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In fractured reservoirs, the fracture network provides the main path for fluid flow. Appropriate estimation of the fracture permeability influences the precise prediction of the reservoir’s future performance. Commonly, for a known geometry of natural or induced fracture, the permeability is estimated by applying local cubic law. One major drawback of this approach is that the fracture surface roughness, which has a significant effect on fracture permeability, is not considered. Moreover, the knowledge about the impact of fracture surface roughness on fracture permeability is not currently sufficient. In this research, the fluid flow in fractures with rough-walled surfaces was studied using computational fluid dynamics. For this purpose, the fluid flow through fractures was simulated by applying appropriate roughness for fracture walls. Furthermore, two correlations, based on response surface methodology and power-law models, were proposed to predict fracture permeability as a function of four independent variables (surface roughness, fracture aperture, angle, and porosity). The results of the two presented correlations were validated, and the statistical analysis indicates that both models are appropriate to predict fracture permeability. The findings of this study will be of great assistance with understanding and characterization of the fluid flow in rough fractures and can be used in future works.

Nano-PCMs, which contain nanostructured materials, can enhance the low thermal conductivity of phase change materials (PCMs). It is crucial to predict the precise thermal conductivity of nano-PCM to assess the heat transfer during phase change procedures such as melting and solidification. In this study, artificial neural network (ANN) and response surface method (RSM) were used to develop a model for predicting the thermal conductivity of carbon-based nano-enhanced phase change materials (NEPCMs) using 482 experimental samples collected from various datasets. The carbon-based nano-particles were SWCNTs, MWCNTs, graphite, graphene, and (CNFs). Six input parameters were considered with varying temperature, mass fractions, sizes, thermal conductivities of PCMs and nano-particles, and the phase of PCM. The ANN was designed using a multi-layered feed-forward structure and Levenberg–Marquardt back-propagation algorithm with one hidden layer consisting of eight neurons. The transfer function was varied to achieve optimal performance. It was revealed that the predictive capacity of the ANN model is greater than that of the RSM model based on their corresponding the coefficient of determination (R2) and root mean square error (RMSE) values. The developed ANN achieved a R2value of 0.99, while the RSM method model achieved an R2value of 0.79.

Cystoid macular oedema (CMO), which is defined as a macular thickening and cystic changes due to accumulation of fluid, could be asymptomatic and only diagnosed using paraclinical techniques. Fluorescein angiography (FA) and optical coherence tomography (OCT) are useful in detecting CMO in clinical practice. Non-leaking CMO, also known as angiographically silent CMO, is referred to as cases of CMO without leakage in fluorescein angiography. This type of CMO has been reported in some retinal dystrophies, in cases of maculopathy as a side effect of certain drugs, and also in some systemic disorders. The exact mechanism and treatment options for this type of CMO are still not clear. This literature review aims to discuss different causes of non-leaking CMO, proposed mechanisms, and management options. Three sections including drugs, retinal dystrophies, and systemic disorders are discussed in this review.

Objective To compare the incidence of falls between patients with visually significant cataracts in both eyes and those who have undergone first-eye cataract surgery.Methods This retrospective case–control study involved patients with a history of cataracts in both eyes who had undergone first-eye cataract surgery within the past 9 to 12 months (pseudophakic group). The control group comprised patients with cataracts in both eyes (cataract group). We assessed best-corrected visual acuity (BCVA), systemic comorbidities and medications (using the Charlson comorbidity index), and independent daily activities (using the Lawton Instrumental Activities of Daily Living scale). The patients were questioned about experiencing two or more falls in the last 6 months.Results Each group comprised 50 patients. Binocular BCVA was significantly better in the pseudophakic group (0.05 ± 0.06 logMAR) than in the cataract group (0.77 ± 0.34 logMAR). Of all participants, 22% reported experiencing two or more falls in the last 6 months. Multivariate analysis demonstrated significantly better BCVA in participants with less than two falls.Conclusions Patients of advanced age with visually significant cataracts in both eyes are at a higher risk of falling. First-eye cataract surgery may mitigate the occurrence of falls by improving binocular BCVA.

We describe a technique for metallic intralenticular foreign body (ILFB) removal in a patient in whom there was no or minimal cataract formation or other complications. This technique required creating two corneal small incisions around the ILFB for inserting iris retractors to expose the ILFB. At the foreign body position, a clear corneal incision was made, and then the ILFB was removed with minimal manipulation by an intraocular magnet without complications. Because most occupational traumas occur in young people, this technique avoids the adverse outcomes of lens extraction in this age group.