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Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.

Partial shading conditions (PSCs) can significantly reduce the output energy produced by photovoltaic (PV) systems. Moreover, when such conditions occur, conventional and advanced maximum power point tracking (MPPT) systems fail to operate the PV system at its peak because the bypassing diodes may cause the PV system to become trapped at a low power point when they are in conduction mode. The PV system can be operated at the global maximum power point (MPP) with the help of global peak searching tools. However, the frequent use of these tools will reduce the output of PV systems since they force the PV system to operate outside its power region while scanning the I-V curve in order to determine the global MPP. Thus, the global peak searching tools should be deployed only when a PSC occurs. In this paper, a simple and accurate method is proposed for detecting PSCs by means of monitoring the sign of voltage changes (positive or negative). The method predicts a PSC if the sign of successive voltage changes is the same for a certain number of successive changes. The proposed method was tested on two types of PV array configurations (series and series–parallel) with several shading patterns emulated on-site. The proposed method correctly and timely identified all emulated shading patterns. It can be used to trigger the global MPP searching techniques for improving the PV system’s output under PSCs; furthermore, it can be used to notify the PV system’s operator of the occurrence of PSCs.

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman’s renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman’s unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman’s national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman’s context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges.

Affordable and clean energy for any rural community is crucial for the sustainable development of the community and the nation at large. The utilization of diesel-based power generation is one of the barriers to the sustainable development of these communities. Such generations require fuel that has a volatile market price and emits massive greenhouse gas emissions. This paper presents the design, modeling, and simulation of a hybrid power system for a rural area in the Sultanate of Oman that aims to reduce daily consumption of diesel fuel and greenhouse gas emissions. Hybrid Optimization of Multiple Energy Resources (HOMER) is utilized to model multiple energy mix hybrid systems and to propose the best optimal energy mix system for a selected community. In addition, Electrical Transient Analyzer Program (ETAP) software is employed to assess hybrid system operational performances, such as bus voltage profiles and active and reactive power losses. This study revealed that the PV–wind–diesel system is the optimal energy mix hybrid microgrid for the Al-Dhafrat rural area in Oman, with a net present cost of USD 14.09 million. Compared to the currently operating diesel-based system, the deployment of this microgrid can reduce the levelized cost of energy, diesel fuel consumption, and greenhouse gas emissions per year by 54.56%, 70.44%, and 70.40%, respectively. This study confirms that the Sultanate of Oman has a substantial opportunity to install a hybrid microgrid system for rural diesel-based communities to achieve sustainable development in the country.

Chronic rhinosinusitis (CRS) is defined as the inflammation of nose and paranasal sinuses, affecting the patients' quality of life and productivity. Chronic rhinosinusitis with nasal polyps (CRSwNP) is a principal clinical entity confirmed by the existence of chronic sinonasal inflammation and is characterized by anterior or posterior rhinorrhea, nasal congestion, hyposmia and/or facial pressure or facial pain. Several epidemiologic studies have revealed wide variations in the incidence of CRS among regions globally ranging from 4.6% to 12%. The Gulf countries are also witnessing an unprecedented burden of CRSwNP. According to the current clinical guidelines, glucocorticosteroids and antibiotics are the principal pharmacotherapeutic approaches. Endoscopic sinus surgery is recommended for those who have failed maximal pharmacotherapy. Recently, biologics are considered as an alternative best approach due to the complications associated with medical therapy and surgery. However, precise data on the clinical position of biologic agents in the management of CRSwNP in the Gulf region is not available. The present review article addresses the current diagnostic and management approaches for CRSwNP and also emphasizes the role of emerging biologics in the current treatment strategies for CRSwNP in the Gulf region. Further, a consensus protocol was convened to rationalize the guideline recommendations, strategize the best practices with biologics, and develop clinical practice guidelines for all primary-care specialists in the Gulf region. The consensus-based report will be a useful reference tool for primary-care physicians in primary-healthcare settings, regarding the appropriate time for the initiation of biological treatment in the Gulf region.

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN’s powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant’s running expenses.

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases 1 , 2 . However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs, a phenomenon termed microsatellite instability; however, larger repeat expansions have not been systematically analysed in cancer 3 – 8 . Here we identified TR expansions in 2,622 cancer genomes spanning 29 cancer types. In seven cancer types, we found 160 recurrent repeat expansions (rREs), most of which (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with enrichment near candidate cis -regulatory elements, suggesting a potential role in gene regulation. One rRE, a GAAA-repeat expansion, located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, in preliminary experiments, treating cells that harbour this rRE with a GAAA-targeting molecule led to a dose-dependent decrease in cell proliferation. Overall, our results suggest that rREs may be an important but unexplored source of genetic variation in human cancer, and we provide a comprehensive catalogue for further study. An atlas explores the landscape of recurrent repeat expansions in human cancer genomes.