A salinity gradient solar pond (SGSP) is a large and deep artificial basin of layered brine, that collects and stores simultaneous solar energy for use in various applications. Experimental and theoretical studies have been launched to understand the thermal behavior of SGSPs, under different operating conditions. This article then traces the history of SGSPs, from their natural discovery to their current artificial applications and the progress of studies and research, according to their chronological sequence, in terms of determining their physical and dynamic aspects, their operation, management, and maintenance. It has extensively covered the theoretical and experimental studies, as well as the direct and laboratory applications of this technology, especially the most famous and influential in this field, classified according to the aspect covered by the study, with a comparison between the different results obtained. In addition, it highlighted the latest methods to improve the performance of an SGSP and facilitate its operation, such as the use of a magnetic field and the adoption of remote data acquisition, with the aim of expanding research and enhancing the benefit of this technology.

Cobalt-ion batteries are considered a promising battery chemistry for renewable energy storage. However, there are indeed challenges associated with co-ion batteries that demonstrate undesirable side reactions due to hydrogen gas production. This study demonstrates the use of a nanocomposite electrolyte that provides stable performance cycling and high Co²⁺ conductivity (approximately 24 mS cm⁻¹). The desirable properties of the nanocomposite material can be attributed to its mechanical strength, which remains at nearly 68 MPa, and its ability to form bonds with H₂O. These findings offer potential solutions to address the challenges of co-dendrite, contributing to the advancement of co-ion batteries as a promising battery chemistry. The exceptional cycling stability of the co-metal anode, even at ultra-high rates, is a significant achievement demonstrated in the study using the nanocomposite electrolyte. The co-metal anode has a 3500-cycle current density of 80 mA cm⁻², which indicates excellent stability and durability. Moreover, the cumulative capacity of 15.6 Ah cm⁻² at a current density of 40 mA cm⁻² highlights the better energy storage capability. This performance is particularly noteworthy for energy storage applications where high capacity and long cycle life are crucial. The H₂O bonding capacity of the component in the nanocomposite electrolyte plays a vital role in reducing surface passivation and hydrogen evolution reactions. By forming strong bonds with H₂O molecules, the polyethyne helps prevent unwanted reactions that can deteriorate battery performance and efficiency. This mitigates issues typically associated with excess H₂O and ion presence in aqueous Co-ion batteries. Furthermore, the high-rate performance with excellent stability and cycling stability performance (>500 cycles at 8 C) of full Co||MnO₂ batteries fabricated with this electrolyte further validates its effectiveness in practical battery configurations. These results indicate the potential of the nanocomposite electrolyte as a valuable and sustainable option, simplifying the development of reliable and efficient energy storage systems and renewable energy applications.

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.

The rapid progress of information technology has made public online participation in policy formulation an inevitable product of modern government reshaping and reconstruction. However, compared with developed countries, citizens’ online participation in policy formulation in China started relatively late. Thus, in order to explore an effective and efficient method for Chinese citizens’ participation in policy formulation, this research made a brief review of the experiences from the typical developed country of United States of America at first, followed by some other developed countries such as Singapore, South Korea, and Japan in Asia with similar situations. Still, combined with the current situation of the China itself into consideration, this research further proposes targeted recommendations. It is expected that the findings in this research could provide some references for the Chinese government to form more effective and efficient theoretical frameworks targeted at the future development trends of the Chinese society and accordingly, to improve the construction of democracy in China.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

In the teaching of professional courses, the introduction of information technology teaching mode, currently the most widely used is blended teaching. This teaching mode highlights the student's learning subject status, and the overall teaching effect is significant. Linux course is a highly practical course, and the introduction of blended teaching mode in specific course teaching is of great significance for promoting curriculum reform and development. This article provides a brief introduction to Linux courses, analyzes the importance of blended teaching methods, and explores strategies for effectively applying online and offline mixed teaching modes in Linux courses.