Hydrogels are a three-dimensional and crosslinked network of hydrophilic polymers. They can absorb a large amount of water or biological fluids, which leads to their swelling while maintaining their 3D structure without dissolving (Zhu and Marchant, Expert Rev Med Devices 8:607-626, 2011). Among the numerous polymers which have been utilized for the preparation of the hydrogels, polysaccharides have gained more attention in the area of pharmaceutics; Sodium alginate is a non-toxic, biocompatible, and biodegradable polysaccharide with several unique physicochemical properties for which has used as delivery vehicles for drugs (Kumar Giri et al., Curr Drug Deliv 9:539-555, 2012). Owing to their high-water content and resembling the natural soft tissue, hydrogels were studied a lot as a scaffold. The formation of hydrogels can occur by interactions of the anionic alginates with multivalent inorganic cations through a typical ionotropic gelation method. However, those applications require the control of some properties such as mechanical stiffness, swelling, degradation, cell attachment, and binding or release of bioactive molecules by using the chemical or physical modifications of the alginate hydrogel. In the current review, an overview of alginate hydrogels and their properties will be presented as well as the methods of producing alginate hydrogels. In the next section of the present review paper, the application of the alginate hydrogels will be defined as drug delivery vehicles for chemotherapeutic agents. The recent advances in the application of the alginate-based hydrogels will be describe later as a wound dressing and bioink in 3D bioprinting.

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Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth's ecosystem; therefore, assessing them has become more important. Remote sensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. Therefore, researchers have examined the relationships between salinity, height, and temperature of the oceans/Seas to estimate their subsurface water temperature using dynamical models and model-based data assimilation (numerical based and statistical) approaches, which simulate these parameters by employing remotely sensed data and in situ measurements. Due to the requirements of comprehensive perception and the importance of global warming in decision making and scientific studies, this review provides comprehensive information on the methods that are used to estimate ocean/sea subsurface water temperature from remotely and non-remotely sensed data. To clarify the subsurface processes, the challenges, limitations, and perspectives of the existing methods are also investigated.

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Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction.

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