Proton exchange membrane fuel cells (PEMFCs) as power systems have been widely studied in various application fields because of advantages such as cleanness and high efficiency with great progress having been made in the past decades both technologically and fundamentally. Despite the many promising developments however, technical challenges remain in terms of performance and lifespans. This is because PEMFCs are complex systems composed of various components and factors such as material property, engineering design and operating conditions can interact with each other to affect lifespans and performance. To fully understand the coupling effects of different factors on the overall performance and durability of PEMFCs, this review will comprehensively present existing research based on four aspects, including fuel cell stacks, subsystems, system integration and control strategy optimizations. First, this review will outline fuel cell stacks with their multi-physics modeling and engineering design to provide an understanding of the operating mechanisms inside PEMFC reactors. Following this, the progress of research into the structure and function of each subsystem is summarized and integration schemes for different applications are briefly presented. Finally, various control strategies for individual PEMFC subsystems to optimize energy management and dynamic performance are discussed. 
The Proton exchange membrane fuel cell (PEMFC) has been widely studied in various application fields because of its advantages of cleanness and high efficiency as a power system. Great progress has been made in the past decades on both technological and fundamental research of PEMFCs. Despite many promising developments, some technical challenges in performance and lifetimes remain to be overcome. The PEMFC is rather a complex system. It is composed of various parts, and factors like material properties, engineering designs and operating conditions are often interacted with each other and therefore affect its lifetime and performance. In order to fully understand the coupling effects of different factors on the overall performance and durability, this work comprehensively reviews the existing research work from four aspects: the fuel cell stack, the subsystem, the system integration and the optimizing control strategy. Firstly, the fuel cell stack with its multi-physics modeling and engineering design is outlined aimed to understand the operating mechanism inside the reactor. Then the progress of research on the structure and functions of each subsystem is summarized. Furthermore, the integration schemes for different applications are briefly presented. Lastly, various control strategies of each subsystem are introduced in order to optimize the PEMFC’s energy management and its dynamic performance.

Review of System Integration and Control of Proton Exchange Membrane Fuel Cells

With the fast growth of the global economy, energy supply and demand have a strong impact on social, economic, and environmental aspects. As a consequence, this has pushed the decision-makers to formulate objectives, guiding economic policies toward sustainable goals. The process is known as Sustainable Development Goals (SDGs) that have been proposed by the United Nations. This being said, the energy sector is a vital domain with a vast potential for improvments in terms of technologies and ligistalations. Solar energy is among the most efficient solutions proposed to reduce the economic and environmental footprints of energy. In this frame, the current paper aims to localize solar energy within SDGs and analyze the contribution of the solar energy towards the achievement of the SDGs. Moreover, the current work highlights the contributions of Mohammed bin Rashid Al Maktoum (MBR) Solar Park in the United Arab Emirates to achieving the SDGs. Indeed, the MBR Solar Park concept offers valuable insights of environmental impacts by deploying clean and affordable energy sources in place of conventional fossil fuel power plants that are still heavily used in the region. The MBR Solar Park operation has already mitigated 6.5 million tonnes of carbon dioxide equivalent and this number will likely rise when all phases are installed and operational. Moreover, it has been shown that MBR Solar Park achieve several SDGs such SDG 8: decent work and economic growth, SDG 9: industry, innovation and infrastructure, SDG 11: sustainable cities and communities, and SDG 15: life on land.

On the contribution of solar energy to sustainable developments goals: Case study on Mohammed bin Rashid Al Maktoum Solar Park

A dual-loop antenna design for hepta-band WWAN/LTE metal-rimmed smartphone applications

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.

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Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries

Osteosarcoma is a primary malignant tumor. It is difficult to cure and expensive to treat. Generally, diagnosis is made by analyzing MRI images of patients. In the process of clinical diagnosis, the mainstream method is the still time-consuming and laborious manual screening. Modern computer image segmentation technology can realize the automatic processing of the original image of osteosarcoma and assist doctors in diagnosis. However, to achieve a better effect of segmentation, the complexity of the model is relatively high, and the hardware conditions in developing countries are limited, so it is difficult to use it directly. Based on this situation, we propose an osteosarcoma aided segmentation method based on a guided aggregated bilateral network (OSGABN), which improves the segmentation accuracy of the model and greatly reduces the parameter scale, effectively alleviating the above problems. The fast bilateral segmentation network (FaBiNet) is used to segment images. It is a high-precision model with a detail branch that captures low-level information and a lightweight semantic branch that captures high-level semantic context. We used more than 80,000 osteosarcoma MRI images from three hospitals in China for detection, and the results showed that our model can achieve an accuracy of around 0.95 and a params of 2.33 M.

