North China University of Water Resources and Electric Power

Abstract This paper innovatively proposes the Black Kite Algorithm (BKA), a meta-heuristic optimization algorithm inspired by the migratory and predatory behavior of the black kite. The BKA integrates the Cauchy mutation strategy and the Leader strategy to enhance the global search capability and the convergence speed of the algorithm. This novel combination achieves a good balance between exploring global solutions and utilizing local information. Against the standard test function sets of CEC-2022 and CEC-2017, as well as other complex functions, BKA attained the best performance in 66.7, 72.4 and 77.8% of the cases, respectively. The effectiveness of the algorithm is validated through detailed convergence analysis and statistical comparisons. Moreover, its application in solving five practical engineering design problems demonstrates its practical potential in addressing constrained challenges in the real world and indicates that it has significant competitive strength in comparison with existing optimization techniques. In summary, the BKA has proven its practical value and advantages in solving a variety of complex optimization problems due to its excellent performance. The source code of BKA is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/161401-black-winged-kite-algorithm-bka .

In recent decades, climate change and other anthropogenic activities have substantially affected groundwater systems worldwide. These impacts include changes in groundwater recharge, discharge, flow, storage, and distribution. Climate-induced shifts are evident in altered recharge rates, greater groundwater contribution to streamflow in glacierized catchments, and enhanced groundwater flow in permafrost areas. Direct anthropogenic changes include groundwater withdrawal and injection, regional flow regime modification, water table and storage alterations, and redistribution of embedded groundwater in foods globally. Notably, groundwater extraction contributes to sea level rise, increasing the risk of groundwater inundation in coastal areas. The role of groundwater in the global water cycle is becoming more dynamic and complex. Quantifying these changes is essential to ensure sustainable supply of fresh groundwater resources for people and ecosystems.

Abstract Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984–1999) to the end (2010–2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km 2 , and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km 2 ) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon emissions.

Abstract Molybdenum disulfide (MoS 2 ) nanosheets have been attracting increasing research interests due to their unique material properties. However, the lack of a reliable large‐scale production method impedes their practical applications. Here a facile, efficient, and scalable method for the fabrication of high‐concentration aqueous dispersion of MoS 2 nanosheets using combined grinding and sonication is reported. The 26.7 ± 0.7 mg/mL concentration achieved is the highest concentration in an aqueous solution reported up to now. Grinding generates pure shear forces to detach the MoS 2 layers from the bulk materials. Subsequent sonication further breaks larger crystallites into smaller crystallites, which promotes the dispersion of MoS 2 nanosheets in ethanol/water solutions. The exfoliation process establishes a new paradigm in the top‐down fabrication of 2D nanosheets in aqueous solution. In the meantime, MoS 2 ‐based sensing film produced using this approach has successfully demonstrated the feasibility of a low‐cost and efficient NH 3 gas sensor using inkjet printing as a viable method.

Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM). In addition, the particle swarm optimization (PSO) is used to determine free parameters of SVM. The study data from a large size catchment of the Yellow River in China are used to illustrate the performance of the proposed model. In order to measure the forecasting capability of the model, an ordinary least-squares (OLS) regression and a typical three-layer feed-forward artificial neural network (ANN) are employed as the benchmark model. The performance of the models was tested using the root mean squared error (RMSE), the average absolute relative error (AARE), the coefficient of correlation (R) and Nash–Sutcliffe efficiency (NSE). The PSO–SVM–EEMD model improved ANN model forecasting (65.99%) and OLS regression (64.40%), and reduced RMSE (67.7%) and AARE (65.38%) values. Improvements of the forecasting results regarding the R and NSE are 8.43%, 18.89% and 182.7%, 164.2%, respectively. Consequently, the presented methodology in this research can enhance significantly rainfall-runoff forecasting at the studied station.

As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply–demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on variational mode decomposition and long short-term memory with seasonal factors elimination and error correction is proposed in this article. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models.

Abstract Irrigation is a land management practice with major environmental impacts. However, global energy consumption and carbon emissions resulting from irrigation remain unknown. We assess the worldwide energy consumption and carbon emissions associated with irrigation, while also measuring the potential energy and carbon reductions achievable through the adoption of efficient and low-carbon irrigation practices. Currently, irrigation contributes 216 million metric tons of CO 2 emissions and consumes 1896 petajoules of energy annually, representing 15% of greenhouse gas emissions and energy utilized in agricultural operations. Despite only 40% of irrigated agriculture relies on groundwater sources, groundwater pumping accounts for 89% of the total energy consumption in irrigation. Projections indicate that future expansion of irrigation could lead to a 28% increase in energy usage. Embracing highly efficient, low-carbon irrigation methods has the potential to cut energy consumption in half and reduce CO 2 emissions by 90%. However, considering country-specific feasibility of mitigation options, global CO 2 emissions may only see a 55% reduction. Our research offers comprehensive insights into the energy consumption and carbon emissions associated with irrigation, contributing valuable information that can guide assessments of the viability of irrigation in enhancing adaptive capacity within the agricultural sector.

