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A field experiment was carried out in one of the fields designated for the Department of Field Crops, College of Agriculture, University of Karbala for the spring season 2019-2020 in a soil with a silty loam to study the effect of Boron spraying on growth and yield of bean. The experiment included studying two factors. First factor included boron concentration (0, 100 and 200) mg L-1. Second factor using three cultivars of bean (local, Dutch and Spanish). The experiment was designed as RCBD. The result showed the concentrations of boron was significantly increased in Average weight rate 50 seed (g) , Average pods weight (g plant-1), Average yield seeds(g.plant-1) and the average yield biological (g.plant-1). Also affected varieties beans significant effect on the chlorophyll content (%) in leaves, Average weight rate 50 seed (g), Average pods weight (g.plant1 ).As shown the interaction between the concentrations of boron and varieties beans was significantly increased Chlorophyll content (%) in leaves, Average weight rate 50 seed (g), Average pods weight (g.plant-1), Average yield seeds (g.plant-1) and the average yield biological (g.plant-1).

The weighting factor adjustment limits the extensive application of conventional model predictive torque control in practice. In this paper, a model predictive torque control strategy with gravitat...

Limited to the power of the light source in ophthalmic optical coherence tomography (OCT), the signal-to-noise ratio (SNR) of the reconstructed images is usually lower than OCT used in other fields. As a result, improvement of the SNR is required. The traditional method is averaging several images at the same lateral position. However, the image registration average costs too much time, which limits its real-time imaging application. In response to this problem, graphics processing unit (GPU)-side kernel functions are applied to accelerate the reconstruction of the OCT signals in this paper. The SNR of the images reconstructed from different numbers of A-scans and B-scans were compared. The results demonstrated that: 1) There is no need to realize the axial registration with every A-scan. The number of the A-scans used to realize axial registration is suitable to set as ∼25, when the A-line speed was set as ∼12.5kHz. 2) On the basis of ensuring the quality of the reconstructed images, the GPU can achieve 43× speedup compared with CPU.

The recent online business blossom has stimulated the soaring demand for logistics service, or rather, courier service, which emerged as a robust momentum of economic growth worldwide. So, to satisfy the fast-changing online businesses, courier services have been urged to be more creative to ensure the desired efficient movement of goods and services along the supply chain, especially in logistics and parcel deliveries. How could the courier service be more creative? What makes courier service creative has not yet been fully explored. Therefore, this study aimed to unveil the antecedents of creativity of courier services from the perspective of managing knowledge which is regarded as the source of wisdom. Taking into consideration the logistical nature of courier service, the proposed framework integrates logistics into knowledge management, and focuses on empirically detecting its relationship with creativity in the context of Malaysia. The results prove that the logistics-related knowledge management (LRKM) affects creativity in a significant and positive manner. However, amongst the four select constructs, only the logistics-related knowledge dissemination (LRKD) followed by the logistics-related knowledge responsiveness (LRKR) are significant operational contributors in the development of creativity in courier services. The logistics-related knowledge generation (LRKG) and logistics-related shared interpretation (LRKS) have not yet appeared significant, which is mainly due to the current insufficient development of the courier service in Malaysia. It indicates that to further enhance creativity, courier service providers should attach importance on managing knowledge of logistics operation, especially in the respects of dissemination and responsiveness, while more efforts shall be exerted to the generation and shared interpretation as both are still having positive potentials towards creativity. This study is amongst the pioneers to cross-extend the theory of logistics management and knowledge management into the courier service industry, and it also expands the application of the theory of creativity into the courier service industry. Future study could further examine the inter-relationships among the detected constructs, the effects of creativity of courier service on the performance of e-business firms, and so on.

The microgrid power plant contains a large number of power electronic converters, which inevitably produce power quality problems such as harmonic noise and voltage fluctuations. Harmonics noise is...

Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions.

