Maria Jade Catalan Opulencia

One of the primary indicators of plaque vulnerability is the lipid composition of atherosclerotic plaques. Therefore, the medical industry requires a method to evaluate necrotic nuclei in atherosclerosis imaging with sensitivity. In this regard, photoacoustic imaging is a plaque detection method that provides chemical information on lipids and cholesterol thickness in the arterial walls of the patient. This aspect aims to increase the low‐frequency axial resolution by developing a new photoacoustic‐based system. A photoacoustic system has been developed to detect the cholesterol thickness of the blood vessels to observe the progression of plaque in the heart’s blood vessels. The application of the coherent photoacoustic discontinuous correlation tomography technique, which is based on a novel signal processing, significantly increased the cholesterol oleate’s sensitivity to plaque necrosis. By enhancing the quality of thickness detection, the system for measuring the thickness of cholesterol in blood vessels has been reduced to approximately 23 microns. The results show that the phase spectrum peaked at 100 Hz at 58.66 degrees, and at 400 Hz, the phase spectrum was 46.37 degrees. The minimum amplitude is 1.95 at 100 Hz and 17.67 at 400 Hz. In conclusion, it can be stated that photoacoustic imaging as a method based on new technologies is of great importance in medical research, which is based on the use of nonionizing radiation to perform diagnostic processes and measure different types of body tissues.

In response to the COVID-19 pandemic, single-use disposable masks saw a dramatic rise in production. Facial masks that are not properly disposed of will expose the environment to a form of non-biodegradable plastic waste that will take hundreds of years to degrade. Therefore, recycling such waste in an eco-friendly manner is imperative. Fibered or shredded waste masks can be used to make green concrete that is an environmentally friendly solution to dispose the facial masks. This study prepared six classes of concrete samples, three of which contained fibers from masks and three of which contained shredded masks at the ages of seven days and 28 days. The results show that in the seven-day and 28-day samples, mask fiber added to the mixes resulted in increased compressive strength. For seven-day and 28-day samples, the compressive strength increased by 7.2% and 10%, respectively. Despite that, the results of the shredded mask addition to concrete indicate that the increase in shredded mask volume has a minor impact on the compressive strength of the seven-day samples. An increase in shredded mask from 0.75 to 1% increased 28-day compressive strength by 14%. However, the compressive strength of the mask fiber decreased by 8 after 1% volume. According to a thermal analysis of 28-day concrete samples, as the fiber percentage increases, the mass loss percentage increases. The mass loss rate for samples containing fibers is higher than that for samples containing shredded mask pieces. In general, based on the results mentioned above, the use of fiber in concrete in its fiber state enhances its strength properties. As a result, using shredded mask pieces in concrete leads to better curing due to the reduction of residual capillary pore water loss in construction materials.

Today, due to the end of fossil fuels and efforts to reduce the use of renewable resources, wind energy is a suitable option for the production of electrical energy due to its high-power generation. To increase the output efficiency of wind turbines, maximum power point tracking techniques are required for wind turbine energy conversion systems. In this research, the maximum power point (MPPT) method for two-way fed wind turbine systems (DFIG) is presented. The performance of the induction generator is presented on both sides of the power and the values of this generator such as speed, torque, voltage, current and maximum power at the time of wind speed changes. The presented work is presented in two scenarios and the model is performed without the algorithm then, a maximum power point tracking method based on the Colonial Competition Algorithm (ICA) has been applied to estimate the power of the two power induction generators. According to the results, it can be said that in the scenario with the algorithm of generating electric power by the turbine, several times in the production state is 9 MW, which is the rate of the turbine's nominal power, while in another scenario, the power generated by the turbine is 85% of the power in the state with the algorithm.

Today, the uncontrolled abstraction of surface water and groundwater resources has created adverse consequences, which include: extinction of living organisms, land subsidence, salinity of coastal aquifers, increased pumping energy. Therefore, the need to manage available water resources is felt more than ever. Among the various water uses (agriculture, drinking, and industry), agriculture accounts for the bulk of water consumption. Due to the climate change and the growing population, determining the appropriate strategy and technology for irrigation is necessary. In the current study, a simulation model is used to numerically simulate the dynamics of daily soil moisture during the potato crop growing season and to estimate crop production and economic benefits. For climatic data, daily observations of a meteorological station have been used. Results and analyses have been presented for all cases of micro and traditional irrigation methods and agricultural management strategies of non-stress irrigation, low irrigation, and rainfed cultivation. The results showed that in the non-stress irrigation method, crop production and net profit are almost equal in both traditional and micro methods. In the low irrigation method, microtechnology has made crop production and net profit 1.75 times more than traditional technology, which indicates the impact of irrigation technology on crop production.

During the production from shale oil formations, the produced water has been dedicated to different procedures such as chemical enhanced oil recoveries, drilling mud making (e.g., for various purposes of lubrication and cooling) and hydraulic fracturing. One of the main challenges of wastewater treatment corresponds to (TDS) total dissolved solids. To measure the required water needed for different processes, it is necessary to proceed with every step saving and then make an average to calculate the required freshwater. In this regard, we have selected five different oil wells with the same rock and reservoir characteristics. SOW#3 has the highest rate of treatment (26%) and SOW#1 has the minimum treated wastewater during hydraulic fracturing processes. It corresponds to the large volume of solid and oil particles, which remained in the treatment devices. However, it is observed that SOW#1 has the highest rate of treatment (32%) and SOW#4 has the minimum treated wastewater (14%) in chemical enhanced oil recovery methods. On the other hand, SOW#3 has the highest rate of treatment (27%) and SOW#5 has the minimum treated wastewater from drilling mud preparation and other well facilities. It is observed that SOW#1 has the highest rate of treatment (27%) and SOW#5 has the minimum saving water during hydraulic fracturing processes, SOW#1 has the highest rate of treatment (38%) and SOW#4 has the minimum saving water (9%).

