The mismatch between water demand and water availability in many megacities poses vexing water management challenges. Managers are forced to take remedial efforts to address these challenges, often ...with a heavy focus on infrastructure solutions such as building reservoirs or interbasin transfers to meet demand, which may in fact exacerbate the problem through unintended consequences that arise from neglect of social, economic, and environmental factors. Such a situation awaits Beijing, China, which faces major water management challenges in spite of the addition of a large interbasin transfer to meet increasing demand. In this study, a sociohydrologic model is developed for investigating Beijing's future water sustainability from a holistic and dynamic perspective. Using the model, we first explore the sociohydrologic mechanisms that contributed to Beijing's worsening water situation during 1988–2014. We then use the model to assess possible future impacts of the South to North Water Diversion Project on Beijing's water supply prospects for the 2015–2035 period. Alternative futures are explored by combining three different sustainable management strategies. The model results show that the source of Beijing's dominant water pressure experienced a transformation from productive to domestic water use over the last 30 years. They also indicate that the transfer water via South to North Water Diversion Project cannot fundamentally reverse Beijing's water shortage in the long term and that demand‐oriented management measures will be required for alleviating the city's water stress. These findings provide guidance not only for Beijing's water management but also for other less developed cities around the world.
Key Points
A dynamic urban sociohydrologic model is developed for understanding Beijing's water sustainability issues
Possibility space of alternative water futures is explored to guide future water management efforts
Constraining domestic water demand remains a top concern for alleviating Beijing's future water stress
The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error” discovery approaches. As an alternative route, the Materials Genome Initiative has largely popularized new ...approaches relying on artificial intelligence and machine learning for accelerating the discovery and optimization of novel, advanced materials. Here, we review some recent progress in adopting machine learning to accelerate the design of new glasses with tailored properties.
•We review some recent progress in machine learning applied to glass science.•We provide an introduction to common machine learning techniques.•We highlight the benefits of “physics-informed machine learning.”•We show how machine learning and physics-based modeling can be used in synergy.•We discuss some potential future directions in the use of machine learning in glass science.
Shale gas is one of the most promising resources for unconventional natural gas. Several shale samples were collected from the Silurian Longmaxi Formation in the Yibin region, Sichuan Province, ...China. The basic geological parameters of the shale samples including total organic carbon, clay mineral content, and vitrinite reflectance were detected. Pore structure characteristics were analyzed with scanning electron microscopy, high-pressure mercury injection, and low-temperature nitrogen and carbon dioxide adsorption methods. Isothermal adsorption and desorption experiments were carried out using gravimetric methods. The isosteric heat of the shale adsorption was calculated using the isothermal adsorption experimental results. According to the experimental results, the shale samples have high maturity, low porosity and penetration. The surface morphological structures include organic pores, clay mineral pores, intergranular pores of authigenic minerals, dissolution pores and micro-cracks. Micropores comprised the majority of the developed pores in the shale samples and play a major role in adsorption processes. The adsorption and desorption results show that the adsorption amount of gas mainly undergoes a rapid increase phase, a slowly rising transition phase and a gentle phase, and desorption hysteresis generally occurs during gas desorption. Adsorption thermodynamics results show that the volume of adsorbed gas decreases with the increase of adsorption temperature and the isosteric heat increases with the increase of the volume of the adsorbed gas.
Isothermal adsorption and desorption experiments were carried out using gravimetric method with magnetic suspension balance.
The operating status of power systems is influenced by growing varieties of factors, resulting from the developing sizes and complexity of power systems. In this situation, the model-based methods ...need to be revisited. A data-driven method, as the novel alternative on the other hand, is proposed in this paper. It reveals the correlations between the factors and the system status through statistical properties of data. An augmented matrix as the data source is the key trick for this method and is formulated by two parts: (1) status data as the basic part; and (2) factor data as the augmented part. The random matrix theory is applied as the mathematical framework. The linear eigenvalue statistics, such as the mean spectral radius, are defined to study data correlations through large random matrices. Compared with model-based methods, the proposed method is inspired by a pure statistical approach without a prior knowledge of operation and interaction mechanism models for power systems and factors. In general, this method is direct in analysis, robust against bad data, universal to various factors, and applicable for real-time analysis. A case study based on the standard IEEE 118-bus system validates the proposed method.
