Evaluation and optimization of central vision compensation techniques

Non-costly, non-invasive, safe, and reliable electronic vision enhancement systems (EVES) and their methods have presented a huge medical and industrial demand in the early 21st century. Two unique, vision compensation and enhancement algorithms are reviewed and compared, qualitatively optimizing the view of a restricted (or truncated) image. The first is described as the convex or fish-eye technique, and the second is the cartoon superimposition or Peli technique (after the leading author for this research). The novelty in this dissertation is in presenting and analyzing both of these with a comparison to a novel technique, motivated by characterization of quality vision parameters (or the distribution of photoreceptors in the eye), in an attempt to account for and compensate reported viewing difficulties and low image quality measures associated with these two existing methods.;This partial cartoon technique is based on introducing the invisible image to the immediate left and right of the truncated image as a superimposed cartoon into respective sides of the truncated image, yet only on a partial basis as not to distract the central view of the image. It is generated and evaluated using MatlabRTM to warp sample grayscale images according to predefined parameters such as warping method, cartoon and other warping parameters, different grayscale values, as well as comparing both the static and movie modes. Warped images are quantitatively compared by evaluating the Root-Mean-Square Error (RMSE) and the Universal Image Quality Index (UIQI), both representing image distortion and quality measures of warped, as compared to original images for five different scenes; landscape, close-up, obstacle, text, and home (or low-illumination) views. Remapped images are also evaluated through surveys performed on 115 subjects, where improvement is assessed using measures of image detail and distortion.;It is finally concluded that the presented partial cartoon method exhibits superior image quality for all objective measures, as well as for a majority of subjective distortion measures. Justification is provided as to why the technique does not offer superior subjective detail measures. Further improvement is suggested, as well as additional techniques and research

Analytical asssessment of the structural behavior of a specific composite floor system at elevated temperatures using a newly developed hybrid intelligence method

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures

Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm

Synthesis and characterization of Turbinaria ornata mediated Zn/ZnO green nanoparticles as potential antioxidant and anti-diabetic agent of enhanced activity

Turbinaria ornata marine macro-algae (TUN) have been applied as carriers for the metallic zinc/ZnO blended nanoparticles, which were synthesized by implementing the extracted phytochemicals of the algae. The resulting hybrid bio-composite (Zn@ZnO/TUN) was characterized as a potential product of promising antioxidant and antidiabetic characteristics in synergetic studies. The obtained composite demonstrate t6he existing or complex biological active groups related to zinc (Zn-O stretching and tetrahedral Zn coordination) and organic groups (amino, methyl, carboxylic, alkynes, P=O, C–C–O, C=N, and N–O) corresponding to the extracted phytochemicals of algae (polysaccharides, phospholipids, lipids, fucose, and phosphodiester). The assessment of Zn@ZnO/TUN hybrid as an anti-oxidant agent validated excellent effectiveness towards the commonly examined radicals (DPPH (88.2 ± 1.44%), nitric oxide (92.7 ± 1.71%), ABTS (90.5 ± 1.8%), and O2●− (30.6 ± 1.32%), considering the determined performance for the commercially used standard (ascorbic acid). Regarding the antidiabetic properties, the incorporation of Zn@ZnO/TUN inhibits the function and activities of the key oxidizing enzymes, either the commercial forms (α-amylase (88.7 ± 1.3%), α-glucosidase (98.4 ± 1.3%), and amyloglucosidase (97.3 ± 1.4%) or the crude intestinal active forms (α-amylase (66.2 ± 1.4%) and α-glucosidase (95.1 ± 1.5%). This inhibitory effectiveness of Zn@ZnO/TUN is significantly better than the measured performances using commercialized miglitol drugs and slightly better than acarbose. Considering the expense and adverse effects of conventional medications, the synthesized Zn@ZnO/TUN blend could be evaluated as a marketable antidiabetic and antioxidant medication. The findings also demonstrate the influence of the derived phytochemicals from Turbinaria ornata and the incorporation of its algae residuals as carriers for the metal nanoparticles on the biological function of the composite. The cytotoxicity investigation reflected safety effect of the composite on colorectal fibroblast cells (CCD-18Co) (96.3% cell viability) and inhibition effect on cancerous colorectal cells (HCT-116) (47.3% cell viability)

Performance Evaluation of a Direct Absorption Collector for Solar Thermal Energy Conversion

The solar absorption efficiency of water as a base-fluid can be significantly improved by suspending nanoparticles of various materials in it. This experimental work presents the photo thermal performance of water-based nano-fluids of graphene oxide (GO), zinc oxide (ZnO), copper oxide (CuO), and their hybrids under natural solar flux for the first time. Nanofluid samples were prepared by the two-step method and the photothermal performance of these nanofluid samples was conducted under natural solar flux in a particle concentration range from 0.0004 wt % to 0.0012 wt %. The photothermal efficiency of water-based 0.0012 wt % GO nanofluid was 46.6% greater than that of the other nanofluids used. This increased photothermal performance of GO nanofluid was associated with its good stability, high absorptivity, and high thermal conductivity. Thus, pure graphene oxide (GO) based nanofluid is a potential candidate for direct absorption solar collection to be used in different solar thermal energy conversion applications