This work aims to provide a state-of-the-art review of the performance of combined cycle power plant (CCPP) based on several proposed inlet air cooling systems. Investigators strive to meet the significant need to promote and develop inlet air cooling technologies to recover heat from the wasted energy in the exhaust gasses of the CCPP and diminish the environmental impacts. Various types of cooling systems mainly offer a boost for electric power generation during the peak load hours. The output power of the CCPP directly depends on the mass flow rate of air that flows through the air compressor. Therefore, during extremely hot weather conditions, subsequently, the air density drops, and this leads to a drastic decrease in the power output. This paper reviews available studies investigated the impacts of inlet air cooling systems on the performance of the CCPP. The fogging cooling system contributed by up to 17% in improving the total performance of the CCPP; however, with the use of the evaporative cooling, the performance was enhanced by only 4%. The energy consumption of mechanical chiller compared to that of evaporative cooling is high due to the effectiveness of the evaporative cooler which depends on the humidity of inlet airflow. Further, the mechanical cooling system can provide the CCPP with a cooling effect for around 7-hour on-peak periods. This method increases the gain of the CCPP performance by 13.6%. Ultimately, the CCPP equipped with an absorption chiller demonstrates the best solution to increase the performance by up to 23%.

This study aims to study the discharging process to verify the influence of geometry modifications and heat transfer flow (HTF) patterns on the performance of a vertical triplex-tube latent heat container. The phase change material (PCM) is included in the middle tube, where the geometry is modified using single or multi-internal frustum tubes instead of straight tubes to enhance the discharging rate. The effects of the HTF flow direction, which is considered by the gravity and opposite-gravity directions, are also examined in four different cases. For the optimal geometry, three scenarios are proposed, i.e., employing a frustum tube for the middle tube, for the inner tube, and at last for both the inner and middle tubes. The effects of various gap widths in the modified geometries are investigated. The results show the advantages of using frustum tubes in increasing the discharging rate and reducing the solidification time compared with that of the straight tube unit due to the higher natural convection effect by proper utilization of frustum tubes. The study of the HTF pattern shows that where the HTF direction in both the inner and outer tubes are in the gravity direction, the maximum discharging rate can be achieved. For the best configuration, the discharge time is reduced negligibly compared with that for the system with straight tubes which depends on the dimensions of the PCM domain.

Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In welldeveloped optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular “fish massage session.” A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.

Since the World Health Organization (WHO) declared the outbreak of severe acute respiratory syndrome COVID-19 virus 2 (COVID-19) virus disease 2 (SARS-CoV-2) on 9 January 2020, the entire world has been exceptionally interested in examining the impact of this pandemic on people and the environment. The pandemic led to unprecedented measures to halt air traffic and close factories due to lockdowns, economic closures, and the stopping of transportation of all kinds. The decline in the use of coal by power plants, oil refining, and steel manufacturing had a beneficial effect on air pollution and caused a decrease in carbon dioxide emissions. Moreover, the concept of sustainability has become more prevalent, reflecting the increasing awareness of the responsibility placed on every member of society. Sustainability is the quality and quantity of change that meets our needs without destroying the giving planet, which is the hope for the survival of future generations. We summarized and discussed the studies and research documenting these effects on the environment and health worldwide to come up with objective conclusions, and to draw some recommendations and concepts about the importance of sustainability. The significance of this article lies in that it aims to briefly review some of the positive and negative impacts observed and reported during the SARS-CoV-2 pandemic on health and the planet’s environment for the duration of April 2020–October 2022, and finally discuss the challenges and prospects to endorse planet sustainability. While COVID-19 had many beneficial effects on the planet’s recovery, there were also profound effects on health due to the disease itself. Government and policymakers must take measures to prevent this environmental healing process from being transient.

Based on ultraviolet absorption spectroscopy technology combined with stoichiometry, a portable photoelectric detection system with wireless transmission was designed with the advantages of simple operation, low cost, and quick response to realize the non-destructive detection of dihydrocoumarin content in coconut juice. Through the detection of a sample solution, the light intensity through the solution is measured and converted into absorbance. Particle swarm optimization (PSO) is applied to optimize support vector regression (SVR) to establish a corresponding concentration prediction model. At the same time, in order to solve the shortcomings of the conventional portable photoelectric detection equipment in data storage, data transmission, and other aspects, based on the optimal PSO-SVR model, we used Python language to develop a friendly graphical user interface (GUI), integrating data collection, storage, analysis, and prediction modeling in one, greatly simplifying the operation process. The experimental results show that, compared with the traditional methods, the system achieves the purpose of rapid and non-destructive detection and has a small gap compared with the detection results of the ultraviolet spectrophotometer. It provides a good method for the determination of dihydrocoumarin in coconut juice.

