aims to demonstrate how artificial intelligence (AI) techniques may play an important role in addressing the above-mentioned need and help determine the optimum design parameters for different stations. For this purpose, we developed a thermo-economic model of a 100 MW SPTS with a molten salt storage system for five stations (two stations in India, and one each in Bangladesh, Pakistan, and Afghanistan). A method-based AI is utilized in this paper to ascertain the design parameters of the system. Additionally, a novel hybrid method based on adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching-learning-based optimization algorithm (ANFIS-GATLBO) is employed. The input parameters are latitude, longitude, design point DNI and SM, while the annual energy produced, levelized cost of energy and capacity factor are the target variables. The results of the study show that although the annual energy produced by SPTS rises by increasing the SM and decreasing design point DNI, optimum design parameters should be determined by the economic factors. In addition, it was found that the ANFIS-GATLBO method used in this study successfully predicted the targets with a correlation coefficient close to 1.
•A 100 MW solar power tower system (SPTS) for 5 different stations is designed and evaluated.•Design parameters (solar multiple (SM), design direct normal irradiance (DNI) and thermal storage hours) are discussed.•An intelligent method based on ANFIS optimized with combination of GA and TLBO is developed to simulate the behavior of SPTS.•To find the optimum design parameters of SPTS, the economic criteria should be taken into account.
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