Thermal performance enhancement of nanofluids based parabolic trough solar collector (NPTSC) for sustainable environment

Due to rapid industrialization and urbanization, upward rise in carbon emissions in the atmosphere, and depletion of fossil fuel and gas reserves have forced to find alternative renewable energy resources, where solar energy is one of the most promising source. Parabolic trough solar collectors (PTCs) can effectively transfer high temperature in the tube of receiver upto 400 °C. In this study, Computational Fluid Dynamics (CFD) analysis is used to analyse the effect of multiple working fluids on efficiency of the PTC. Two different types of nanofluids used for analyising the thermal efficiency of PTC through CFD simulations, are Alumina and Copper-oxide nanofluids. The concentration of Copper Oxide and Alumina was kept to 0.01% in the nanofluids. The efficiency for PTC is calculated at two different mass flow rates i.e., 0.0112 Kg/s and 0.0224 Kg/s. The highest efficiency is 13.01 and 13.1% using Al2O3 as nanofluids at 0.0112 Kg/s and 0.0224 Kg/s flow rates, while CuO has an efficiency of 13.92% and 14.79% for these flow rates. The behaviour of absorber tube material on temperature distribution for steel, copper and aluminum as absorber tube material was also investigated. Changing the material from steel to copper and aluminum increased the outlet temperature of the fluid. The maximum output temperature was achieved for copper is 311 K while steel and aluminum showed lower temperature of 307 K and 308 K of the fluid at the outlet. Furthermore, the impact of the receiver tube's length on the working fluid's temperature is also studied. Copper Oxide nanofluid has higher temperature at the outlet for both mass flow rates as compared to alumina nanofluid. Accordingly, a comparison was made for the CFD results with the experimental findings from literature. The nanofluids based PTCs system is promising method for the sustainable environment applications

The impact of Oligo-miocene basaltic intrusions on the petroleum system in Gulf of Suez rift basin, Egypt: new insights into tikhermal maturity and reservoir quality

In recent years, the petroleum industry has prioritized the exploration of new and unconventional petroleum reservoirs. As a result, this study assessed the significance of basaltic intrusions from two key aspects: their impact on the thermal maturity of pre-rift source rocks and their potentiality as reservoirs. The present study attempts to integrate surface field investigations of basaltic dykes in Wadi Nukhul and Wadi Matulla as surface analogs with petroleum system modeling of pre-rift source rocks containing subsurface basaltic intrusions in the Abu Rudeis-Sidri field. Therefore, the fracture networks were observed in Wadi Nukhul and Wadi Matulla, suggesting that both the basaltic dykes and host rocks have interconnected fractures, which is critical for a high-quality reservoir of the dykes and efficient oil expulsion. As a result, the analysis of burial history, temperature, maturity, generation, transformation ratio, and expelled oil quantity revealed a significantly high value for basaltic intrusions. Moreover, the Abu Rudeis-Sidri field had a good petroleum system with thermally mature source rocks by basaltic intrusions. Furthermore, the fractured basaltic intrusions presented a high-quality oil reservoir well-sealed by the thick Rudeis Formation. Oil production has doubled since the discovery of this reservoir. This study introduces a novel approach to understanding the distribution pattern of basaltic intrusions in subsurface and surface analogs, which can serve as a model for exploring new potential unconventional basaltic reservoirs in the Gulf of Suez rift basin

Improving the Mechanical Properties of Concrete Mixtures by Shape Memory Alloy Fibers and Silica Fume

Concrete, as one of the most widely applied materials in buildings, has high environmental impacts. Researchers are continually seeking solutions to mitigate these environmental issues while enhancing the mechanical strength and durability of concrete. However, there is a lack of studies on the effect of combining silica fume (SF) as pozzolanic materials and shape memory alloy (SMA) fibers on the mechanical properties of concrete. Moreover, there is very limited research on the influence of these materials on concrete mixtures after primary failure cracks using the secondary compressive strength test. In this research, 0.1, 0.2, and 0.3% SMA and 5, 7.5, and 10% SF were applied and then subjected to compressive strength, splitting tensile strength, flexural strength, secondary compressive strength, and ultrasonic pulse velocity tests. According to the results, 10% SF is more economical, which increases the compressive, splitting tensile, and flexural strength by 14%, 7%, and 10%, respectively. Also, using 0.3% SMA improves the compressive, splitting tensile, and flexural strength by 2%, 5%, and 8%, respectively. Furthermore, SMA has the ability to reduce the secondary compressive strength compared to other samples, indicating the quality of this material in controlling stress after cracking. Finally, it was indicated that the combined use of these two materials increases the strength parameters