Energy is the source of economic growth, and energy consumption indicates the country's state of development. Energy engineering is a relatively new technical discipline. It is increasingly considered as a significant step in meeting carbon reduction targets, which can produce a variety of appealing outcomes that are useful to humanity's evolution. Many countries have adopted national policies to decrease pollution by reducing fossil fuel use and increasing renewable energy usage by alleviating climate change (wind and solar, etc.). The ever-growing need for renewable sources has led to economic and technological problems, such as wind energy, essential for effective grid control, and the design of a wind project. Precise estimates offer network operators and power system designers vital information for the generation of an appropriate wind turbine and controlling demand and supply power. This work provides an in-depth study of the proliferation of artificial intelligence (AI) in the prediction of wind energy generation. The devices employed to calculate wind speed are examined and discussed, with a focus on studies recently published. This review's findings show that AI is being employed in power wind energy measurement and forecasts. When compared to individual systems, the hybrid AI system gives more accurate findings. The discussion also found that correct handling and calibration of the anemometer can increase predicting accuracy. This conclusion suggests that increasing the accuracy of wind forecasting can be accomplished by lowering equipment errors that measure the meteorological parameter and mitigate carbon emission.

Energy sources, such as wind, solar, hydro, etc., are an important source of energy, and wind power generators are important energy conversion machines. The doubly fed induction generator has wide industrial and commercial applications due to its superior performance, combined with fault and eco-friendly properties. However, the fault current dynamics of wind farms identify the potential impacts of fault currents and the voltage on the protection. This study focuses on wind farms (employing doubly fed induction generators) perturbation during symmetrical (three-phase) symmetrical short circuit (SSC) at different points. The detail of analyzing the doubly fed induction generator (DFIG) performance during transient conditions, control and modeling is studied in this paper. These two points are selected as grid-side converter and point of common coupling, respectively. These comparison results fetched the more precise understanding of the fault diagnosis reliability with reduced complexity, stability, and optimization of the system. The present findings illustrated the main difference between point of common coupling and grid-side converter under SSC faults and the robustness of these two mentioned points.

In this study, the engine performance and emissions of gasoline were examined by applying a response surface methodology (RSM) optimisation approach. Fusel oil–gasoline blends were used to operate an engine at various speeds and loads. The optimal fusel oil–gasoline blend mix ratio was determined to minimise fuel consumption and nitrogen oxide and hydrocarbon emissions and to maximise the brake power (BP). The results demonstrate that the engine load and speed have a significant effect on performance and emissions. In addition, the blended fuels (F10 and F20) were shown to reduce NOx emissions. Furthermore, insignificant effects on engine performance were observed for fusel oil compared with pure gasoline. The design of experiments (DoE) method, which is a statistical technique, indicated that F20 was the optimum blend ratio among the three studied fuels, based on the RSM. The optimal parameters were a load corresponding to 60% of the wide open throttle engine load and an engine speed of 4500 rpm for the F20 blend, resulting in a high desirability value of 0.852 for the test engine, with values of 67.6 kW, 235.17 g/kW.h, 0.118%vol, and 1931.4 ppm for the BP, brake-specific fuel consumption, CO emission, and NOx emission, respectively.

Eddy current testing technique is being utilized in engineering, such as in nuclear steam pipe, aircraft and gas/oil pipeline, due to its sensitivity to small cracks and subsurface defects, immediate results, environmental friendliness, and use in examining complex sizes and shapes of substances. However, the lift-off noise due to irregular inspected materials surface, varying coating thicknesses, or movement of transducers extremely limits the implementation of eddy current testing in a non-disastrous testing which impacts the measure of defect depth on the conductive material. In this paper, a study on hybrid giant magneto-resistance/infrared probe is proposed to minimize the influence of lift-off for detecting the depth defect. The giant magneto-resistance reads the magnetic field which reflects any defect inside the pipeline, and infrared sensors read the movement of each giant magneto-resistance inside the pipeline. The error compensation technique depends on Mamdani fuzzy which examines the interaction that exists between the peak value of giant magneto-resistance and the infrared sensor signal. The eddy current testing inspection system includes details of the giant magneto-resistance–eddy current probe design and instrumentation of the error compensation technique. The measurement method is based on alternating current supply with 30 kHz frequency to ensure that the crack signals are clearly displayed. The proposed method is verified experimentally, and the result shows that the impact of lift-off noise is highly reduced in the eddy current testing technique and enhances the sensor accuracy. The depth defect error caused by 1 mm lift-off is reduced to 7.20%.