/pdf/osteosarcoma-mri-image-assisted-segmentation-system-base-on-250y83ag.pdf

Osteosarcoma MRI Image-Assisted Segmentation System Base on Guided Aggregated Bilateral Network

Segmentation of osteosarcoma in MRI images by K-means clustering, Chan-Vese segmentation, and iterative Gaussian filtering

Brain tumors are a major health problem that affect the lives of many people. These tumors are classified as benign or cancerous. The latter can be fatal if not properly diagnosed and treated. Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patient's full recovery after treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.

/pdf/detection-and-localization-of-early-stage-multiple-brain-2ztsfutl48.pdf

Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting.

Human brain tumor is a major health issue that may cause severe disabilities to a patient, ranging from loss of sensory feelings and paralysis to death. The accurate diagnosis of brain tumors that includes detection and localization is an essential procedure for effective treatment planning. Despite the fact that many brain tumor segmentation techniques have been developed in the past, effective tumor segmentation is still challenging because of the complex nature of the brain MRI image. It is difficult to differentiate between the tumor, which appears in the MRI image as a bright structure, and other bright structures that are not necessarily tumors. To address this problem, we propose a novel effective tumor segmentation method for brain MRI images. This technique is based on multiple threshold and object counting technique that can accurately detect the position of the tumor by being able to eliminate other similar structures.

MRI Tumor Detection and Localization by Multiple Threshold Object Counting Technique

Dust is one of the significant constraints in utilizing solar photovoltaic systems under harsh weather conditions in the desert regions due to creating a shadow that blocks solar irradiance from reaching solar cells and consequently, significantly reducing their efficiency. In this research, experimental study was performed to comprehend the nature of dust particles and their impact on the electrical power output that is generated from azimuth tracking solar PV modules under Sharjah environmental conditions in winter season. According to laboratory experiments, the power losses are linearly related to the dust accumulated density on the surface of the solar panel with a slope of 1.27% per g/m2. The conducted Outdoor studies revealed that the absolute reduction in output power increased by 8.46% after 41 continuous days with one low-intensity rainy day. The linear relationship obtained from indoor experiments was applied later to estimate the dust deposited density on the outdoor setup. The results showed that a regular cleaning process every two weeks is recommended to maintain the performance and to avoid the soiling loss. This work will help engineers in the solar PV plants to forecast the dust impact and figure out the regularity of the cleaning process in case of single axis tracking systems.

/pdf/experimental-study-of-the-impact-of-dust-on-azimuth-tracking-3ly3ue5c9o.pdf

Experimental study of the impact of dust on azimuth tracking solar PV in Sharjah

An electric boat hybrid power system design is proposed. The system uses a wind turbine that is driving an induction generator as the first renewable energy power source, solar PV panels with Maximum Power Point Tracking (MPPT) system as the second renewable energy power source, and polymer electrolyte membrane (PEM) fuel cell for the third renewable energy power source. The hybrid power system is used to drive the AC motor for the electric boat. Circuit breaker blocks are used to switch between using wind turbine and other renewable sources to operate the boat based on wind speed. The proposed system which has three elements is simulated using SIMULINK. The simulation results will be demonstrated to show the continuous operation of the electric boat regardless the changes in weather conditions such as solar irradiance and wind speed.

Waleed Obaid

Papers

Segmentation of osteosarcoma in MRI images by K-means clustering, Chan-Vese segmentation, and iterative Gaussian filtering

Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting.

MRI Tumor Detection and Localization by Multiple Threshold Object Counting Technique

Experimental study of the impact of dust on azimuth tracking solar PV in Sharjah

Wind-Fuel-Cell-Solar Hybrid Electric Boat Power Design with MPPT System