The melting duration in the photovoltaic/phase-change material (PV/PCM) system is a crucial parameter for thermal energy management such that its improvement can realize better energy management in respect to thermal storage capabilities, thermal conditions, and the lifespan of PV modules. An innovative and efficient technique for improving the melting duration is the inclusion of an exterior metal foam layer in the PV/PCM system. For detailed investigations of utilizing different metal foam configurations in terms of their convective heat transfer coefficients, the present paper proposes a newly developed mathematical model for the PV/PCM–metal foam assembly that can readily be implemented with a wide range of operating conditions. Both computational fluid dynamic (CFD) and experimental validations proved the good accuracy of the proposed model for further applications. The present research found that the average PV cell temperature can be reduced by about 12 °C with a corresponding improvement in PCM melting duration of 127%. The addition of the metal foam is more effective at low solar radiation, ambient temperatures far below the PCM solidus temperature, and high wind speeds in nonlinear extension. With increasing of tilt angle, the PCM melting duration is linearly decreased by an average value of (13.4–25.0)% when the metal foam convective heat transfer coefficient is changed in the range of (0.5–20) W/m².K. The present research also shows that the PCM thickness has a positive linear effect on the PCM melting duration, however, modifying the metal foam configuration from 0.5 to 20 W/m².K has an effect on the PCM melting duration in such a way that the average PCM melting duration is doubled. This confirms the effectiveness of the inclusion of metal foam in the PV/PCM system.

Tourism, as an industry, has become one of the most dynamic sectors of the world economy these days and has specific features that are different from other industries. In the tourism industry, production and consumption points occur spatially at the same time. In addition, the tourism industry contributes to the economic growth of developed regions and can simultaneously distribute the wealth created geographically. It is notable that the coronavirus disease 2019 (COVID-19) pandemic has caused many challenges in the tourism industry regarding the presence of tourists in tourism centres and the closing of all tourism service chains, including food, entertainment, transportation and travel services worldwide. Tourism-related businesses, which are considered as invisible export and one of the engines of development and occupation, have been rendered obsolete. In other words, the businesses, as well as multiple units and activities in the related chain, have been damaged and employees of this industry have lost their jobs. This has led to the recession and regressive course of the developing and large industry of tourism in the world. It is worth noting that the tourism industry includes various sections, the most important of which is religious tourism. All religions in the world have different religious places, works, traditions and customs, which have become amongst the most important tourist attractions. Meanwhile, Muslims and the religion of Islam play a significant role in this branch of tourism. The Hajj, pilgrimage to holy places and the existence of mourning ceremonies or religious celebrations of Muslims are amongst the largest religious tourism events in the world. Given the importance of this issue, the present study aimed to evaluate the impact of the COVID-19 pandemic on religious tourism in Iraq in 2021. This field study was conducted on 4500 Muslim managers and staff of restaurants, hotels, grocery stores, clothing stores and souvenir shops around the holy shrines of imams and religious places in Karbala, Najaf, Kufa, Samarra and Kazemi. According to the results, the tourism of Iraq, which is mainly limited to Muslim religious sites in several major Iraqi cities, has also seen a decline in the number of religious tourists. The negative effects of COVID-19 on religious tourism have also been proved statistically by the Statistical Package for the Social Sciences (SPSS), as µ ≥ 3 has been counted in all indices.Contribution: Our findings offered new insights into the impact of COVID-19 on tourism, based on statistical analysis. In this study, the authors showed how COVID-19 affects various aspects of religious tourism, which has not been addressed in previous researches.

Pressure fluctuations are a critical phenomenon that can endanger the safety and stability of hydraulic structures, especially stilling basins. Hence, the accurate estimation of the dimensionless coefficient of pressure fluctuations () is critical for hydraulic engineers. This study proposed predictive soft computing models to estimate on sloping channels. Therefore, three robust soft computing methods, including extreme learning machine (ELM), group method data of handling (GMDH), and M5 model tree (M5MT), were used to estimate . The results revealed that ELM was more accurate than GMDH and M5MT methods when comparing statistical indices, including correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), index agreement (I_a), and BIAS values. The performance of ELM was found to be more accurate (CC = 0.9183, RMSE = 0.0067, MAE = 0.0051, SI = 11.88%, Ia = 0.9569) when compared with the results of GMDH (CC = 0.8818, RMSE = 0.0078, MAE = 0.0058, SI = 13.89%, Ia = 0.9361) and M5MT (CC = 0.6883, RMSE = 0.0120, MAE = 0.0090, SI = 21.28%, Ia = 0.7905) in the testing stage. In addition, the BIAS values revealed that ELM slightly overestimated the values of , especially at the peak point compared with GMDH and M5MT results. Overall, the suggested soft computing techniques worked well for predicting pressure fluctuation changes in the hydraulic jump.