A metering roundabout where traffic is controlled by signals where phase times are influenced by queue detector occupancy may be the solution to reduce queue lengths under unbalanced traffic flows. In the past decades, a number of studies have attempted to evaluate the effectiveness of metering roundabout, especially on the dominant approach. Little studies, however, have been directed on prediction of the queuing lengths, which is essential to determine the detector locations. This paper introduces a queue length estimation model using adaptive neuro fuzzy inference system for unbalanced roundabout traffic flows. The adaptive neuro fuzzy inference system model consists of an input layer representing four parameters as arrival volumes, conflicting volumes, phase green and red time, and output layer with four neuron representing queuing length. MATLAB software and additional statistical tests were used as the tool to develop the models for the data. In order to conduct credible model validations, model output data were compared against the observed data collected using drones. The results from the analysis demonstrated that adaptive neuro fuzzy inference system model is able to estimate the queuing length at metering roundabouts. Thus, it is expected that the adaptive neuro fuzzy inference system model will help practitioners in determining optimal detector locations and will be a foundation research for roundabouts with signals.

Cycle to cycle variation of a spark ignition engine is analyzed utilising wavelet analysis technique based on the spectral-temporal approach to stabilizing high cyclic variations. The selected examples of previously published cyclic IMEP measurements from a DISI engine a different percentage of fusel oil blended with gasoline (FAWE10, FAWE20, FBWE10 and FBWE20) under 4500 rpm engine speed and 60% WOT are studied. The results reveal that the increase in spectral power is observed with increasing fusel oil up to 20% with both blends (FBWE20 and FAWE20). The indicates a pronounced influence of fusel oil ratio on the engine CCV of IMEP. However, the maximum peak in GWS observed for FBWE20 fuel blend with more uniform overall spectral power for FAWE20 fuel blend which indicates a noticeable enhancement in cyclic variations behaviour after water extraction. As an outcome, the fusel oil water content has acted negatively to restrict the combustion as the fusel oil increased as well as the cyclic variations affected despite the high oxygen content and octane number of fusel oil.

There are tremendous security concerns with patient health monitoring sensors in Internet of Things (IoT). The concerns are also realized by recent sophisticated security and privacy attacks, including data breaching, data integrity, and data collusion. Conventional solutions often offer security to patients’ health monitoring data during the communication. However, they often fail to deal with complicated attacks at the time of data conversion into cipher and after the cipher transmission. In this paper, we first study privacy and security concerns with healthcare data acquisition and then transmission. Then, we propose a secure data collection scheme for IoT-based healthcare system named SecureData with the aim to tackle security concerns similar to the above. SecureData scheme is composed of four layers: 1) IoT network sensors/devices; 2) Fog layers; 3) cloud computing layer; and 4) healthcare provider layer. We mainly contribute to the first three layers. For the first two layers, SecureData includes two techniques: 1) light-weight field programmable gate array (FPGA) hardware-based cipher algorithm and 2) secret cipher share algorithm. We study KATAN algorithm and we implement and optimize it on the FPGA hardware platform, while we use the idea of secret cipher sharing technique to protect patients’ data privacy. At the cloud computing layer, we apply a distributed database technique that includes a number of cloud data servers to guarantee patients’ personal data privacy at the cloud computing layer. The performance of SecureData is validated through simulations with FPGA in terms of hardware frequency rate, energy cost, and computation time of all the algorithms and the results show that SecureData can be efficient when applying for protecting security risks in IoT-based healthcare.

The safe and efficient operation of the integrated energy system is severely hampered by a number of unpredictable elements, such as the output of renewable energy sources, the cost of energy purchases, and full demand response (IES). The effectiveness and excellence of the integrated energy system scheduling method can be increased with advanced modeling of unpredictable aspects. Thus, the IES robust stochastic optimisation model is constructed and solved with the hybrid shuffled frog-leaping and local search (HSFLA–LS) algorithm. Finally, a simulation analysis considering the uncertainty of energy purchase price based on the hybrid SFLA–LS algorithm is reduced by USD 1.63 (0.64%) and USD 3.34 (1.3%), compared to PSO and GA, respectively. In addition, the time taken to execute the SFLA–LS algorithm for the program is reduced by 1.886 s (1.59%), and 3.117 s (2.7%), compared to PSO and GA, respectively. The findings demonstrate that the suggested approach can lower system running expenses, and achieve the coordination and optimization of economy and robustness.

