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The rapid expansion of aquaculture ponds at the end of the last century raised concerns about their environmental and ecological impacts in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This study aims to comprehensively investigate the spatiotemporal evolutionary characteristics and explore the influencing factors of aquaculture development in the GBA. An automated approach was established to extract the aquaculture pond areas on the Google Earth Engine (GEE) platform using Landsat images and showed high accuracy. The aquaculture development in the GBA underwent three phases of rapid expansion (1986–1996), constant transformation (1996–2010), and drastic contraction (2010–2020), and was closely related to the important aquaculture policies in China and Guangdong Province. The local aquaculture ponds showed three evolutionary patterns of continuous shrinkage, earlier shrinkage, and later shrinkage, which were mainly influenced by the economic development level and urbanization in each city. Rapid urbanization created great demands for aquaculture products and intense land use competition, which drove the conversion of aquaculture ponds into impervious land and migration from the central GBA towards the surrounding coast and upstream areas. Government policies promoted the initial rapid increase and regulated the aquaculture ponds to develop towards a more environment-friendly and intensive mode, which led to a steady increase of aquaculture products despite the drastic decrease in aquaculture pond areas in the past decade. These evolutionary features are of significance for policymakers and farmers to maintain the sustainable development of aquaculture in the GBA and Guangdong Province.

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas.

A coal mine dual prevention construction evaluation model based on association rule mining and combined weighting cloud model is proposed. Firstly, based on association rule mining, the evaluation indicators for the effectiveness evaluation system of the dual prevention mechanism construction in coal mines are determined. Secondly, the analytic hierarchy process and anti entropy weight method are used to obtain the subjective and objective weights of the indicators. Then, the composite normalization method is used to obtain the combined weights of the indicators, and a cloud theory model is introduced to construct an evaluation model for coal mine dual prevention construction based on the cloud model. Finally, by analyzing a coal mine as a sample, the evaluation results of the model are compared with those of the analytic hierarchy process cloud model and the anti entropy weight method cloud model. The results indicate that the evaluation model is more scientific and accurate in the evaluation of dual prevention construction in coal mines, which helps to assist coal mines in establishing a scientific and reasonable “dual prevention” mechanism.

Abstract A ground-breaking roadmap of III-nitride solid-state deep-ultraviolet light emitters is demonstrated to realize the wafer-scale fabrication of devices in vertical injection configuration, from 2 to 4 inches. The epitaxial device structure is stacked on a GaN template instead of conventionally adopted AlN, where the primary concern of the tensile strain for Al-rich AlGaN on GaN is addressed via an innovative decoupling strategy, making the device structure decoupled from the underlying GaN template. Moreover, the strategy provides a protection cushion against the stress mutation during the removal of substrates. As such, large-sized wafers can be obtained without surface cracks, even after the removal of the sapphire substrates by laser lift-off. Wafer-scale fabrication of 280 nm vertical injection deep-ultraviolet light-emitting diodes is eventually demonstrated, where a light output power of 65.2 mW is achieved at a current of 200 mA, largely thanks to the significant improvement of light extraction. This work will definitely speed up the application of III-nitride solid-state deep-ultraviolet light emitters featuring high performance and scalability.

IOTA is an emerging decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications. It has the advantages of zero transaction fees, incremental scalability, and high-performance transaction rates. Despite its well-understood benefits, IOTA nodes need to withstand considerable resource costs to generate the distributed ledger. The main reason for this is that IOTA abandons the original blockchain reward mechanism and does not charge transaction fees. Therefore, in this paper we address the cost optimization issue for IOTA based on Lyapunov optimization theory. We take the first step in investigating the cost optimization problem of IOTA and exploring a new optimization scheme using Lyapunov optimization theory. Our proposed scheme enables IOTA to minimize the total cost of IOTA nodes through a computational optimization algorithm. Then, an optimized transaction rate control algorithm can be designed based on the large deviation theory to reduce orphan tangles that waste computational costs. In addition, we define and deduce the effective width of the tangle to monitor the total throughput and reduce the time spent on cost optimization to avoid unnecessary waste of resources. Lastly, a comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is both efficient and practical.

The Japanese giant salamander, Andrias japonicus, is an iconic but declining species of aquatic amphibian. A population within the Nawa River Basin of Tottori Prefecture persists but faces two significant threats. The first major threat is waterway obstructions, in the form of dams, weirs, and concrete embankments, which severely fragment and degrade the habitat. The second is water pollution, primarily caused by runoff from animal farms. Both threats to A. japonicus can be mitigated with proper actions. We provide science-based recommendations, advocating for the modification of dams and weirs by implementing ladderways to restore habitat connectivity and the provisioning of adequate breeding habitat in the form of artificial nest boxes. We also recommend the implementation of regular water quality monitoring and the enforcement of regulations regarding allowed runoff from animal farms. The Nawa River Basin could also be designated as an official sanctuary area for the Japanese Giant Salamander, lending additional formal protection and providing ecotourism opportunities for economically declining rural areas. Though the threats to A. japonicus are serious, the implementation of these mitigation measures and continual monitoring can allow this remarkable species to thrive.

Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations.

Efficient evacuation route planning during underground coal mine fires is essential to minimize casualties. This study addresses current shortcomings by proposing a real-time method that integrates a multifactor coupling analysis and the optimized multilayer perceptron regressor-shortest path faster algorithm (MSPFA). This research aims to enhance evacuation route planning by overcoming factors such as inadequate consideration, low accuracy, and information lag in existing methods. This study improves the shortest path faster algorithm (SPFA) for dynamic route planning, mitigates the impact of fixed walking speed parameters using the particle swarm algorithm, and selects the optimal model (MLPRegressor) through the Bootstrap algorithm for estimating personnel walking speeds. Validated through smoke-spread experiments, the MSPFA algorithm dynamically adjusts evacuation routes, preventing toxic passages. Visualization via drawing interchange format (DXF) successfully enhances route comprehension. The MSPFA algorithm outperforms the Dijkstra algorithm with a runtime of 78.5 msand a personnel evacuation time of 3344.74 s. This research establishes a theoretical foundation for intelligent evacuation decision making in underground fire disasters. By introducing the MSPFA algorithm, it provides crucial technical support, significantly reducing the risk of casualties during emergencies.