The dynamic topology, narrow transmission bandwidth, and limited energy of sensor nodes in mobile underwater acoustic sensor networks (UASNs) pose challenges to design an efficient and robust network for underwater communications. In this paper, we propose a novel machine learning-based clustering and routing scheme, named energy-efficient clustering and cooperative routing based on improved K-means and Q-learning (ECRKQ), to reduce and balance energy consumption among sensor nodes in a mobile UASN and improve the bandwidth utilization. In the cluster head (CH) selection stage, ECRKQ modifies the K-means algorithm to dynamically select a CH based on the residual energy of the node and the distance from the node to the centroid in a cluster. In the clustering stage, ECRKQ adopts the Q-learning algorithm by incorporating the residual energy of the CH, the energy consumption of data transmission from the node to the CH, and the energy consumption of the data transmission from the CH to the base station into the Q-value function. In the data transmission stage, ECRKQ applies the dynamic coded cooperation (DCC) transmission to improve the bandwidth utilization and the robustness of the underwater communications. In the DCC transmission, cooperative nodes are also dynamically selected based on the residual energy and the energy consumption of transmitting a packet to their destinations. In the simulation, we apply the ocean current drifting model to emulate the position variation of nodes caused by ocean currents in a mobile UASN. The simulation results show that the proposed ECRKQ scheme can achieve more balanced energy consumption among sensor nodes in a mobile UASN than that of the existing scheme.

Education for Sustainable Development (ESD) forms part of Target 4.7 of Sustainable Development Goal 4 in the 2030 Agenda. This paper presents an effort to propose an evaluation framework of elementary school teaching materials in Taiwan for sustainable development considering three dimensions of sustainability: Environmental, social, and economic. The proposed framework comprises four levels: Lever 1, education for sustainable development; Level 2, teaching scopes; Level 3, learning indicators, and; Level 4, learning topics. This study first, through literature reviews, proposed an initial evaluation framework and then, through in-depth expert interviews, obtained a modified framework. Thereafter, the Delphi questionnaires were conducted to establish the final evaluation framework. The framework includes four teaching scopes, ten learning indicators and twenty-one learning topics. Furthermore, the weights of each scope and its associated indicators were analyzed and compared through AHP questionnaires to obtain the scoring table for sustainability teaching materials implemented in a school. Finally, the scoring table was applied to an existing elementary school to investigate its implementation of the teaching materials on sustainable development. Based on the result from the scoring table, the areas needed for improvement were identified and the improvement strategies were then proposed.

As a unique landform in the island and reef area, the newborn sandbank is not only the initial stage of island development, but also has a rapid evolution and a complex dynamic mechanism. However, the dynamic geomorphology mechanism of the newborn sandbank is still lacking extensive study and direct evidence of the interaction process between the marine dynamics and the newborn sandbank geomorphology. Therefore, in order to reveal the interaction mechanisms between marine dynamics and newborn sandbanks, a newborn sandbank in the sea area of the Xisha Islands, in the South China Sea, has been selected as the focus of this research. The method of numerical simulation was used to discuss and analyze the wave field characteristics around the newborn sandbank and their impacts on the sandbank’s migration and development. The results show that: (1) The islands and reefs have significant refraction, diffraction, and energy dissipation effects on waves, and the newborn sandbank has the same effect, but with a weaker function. The wave height around the reef islands reduced by approximately 60–67% in dominated and strong wave directions. At the same time, the wave height attenuation in the wave shadow zone, behind the newborn sandbank, can reach approximately 27–33%. (2) Wind is important for the evolution of wave fields; in particular, when the wind speed exceeds grades four and five, the effect of the wind on the waves is particularly significant, causing the winds to control the wave characteristics around the islands and newborn sandbanks. This results in significant seasonal differences in wave fields within the sea area. (3) The wave direction primarily controls the migration direction of the newborn sandbank, and the wave height primarily controls the migration speed and distance. After one month of wave action in the strong wave direction, the maximum eastward deposition length was approximately 50 m. After one month of wave action in the dominated wave direction, the maximum eastward deposition length was approximately 60 m. Therefore, the topography of the newborn sandbank affects the wave propagation, meanwhile, the wave conversely determines migration and development of the newborn sandbank in a short term. The dynamic geomorphology action between the wave and newborn sandbank is a fast two-way process, and occurs not only during storms or the winter monsoon, but also during other, more common, weather events.

To overcome the $\mathcal {O}(L^{2})$ complexity of the recursive least squares (RLS) algorithm, this article proposes a more computationally efficient fast array adaptive equalizer (FAE). Benefiting from the array representation and structural adjustments of the equalizer input vector, the proposed fast array RLS (FARLS) algorithm with the linear $\mathcal {O}(L)$ complexity is derived while ensuring performance. The comprehensive comparison of current adaptive algorithms is conducted, including the classical RLS and least mean square algorithms, as well as the emerging proportionate RLS and data-reusing RLS algorithms. Through two aspects of validation, including computational efficiency and steady-state mean square deviation, the FARLS algorithm is successfully demonstrated to have advantages over the above algorithms. In addition, we combine the FARLS algorithm with phase coherent in FAE to further enhance equalization performance, and validate its noise robustness and Doppler shift resistance through simulations and at-sea experiments. Real data shows that FAE outperforms its counterparts by at least 42.1% in terms of the percentage the percentage of frames with BER = 0.