To reduce the allocation of energy storage capacity in wind farms and improve economic benefits, this study is focused on the virtual synchronous generator (synchronverter) technology. A system accompanied by wind power, energy storage, a synchronous generator and load is presented in detail. A brief description of the virtual synchronous generator control strategy is given. The capacity allocation is based on different optimization goals and the optimal energy storage capacity configuration of the coordinated frequency modulation (FM) control strategy. The detail of the dual-loop control strategy is carried out by establishing the grid-connected transfer function model of the synchronverter energy storage and a theoretical model of life cycle cost is established. The optimal control strategy of coordinated FM for wind storage is implemented using MATLAB software. The simulation showed that the proposed strategy provided the energy storage capacity at high wind speed, which is configured to be 5.9% of the installed capacity of the wind turbine, marking a reduction of 26% compared with the 8% capacity required for independent support. In addition, the proposed method has improved the energy storage capacity configuration of the coordinated FM control strategy.

Generating systems are known as adequately reliable when satisfying the load demand. Meanwhile, the efficiency of electrical systems is currently being more influenced by the growing adoption of the Wind/Solar energy in power systems compared to other conventional power sources. This paper proposed a new optimization approach called Metaheuristic Scanning Genetic Algorithm (MSGA) for the evaluation of the efficiency of power generating systems. The MSGA is based on a combination of metaheuristic scanning and Genetic Algorithm. The MSGA technique is used for evaluating the reliability and adequacy of generation systems integrated with wind/Solar energy is developed. The usefulness of the proposed algorithm was tested using Reliability Test System ‘IEEE-RTS-79’ which include both of wind power and solar power generation. The result approve the effectiveness of the proposed algorithm in improving the computation time by 85% and 2% in comparison with the particle swarm optimization (PSO) and differential evolution optimization algorithm (DEOA) respectively. In addition, the proposed model can be used to test the power capacity forecasting scheme of the hybrid power generation system with the wind, solar and storage.

Accurate measurement the depth of defect is essential to ensure the reliability and integrity of the pipe line structure and safety of the personnel workers in the oil and gas industry. This study proposes intelligent algorithm base on Fuzzy logic scheme to compensate the depth error measurement of GMR sensor on mild steel superficial crack. Intelligent rules in Fuzzy logic scheme allows the propose method to be effective on compensate lift off effect in between 1 and 4 mm. The Eddy current testing probe is design by utilize the sensitive of GMR sensor on magnetic field in order to improve the accuracy depth crack measurement on mild steel. The Arduino Mega 2650 was used as a data processing device for receiving and transmitting of signal through the available fifty-four digital pins (as input and output) and sixteen analogue pins. MATLAB is use as a platform to design and implementation the Fuzzy error compensation. The experiment results show the proposed sensor error compensation scheme able to reduce the error measurement up to 23%.

Non-Destructing Testing (NDT) is crucial in the oil and gas industry to avoid fatal pipeline accidents and alarming property damage. Its general applications include the detection of pipeline defects in nuclear power plants, steam generator tubing, aircraft etc. Meanwhile, the Piezoelectric with Lamb Wave method testing system (PZ-LW), also known as Long Range Ultrasonic Testing (LRUT) is one of the methods in NDT used for crack detection on long-distance surface area. The current research proposed the ultrasonic testing probe design for the ideal gap between Piezo sensor resulting in reflection signal optimization for Piezoelectric sensor array in carbon steel pipe inspection. Response Surface Methodology (RSM) used the signal wave frequency excitation and the distance between the sensors array were optimized for appropriate multiple defect detection on 60 mm diameter carbon steel pipe sample. An Artificial Axial and Hole defect was utilized to test its efficiency. The analysis of the experimental results suggests the accuracy of PZ-LW system to amount to 98.55% when determining the defect location (hole defect), above 96.05% (axial defect measurement), while 98% and beyond (identification of defect shape). The inspection position is achievable until 311,399 m on free damaged pipe, 64,377 m (axial damage) and 61,267 m (hole crack defect damage). The reflection signal on PZ-LW inspection indicated good feedback signal amplitude with high distance detection in axial crack defect. The comparison of experimental and simulation results in Simulation of Non-Destructed Testing (SIMNDT) has successfully proven the consistency and accuracy of the proposed PZ-LW system for carbon steel pipe inspection.