The objective of this article is to further the study of journal interdisciplinarity, or, more generally, knowledge integration at the level of individual articles. Interdisciplinarity is operationalized by the diversity of subject fields assigned to cited items in the article's reference list. Subject fields and subfields were obtained from the L euven‐ B udapest ( ECOOM ) subject‐classification scheme, while disciplinary diversity was measured taking variety, balance, and disparity into account. As diversity measure we use a H ill‐type true diversity in the sense of J ost and L einster‐ C obbold. The analysis is conducted in 3 steps. In the first part, the properties of this measure are discussed, and, on the basis of these properties it is shown that the measure has the potential to serve as an indicator of interdisciplinarity. In the second part the applicability of this indicator is shown using selected journals from several research fields ranging from mathematics to social sciences. Finally, the often‐heard argument, namely, that interdisciplinary research exhibits larger visibility and impact, is studied on the basis of these selected journals. Yet, as only 7 journals, representing a total of 15,757 articles, are studied, albeit chosen to cover a large range of interdisciplinarity, further research is still needed.

Assessment of ecoenvironmental vulnerability plays an important role in the guidance of regional planning, the construction and protection of ecological environment, which requires comprehensive consideration on regional resources, environment, ecology, society and other factors. Based on the driving mechanism and evolution characteristics of ecoenvironmental vulnerability in cold and arid regions of China, a novel evaluation index system on ecoenvironmental vulnerability is proposed in this paper. For the disadvantages of conventional entropy weight method, an improved entropy weight assessment model on ecoenvironmental vulnerability is developed and applied to evaluate the ecoenvironmental vulnerability in western Jilin Province of China. The assessing results indicate that the model is suitable for ecoenvironmental vulnerability assessment, and it shows more reasonable evaluation criterion, more distinct insights and satisfactory results combined with the practical conditions. The model can provide a new method for regional ecoenvironmental vulnerability evaluation.

ABSTRACT As artificial intelligence (AI) has been integrated into foreign language (FL) education, learners' well‐being is influenced by various factors, including technological, personal and contextual elements. However, few studies explored how external and internal factors jointly shape FL learners' well‐being in the era of generative AI. To fill this gap, this study explores the effects of generative AI acceptance, perceived teachers' enthusiasm and self‐efficacy on FL learners' well‐being by investigating 613 university learners of English as a foreign language (EFL). The structural equation modelling results reveal that (1) generative AI acceptance positively predicts EFL learners' well‐being and self‐efficacy; (2) perceived teachers' enthusiasm does not predict learners' well‐being and positively predicts EFL learners' self‐efficacy; and (3) the self‐efficacy for receptive skills mediates the relationship between generative AI acceptance/perceived teachers' enthusiasm and EFL learners' well‐being, whereas self‐efficacy for productive skills does not play the mediation role. This research broadens the understanding of the antecedents of EFL learners' well‐being and extends the application of self‐efficacy theory in the AI‐driven educational environment, providing significant pedagogical implications.

In recent years, the haze has caused serious troubles to people's lives, with the continuous increase of PM2.5 emissions. The accurate prediction of PM2.5 is very crucial for policy makers to make predictive measures. Due to the nonlinearity of the PM2.5 time series, it is difficult to predict accurately. Despite some studies about PM2.5 being proposed, the problem of the LSTM (long short-term memory) gradient disappearance and random selection of wavelet orders and layers isn't still solved. In this study, a novel model based on WT (wavelet transform)-SAE (stacked autoencoder)-LSTM is proposed. Firstly, six study sites from China are taken as examples and WT is used to decompose PM2.5 time series into several low-and high- frequency components based on different samples. Secondly, the decomposed components are predicted based on SAE-LSTM. Finally, the predicted results are reconstructed in view of all low-and high-frequency components and the predicted results are obtained. The results imply that: (1) the forecasting performance of SAE-LSTM is better than that of other models (e.g., BP (back propagation)) used for comparison; (2) for six different PM 2.5 samples, four orders five layers, five orders six layers, five orders seven layers, three orders six layers, five orders seven layers, and five orders six layers are the most appropriate. The conclusion that such a novel model may help to enhance the accuracy of PM 2.5 prediction can be drawn.