Chitosan modification is an important method for the development of adsorbents that has attracted considerable interest in recent years. In this regard, a new type of efficient Pb(II) adsorbent was ...prepared in a simple and cost-effective way. In this study, carboxylated chitosan (CYCS) and carboxylated nanocellulose (CNC) were used to chelate and synthesize hydrogel spheres with effective adsorption sites, in calcium chloride solution. The prepared carboxylated chitosan/carboxylated nanocellulose (CYCS/CNC) hydrogel beads were used as Pb(II) adsorbents, and using scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy, the structure and adsorption properties of the prepared beads were investigated. The CYCS/CNC adsorbents exhibited an excellent aqueous Pb(II) adsorption capacity (qm = 334.92 mg g−1), and the experimental results further revealed that the adsorption data fitted well with the Langmuir model, and the adsorption kinetics accorded with the pseudo-second-order model. Additionally, the adsorption mechanism was identified as monolayer chemisorption.
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•Efficient and economic CYCS/CNC composite is prepared for Pb(II) adsorption.•CYCS/CNC exhibits excellent aqueous Pb(II) adsorption capacity (Qm = 334.92 mg g−1).•Adsorption mechanism of CYCS/CNC includes chemical and electrostatic adsorption.•The adsorption performance is good even after four regeneration cycles.
scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the ...number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.
In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. ...Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it's effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries.
With the increasingly prominent environmental problems in the world today, the development of an integrated energy system and the introduction of a carbon-trading mechanism have become important ...means to realize the low carbonization of the energy industry. Based on this, this paper introduces the carbon-trading mechanism into the research on the optimal dispatch of an integrated energy system. The mechanism of integrated energy demand response participating in low-carbon economic dispatch is analyzed. The relationship between carbon emissions and carbon-trading price in carbon-trading mechanism is described. On the basis of considering the commodity attributes of the electricity and gas load and the flexible supply characteristics of the thermal load, an incentive-type comprehensive energy demand response model is established. Finally, aiming at the lowest comprehensive operating cost, a comprehensive energy system model considering the power balance and equipment constraints of the electric–gas–heat system is established, using an improved particle swarm algorithm to solve it. Simulations verify the effectiveness of the proposed method in reducing the carbon emissions and operating costs of integrated energy systems.
Background
ATP‐binding cassette subfamily G member 1 is mostly known as a transporter for intracellular cholesterol efflux, and a number of studies indicate that ABCG1 also functions actively in ...tumor initiation and progression. This review aimed to provide an overall review of how ABCG1 acts in tumor progression.
Method
A comprehensive searching about ABCG1 and tumor was conducted up to November 2023 using proper keywords through databases including PubMed and Web of Science.
Result
Overall, ABCG1 plays a crucial role in the development of multiple tumorigenesis. ABCG1 enhances tumor‐promoting ability through conferring stem‐like properties to cancer cells and mediates chemoresistance in multiple cancers. Additionally, ABCG1 may act as a kinase to phosphorylate downstream molecules and induces tumor growth. In tumor microenvironment, ABCG1 plays a substantial role in immunity response through macrophages to create a tumor‐favoring circumstance.
Conclusion
High expression of ABCG1 is usually associated with poor prognosis, which means ABCG1 may be a potential biomarker for early diagnosis and prognosis of various cancers. ABCG1‐targeted therapy may provide a novel treatment for cancer patients.
ABCG1 plays a critical role in promoting tumor development mainly through four aspects including tumor initiation and progression, chemoresistance, cancer cell stemness and tumor microenvironment. High expression of ABCG1 in multiple cancers is usually associated with a poor prognosis.
The application of machine learning to predict materials' properties usually requires a large number of consistent data for training. However, experimental datasets of high quality are not always ...available or self-consistent. Here, as an alternative route, we combine machine learning with high-throughput molecular dynamics simulations to predict the Young's modulus of silicate glasses. We demonstrate that this combined approach offers good and reliable predictions over the entire compositional domain. By comparing the performances of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning.
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