In an attempt to meet the global demand, renewable energy systems (RES) have gained an interest in it due to the availability of the resources, especially solar photovoltaic system that has been an importance since many years because of per watt cost reduction, improvement in efficiency, and abundant availability. Photovoltaic system in remote and rural areas is very useful where a grid supply is unavailable. In this scenario, power electronic converters are an integral part of the renewable energy systems particularly for electronic devices which are operated from renewable energy sources and energy storage system (fuel cell and batteries). In this article, a new topology of charge pump capacitor (CPC) which is based on high voltage gain technique DC-DC boost converter (DCBC) with dynamic modeling is proposed. To testify the efficacy of the introduced topology, a prototype has been developed in a laboratory, where input was given 10VDC and 80VDC output voltage achieved at the load side. Furthermore, the experimental result shows that the voltage stress of MOSFET switches is very less in comparison with the conventional boost converter with the same parameters as the proposed converter.

The advancement of using the cryogenic energy storage (CES) system has enabled efficient utilization of abandoned wind and solar energy, and the system can be dispatched in the peak hours of regional power load demand to release energy. It can fill the demand gap, which is conducive to the peak regulation of the power system and can further promote the rapid development of new energy. This study optimizes the various types of energy complementary to the CES system using hybrid gravitational search algorithm-local search optimization (hGSA-LS). First, the mathematical model of the energy storage system (ESS) including the CES system is briefly described. Second, an economic scheduling optimization model of the IES is constructed by minimizing the operating cost of the system. Third, the hGSA-LS methods to solve the optimization problem are proposed. Simulations show that the hGSA-LS methodology is more efficient. The simulation results verify the feasibility of CES compared with traditional systems in terms of economic benefits, new energy consumption rate, primary energy saving rate, and carbon emissions under different fluctuations in energy prices. Optimization of the system operation using the proposed hGSA-LS algorithm takes 5.87 s; however, the GA, PSO, and GSA require 12.56, 10.33, and 7.95 s, respectively. Thus, the hGSA-LS algorithm shows a comparatively better performance than GA, PSO, and GSA in terms of time.

The complexity of the knowledge structure in the clinical cases, involving a wide range of attributes, results in making its case similarity calculation more complex. The existing medical ontologies, due to different expressions of the same concepts in computer information retrieval, causes difficulties in terms of sharing useful information in different database systems. This paper constructs a new decision support system based on KNN/ontology method was proposed. The detail of the methods and processes of common clinical case knowledge acquisition in combination with the method of obtaining structured information has been presented. The clinical case data similarity calculation method based on various types such as symptom information, medical history information, complications, surgical information, diagnostic results and other information, for record of a clinical diagnosis and treatment process. The validity of the similarity calculation method and the weight calculation method is verified by the clinical case data. The proposed methods can be effective for improving the quality and level of clinical services for medical service organizations.

An increase in demand for renewable energy resources, energy storage technologies, and electric vehicles requires high-power level DC-DC converters. The DC-DC converter that is suitable for high-power conversion applications (i.e., resonant, full-bridge or the dual-active bridge) requires magnetic transformer coupling between input and output stage. However, transformer design in these converters remains a challenging problem, with several non-linear scaling issues that need to be simultaneously optimized to reduce losses and maintain acceptable performance. In this paper, a new transformer-less high step-up boost converter with a charge pump capacitorand capacitor-inductor-diode CLD cell is proposed using dynamic modeling. The experimental and simulation results of the proposed converter are carried out in a laboratory and through Matlab Simulink, where 10 V is given as an input voltage, and at the output, 100 V achieved in the proposed converter. A comparative analysis of the proposed converter has also been done with a conventional quadratic converter that has similar parameters. The results suggest that the proposed converter can obtain high voltage gain without operating at the maximum duty cycle and is more efficient than the conventional converter.

Wind energy is considered as one of the most prominent sources of energy for sustainable development. This technology is of interest owing to its capability to produce clean, eco-friendly, and cost-effective energy for small-scale users and rural areas where grid power availability is insufficient. Wind power generation has developed rapidly in the past decade and is expected to play a vital role in the economic development of countries. Therefore, studying dominant economic factors is crucial to properly approach public and private financing for this emerging technology, as industrial growth and energy demands may outpace further economic studies earlier than expected. In this study, a strategy-focused method for performing economic analysis on wind energy based on financial net present value, levelized cost of energy, internal rate of return, and investment recovery period is presented. Numerical and simulation results depict the most optimal and economical system from a 3 and a 10 kW wind energy conversion system (WECS). Moreover, the aforementioned criteria are used to determine which WECS range is the most suitable investment with the shortest payback period. Finally, an economically viable and profitable wind energy system is recommended.

