Fast and automatic object detection in remote sensing images is a critical and challenging task for civilian and military applications. Recently, deep learning approaches were introduced to overcome the limitation of traditional object detection methods. In this paper, adaptive mask Region-based Convolutional Network (mask-RCNN) is utilized for multi-class object detection in remote sensing images. Transfer learning, data augmentation, and fine-tuning were adopted to overcome objects scale variability, small size, the density of objects, and the scarcity of annotated remote sensing image. Also, five optimization methods were investigated namely: Adaptive Moment Estimation (Adam), stochastic gradient decent (SGD), adaptive learning rate method (Adelta), Root Mean Square Propagation (RMSprop) and hybrid optimization. In hybrid optimization, the training process begins Adam then switches to SGD when appropriate and vice versa. Also, the behaviour of adaptive mask RCNN was compared to baseline deep object detection methods. Several experiments were conducted on the challenging NWPU-VHR-10 dataset. The hybrid method Adam_SGD acheived the highest Accuracy precision, with 95%. Experimental results showed detection performance in terms of accuracy and intersection over union (IOU) boost of performance up to 6%.
The rapid urbanization and industrialization is causing worldwide water pollution, calling for advanced cleaning methods. For instance, pollutant adsorption on magnetic oxides is efficient and very practical due to the easy separation from solutions by an magnetic field. Here we review the synthesis and performance of magnetic oxides such as iron oxides, spinel ferrites, and perovskite oxides for water remediation. We present structural, optical, and magnetic properties. Magnetic oxides are also promising photocatalysts for the degradation of organic pollutants. Antimicrobial activities and adsorption of heavy metals and radionucleides are also discussed.
Agriculture and industrial wastes (AIWs) have attracted much attention because of their huge environmental, economic, and social impacts. AIWs have been considered a crucial link of a closed-loop for the fabrication of nanomaterials and composites wherein they replace traditional resources with sustainable waste in waste management. In this context, the proper disposal of AIWs is required. This review aims to investigate the technical feasibility of using innovative AIW resources and various strategies for the fabrication of nanomaterials for improving energy applications. First, the utilization of AIWs is classified comprehensively. Second, key technologies to produce nanomaterials are summarized. In addition, this review discusses the potential applications of the fabricated nanomaterials in energy storage and energy conversion.
The rising significance of the field of biopolymers has driven the rapid progress of this distinctive class of polymeric materials in the past decades. Biodegradable polymers have acquired much attention because they play an essential role in humans’ lives due to their specific tunable electrical conductivity and biodegradability characteristics, making them fascinating in many applications. Herein, we debated the recent progress in developing biodegradable polymers and their applications. Initially, we introduce the basics of conducting and biodegradable polymers, trailed by debates about the effective strategies currently used to develop biopolymers. Special importance will focus on the uses of biodegradable polymers in drug delivery and tissue engineering, as well as wound healing, demonstrating the recent findings, and uses of several biodegradable polymers in modern biological uses. In this review, we have provided comprehensive viewpoints on the latest progress of the challenges and future prospects involving biodegradable polymers’ advancement and commercial applications.
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