ABSTRACT As artificial intelligence (AI) gains prominence, its integration into second language (L2) /foreign language (FL) instruction has become a significant trend. Despite the considerable promise of AI for L2/FL learning, more research is still needed on its effects on student academic engagement in literature classes and the corresponding emotional experiences. This study, therefore, aimed to examine the effects of AI use on English as a foreign language (EFL) learners' academic engagement, and the emotional experience was also qualitatively explored. Students were allocated to the experimental group ( N = 48), who received instruction integrated with AI, and the control group ( N = 48), who received traditional instruction without AI assistance. Quantitative data were collected using an FL engagement scale, supplemented by individual semi‐structured interviews in the qualitative phase. The results indicated that integrating AI into EFL instruction has a positive effect on students' cognitive, emotional and social engagement. Moreover, the learners' emotional experiences were found to be abundant and dynamic, exerting influence on their academic engagement. This study provides valuable insights for language educators and researchers regarding integrating AI into EFL instruction.

Background: Competition for freshwater between cities and agriculture is projected to grow due to rapid urbanization, particularly in the Global South. Water reallocation from rural to urban regions has become a common strategy to meet freshwater needs in growing cities. Conceptual issues and associated measurement problems have impeded efforts to compare and learn from global experiences. This review examines the status and trends of water reallocation from rural to urban regions based on academic literature and policy documents.
\nMethods: We conduct a systematic literature review to establish the global reallocation database (GRaD). This process yielded 97 published studies (academic and policy) on rural-to-urban reallocation. We introduce the concept of reallocation ‘dyads’ as the unit of analysis to describe the pair of a recipient (urban) and donor (rural) region. A coding framework was developed iteratively to classify the drivers, processes and outcomes of water reallocation from a political economy perspective.
\nResults: The systematic review identified 69 urban agglomerations receiving water through 103 reallocation projects (dyads). Together these reallocation dyads involve approximately 16 billion m3 of water per year moving almost 13 000 kilometres to urban recipient regions with an estimated 2015 population of 383 million. Documented water reallocation dyads are concentrated in North America and Asia with the latter constituting the majority of dyads implemented since 2000.
\nSynthesis: The analysis illustrates how supply and demand interact to drive water reallocation projects, which can take many forms, although collective negotiation and administrative decisions are most prevalent. Yet it also reveals potential biases and gaps in coverage for parts of the Global South (particularly in South America and Africa), where reallocation (a) can involve informal processes that are difficult to track and (b) receives limited coverage by the English-language literature covered by the review. Data regarding the impacts on the donor region and compensation are also limited, constraining evidence to assess whether a water reallocation project is truly effective, equitable and sustainable. We identify frameworks and metrics for assessing reallocation projects and navigating the associated trade-offs by drawing on the concept of benefit sharing.

Recently, the development of modern vehicles has brought about aggressive integration and miniaturization of on-board electrical and electronic devices. It will lead to exponential growth in both the overall waste heat and heat flux to be dissipated to maintain the devices within a safe temperature range. However, both the total heat sinks aboard and the cooling capacity of currently utilized thermal control strategy are severely limited, which threatens the lifetime of the on-board equipment and even the entire flight system and shrink the vehicle’s flight time and range. Facing these thermal challenges, the USA proposed the program of “INVENT” to maximize utilities of the available heat sinks and enhance the cooling ability of thermal control strategies. Following the efforts done by the USA researchers, scientists in China fought their ways to develop thermal management technologies for Chinese advanced energy-optimized airplanes and spacecraft. This paper elaborates the available on-board heat sinks and aerospace thermal management systems using both active and passive technologies not confined to the technology in China. Subsequently, active thermal management technologies in China including fuel thermal management system, environment control system, non-fuel liquid cooling strategy are reviewed. At last, space thermal control technologies used in Chinese Space Station and Chang’e-3 and to be used in Chang’e-5 are introduced. Key issues to be solved are also identified, which could facilitate the development of aerospace thermal control techniques across the world.

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time-frequency domains. The key features are selected based on Pearson's Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL.