Solar energy exploitation through photovoltaic technology has demonstrated a sustainable way for curbing energy needs and cutting environmental issues happening due to emissions of carbon dioxide, CO2 from the usage of non-renewable energy resources. The purpose of this study was to reduce metallization by choosing optimal metallization on silicon photovoltaic wafer. Using numerical study, the effects of the number of busbars, fingers, and soldering/probe points were analyzed and also the study of the size of busbar and finger was carried out to find the optimal value for each which assures better performance. It is revealed that increasing the number and size of busbars, fingers, and probe points result in increasing fill factor, however, the efficiency of the device is limited to a number which provides the best optimal performance in terms of efficiency, whereas increasing the size (width) of the parameter result in a decrease in efficiency increasing shading factor. The optimal value of prescribed parameters was recorded as 4, 82, and 20 numbers of busbars, fingers, and probe points, respectively, while an optimal value of the width of busbar and finger is found as 0.5 mm and 60 µm, respectively. These values attained efficiency and fill factor above 20% and 80%, respectively. This study finds a realistic method to further diminish the metallization, improve the performance, and reduce the cost of often used industrial silicon photovoltaic cells.

In this article, the adaptive neuro-fuzzy inference system (ANFIS) and multiconfiguration gas-turbines are used to predict the optimal gas-turbine operating parameters. The principle formulations of gas-turbine configurations with various operating conditions are introduced in detail. The effects of different parameters have been analyzed to select the optimum gas-turbine configuration. The adopted ANFIS model has five inputs, namely, isentropic turbine efficiency (Teff), isentropic compressor efficiency (Ceff), ambient temperature (T1), pressure ratio (rp), and turbine inlet temperature (TIT), as well as three outputs, fuel consumption, power output, and thermal efficiency. Both actual reported information, from Baiji Gas-Turbines of Iraq, and simulated data were utilized with the ANFIS model. The results show that, at an isentropic compressor efficiency of 100% and turbine inlet temperature of 1900 K, the peak thermal efficiency amounts to 63% and 375 MW of power resulted, which was the peak value of the power output. Furthermore, at an isentropic compressor efficiency of 100% and a pressure ratio of 30, a peak specific fuel consumption amount of 0.033 kg/kWh was obtained. The predicted results reveal that the proposed model determines the operating conditions that strongly influence the performance of the gas-turbine. In addition, the predicted results of the simulated regenerative gas-turbine (RGT) and ANFIS model were satisfactory compared to that of the foregoing Baiji Gas-Turbines.

Ethanol fuel is widely used as an alternative fuel in spark ignition engines. The use of ethanol reduces dependence on fuel from the fraction of hydrocarbons. As such, adding swirl generators to the intake manifold aims to make the internal airflow more turbulent. The influence of swirl on the performance, emissions, and combustion in a constant speed Spark Ignition (SI) engine was studied experimentally. Also, the effects of the addition of ethanol in the fuel on engine performance and emissions under development of the intake manifold with a swirl generator were determined. Thus, the study analyses the effects of adding a swirl generator to the intake manifold on engine performance, fuel consumption, and emissions produced. Influence of the swirl generator on the airflow and swirl pattern testing is done on the three types of swirl generators, which are the concave, flat bottom, and symmetrical with different angle positions. Engine experiment was performed at 2,000, 2,500, 3,000, 3,500, 4,000, 4,500, and 5,000 rpm engine speed at WOT using a concave swirl type generator. Response Surface method (RSM) was used to find the optimum intake manifold design according to response (flow and swirl) and independent variables (a type of swirl generators and angle positions). The concave swirl type generator at 6.53 angle position is considered to be optimum for this particular engine. The highest engine torque and BSFC were with a swirl generator intake manifold. The HC and CO emissions increased with intake manifold with a swirl generator. The CO2 emission reduced with swirl generator intake manifold by 2%, 3%, and 5% with gasoline E10 and E20 compared to the standard intake manifold. 2019, 2019 Taylor & Francis Group, LLC. Scopus

Eddy current testing plays an important role in numerous industries, particularly in material coating, nuclear and oil and gas. However, the eddy current testing technique still needs to focus on the details of probe structure and its application. This paper presents an overview of eddy current testing technique and the probe structure design factors that affect the accuracy of crack detection. The first part focuses on the development of different types of eddy current testing probes and their advantages and disadvantages. A review of previous studies that examined testing samples, eddy current testing probe structures and a review of factors contributing to eddy current signals is also presented. The second part mainly comprised an in-depth discussion of the lift-off effect with particular consideration of ensuring that defects are correctly measured, and the eddy current testing probes are optimized. Finally, a comprehensive review of previous studies on the application of intelligent eddy current testing crack detection in non destructive eddy current testing is presented.

Fusel oil produced in small quantities as a by-product obtained through the fermentation of some agricultural products. Thereby the possibility of using fusel oil to replace gasoline or blending at...

The arriving optimum improvement of a thermodynamic system of energy conversion such as a combined cycle power plant (CCPP) is complicated due to the existence of different factors. Energy and exergy analysis is utilized as effective methods to determine both the quantity and quality of the energy sources. This paper reviews the latest thermodynamics analysis on each system components of a CCPP independently and determine the exergy destruction of the plant. A few layouts of the CCPP plant from different locations considered as case studies. In fact, the most energy losses occurred in the condenser compared with the plant components. It found that in the combustion chamber (CC) the highest exergy destruction occurred. The ambient temperature causes an evident decrement in the power production by the gas turbine (GT). The result has proved that besides energy, exergy analysis is an efficient way to the assessment of the performance of the CCPP by recommending a more advantageous configuration of the CCPP plant, which would lead to reductions in fuel required and emissions of air pollutants.

In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production steadily. Thus, Small Hydropower Plant (SHP) includes a Kaplan turbine was verified to explore its applicability. This paper concentrates on applying on Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation to make performance predictions of the hydropower plant at the Himreen lake dam-Diyala in terms of net turbine head, flow rate of water and power production that data gathered during a research over a 10 year period. The model studies the uncertainties of inputs and output operation and there's a designing to network structure and then trained by means of the entire of 3570 experimental and observed data. Furthermore, ANN offers an analyzing and diagnosing instrument effectively to model performance of the nonlinear plant. The study suggests that the ANN may predict the performance of the plant with a correlation coefficient (R) between the variables of predicted and observed output that would be higher than 0.96. Keywords: Himreen Lake Dam, Small Hydropower plants, Artificial Neural Networks, Feed forward-back propagation model, Generation system's prediction

This paper investigates the effect of perforation shape or geometry on the heat transfer of perforated fins. The type of heat exchanger used is heat sink with the perforated fins under the forced convection heat transfer to determine the performance for each perforation shape between circular, rectangular, triangular and also with the non-perforated fins. The experimental result compared between the perforation shape and the heat transfer coefficient to clarify the best perforation shape for the plate heat sink. The fluid and heat transfer properties of plate fins or normally heat sink were studied experimentally and numerically using CFD. The difference between experimental and numerical results was reported to be about 8% and 9% for temperature distributions when the power supplied are 150 W and 100 W respectively. The highest temperature different of the fin are with the circular perforation shape which is 51.29% when compared the temperature at the tip of the fins with the temperature at the heat collector followed by the rectangular perforation shape with 45.57% then followed by the triangular perforation shape by 42.28% then lastly the non-perforated fins by 35.82%. The perforations of the fines show a significant effect on the performance of forced convection heat transfer.

Power systems employ measures of reliability indices to indicate the effectiveness a power system to perform its basic function of supplying electrical energy to its consumers. The amount of energy required in a generating system to ensure an adequate supply of electricity is determined using analytical and simulation techniques. This study focuses on reviewing the generation reliability assessment methods of power systems using Monte Carlo simulation (MCS) and variance reduction techniques (VRTs). MCS is a very flexible method for reliability assessment of the power systems, by the sequential process it can imitate the random nature of the system components and can be broadly classified into two, sequential and non-sequential simulations. A brief introduction to MCS is provided. Unlike analytical methods, MCS can be used to quantitatively estimate the system reliability in even the most complex system generating capacity situations. The major drawback of the MCS is that it requires more computational time to reach for converging with estimated the values of reliability indices. This paper presents an effective methods for accelerating MCS in power system reliability assessment. VRT used is to manipulate the way each sample of an MCS is defined in order to both preserve the randomness of the method and decrease the variance of the estimation. In addition, the study presents detailed descriptions of generation reliability assessment methods, in order to provide computationally efficient and precise methodologies based on the pattern simulation technique. These methodologies offer significantly improved computational ability during evaluations of power generation reliability.