Abstract. Wetlands, often called the “kidneys of the earth”, play an important role in maintaining ecological balance, conserving water resources, replenishing groundwater and controlling soil erosion. Wetland mapping is very challenging because of its complicated temporal dynamics and large spatial and spectral heterogeneity. An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping by combining an automatic sample extraction method, existing multi-sourced products, satellite time-series images and a stratified classification strategy. This approach allowed for the generation of the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal tidal wetland sub-categories (mangrove, salt marsh and tidal flats), which was developed using Google Earth Engine platform. We first combined existing multi-sourced global wetland products, expert knowledge, training sample refinement rules and visual interpretation to generate large and geographically distributed wetland training samples. Second, we integrated the Landsat reflectance time-series products and Sentinel-1 synthetic aperture radar (SAR) imagery to generate various water-level and phenological information to capture the complicated temporal dynamics and spectral heterogeneity of wetlands. Third, we applied a stratified classification strategy and the local adaptive random forest classification models to produce the wetland dataset with a fine classification system at each 5∘×5∘geographical tile in 2020. Lastly, GWL_FCS30, mosaicked by 961 5∘×5∘ regional wetland maps, was validated using 25 708 validation samples, which achieved an overall accuracy of 86.44 % and a kappa coefficient of 0.822. The cross-comparisons with other global wetland products demonstrated that the GWL_FCS30 dataset performed better in capturing the spatial patterns of wetlands and had significant advantages over the diversity of wetland sub-categories. The statistical analysis showed that the global wetland area reached 6.38 million km2, including 6.03 million km2 of inland wetlands and 0.35 million km2 of coastal tidal wetlands, approximately 72.96 % of which were distributed poleward of 40∘ N. Therefore, we can conclude that the proposed method is suitable for large-area wetland mapping and that the GWL_FCS30 dataset is an accurate wetland mapping product that has the potential to provide vital support for wetland management. The GWL_FCS30 dataset in 2020 is freely available at https://doi.org/10.5281/zenodo.7340516 (Liu et al., 2022).

Corrosion, more specifically, pitting corrosion happening extremely in marine environments, leads to lifespan of materials drastically decreasing in service, which causes enormous economic loss and even environmental disaster and casualties. In the past decade, increasing efforts have been made to study the corrosion behaviors of materials in chloride-containing aqueous environments. Herein, this work provides an overview of recent progress in understanding the degradation mechanism and improving the corrosion resistance and corrosion-wear resistance of materials from bulk metal to surface treatment involving organic coating, metal and its alloy or compound coating. The particular emphasis is given to the periodic layered structures (PLSs), whose anti-corrosion properties outperformed others to some extent, wherever in terms of bulk metal or surface treatment, regardless of aggressive environment (corrosion or corrosion-wear conditions). Numerical simulation based on kinds of models at different scales is introduced to deeply understand the process of corrosion and/or corrosion-wear in chloride-containing aqueous environment. Combined experimental result with numerical simulation, the micro-galvanic corrosion dominated degradation mechanism of PLSs is critically analyzed. Types of setups to realize corrosion-wear in laboratory are also summarized. At last, future research and development are prospected, offering to develop a basic application of PLSs designed by corrosion protection methodology in the near future.

Riverine ecosystems have adapted to natural discharge variations across seasons. However, evidence suggesting that climate change has already impacted magnitudes of river flow seasonality is limited to local studies, mainly focusing on changes of mean or extreme flows. This study introduces the use of apportionment entropy as a robust measure to assess flow-volume nonuniformity across seasons, enabling a global analysis. We found that ~21% of long-term river gauging stations exhibit significant alterations in seasonal flow distributions, but two-thirds of these are unrelated to trends in annual mean discharge. By combining a data-driven runoff reconstruction with state-of-the-art hydrological simulations, we identified a discernible weakening of river flow seasonality in northern high latitudes (above 50°N), a phenomenon directly linked to anthropogenic climate forcing.

In this study, a facilitated transport mixed matrix membrane was fabricated by a surface coating method. Polyvinyl amine (PVAm) and chitosan (Cs) were used as the polymer matrix materials and coated onto a porous polysulfone (PS) support. Graphene oxide (GO) grafted with hyperbranched polyethylenimine (HPEI-GO) was added as the nanofiller. The gas separation tests with CO 2 /N 2 (10:90 v:v) mixed gas suggest that the addition of GO could improve CO 2 /N 2 selectivity. The highest CO 2 permeance was 36 GPU in the membrane with 2.0 wt % HPEI-GO, and the optimal selectivity was 107 in the membrane with 3.0 wt % HPEI-GO. Herein, GO could provide a transport channel for CO 2 and enhance the long-term stability of the membranes. Further gas separation tests under various relative humidities confirmed that facilitated transport was the main mechanism of gas separation through the membrane. The stability test suggests that the membrane has long-term stability. CO 2 transports through the membrane mainly by the facilitated transport mechanism with assistance from the solution-diffusion mechanism, while N 2 transports only by the solution-diffusion mechanism.

Halloysite nanotubes (HNTs) are naturally occurring clay mineral with nanotubular structures and have found increasing potential applications in industrial fields.