Explore the vast range of titles available.

Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km 2 rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries. Potential rooftop photovoltaic in China affords 4 billion tons of carbon mitigation in 2020 under ideal assumptions, equal to 70% of China’s carbon emissions from electricity and heat. Yet most cities have exploited the potential to a limited degree.

The recent novel coronavirus disease (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is seeing a rapid increase in infected patients worldwide. The host immune response to SARS-CoV-2 appears to play a critical role in disease pathogenesis and clinical manifestations. SARS-CoV-2 not only activates antiviral immune responses, but can also cause uncontrolled inflammatory responses characterized by marked pro-inflammatory cytokine release in patients with severe COVID-19, leading to lymphopenia, lymphocyte dysfunction, and granulocyte and monocyte abnormalities. These SARS-CoV-2-induced immune abnormalities may lead to infections by microorganisms, septic shock, and severe multiple organ dysfunction. Therefore, mechanisms underlying immune abnormalities in patients with COVID-19 must be elucidated to guide clinical management of the disease. Moreover, rational management of the immune responses to SARS-CoV-2, which includes enhancing anti-viral immunity while inhibiting systemic inflammation, may be key to successful treatment. In this review, we discuss the immunopathology of COVID-19, its potential mechanisms, and clinical implications to aid the development of new therapeutic strategies against COVID-19.

With the continuous advancement of artificial intelligence (AI) technology, AIGC (AI-generated content) has increasingly permeated various sectors, leading to a significant transformation in the design industry. This study aims to explore user purchase intention and the influencing factors of AI-generated cultural and creative products, thereby formulating strategies to enhance user satisfaction. Based on the stimulus-organism-response theory, the theory of planned behavior, the value adoption model, the innovation diffusion theory, and the unified theory of acceptance and use of technology 2, a comprehensive model is constructed. The model also incorporates external variables such as perceived value (PV), perceived price (PP), social influence, hedonic motivation (HM), and cultural experience (CE). Additionally, self-innovation is considered as a key moderator to explore its role in moderating the relationships between PV, PP, and user perceived behavioral control. Using 526 valid samples, this study employs structural equation modeling to conduct exploratory factor analysis and confirmatory factor analysis, and further verifies the importance of variables through artificial neural networks. The findings indicate that behavioral attitude, HM, PP, PV, and generative quality are the primary factors influencing user purchase intention. In the decision-making process, users not only consider the price and quality of the products but also place significant importance on the pleasurable experience and cultural uniqueness they offer. This study extends the theoretical application of AIGC in the field of cultural and creative consumption, enriches the user behavior research model, and provides practical insights for companies to optimize AI-generated cultural products, enhance user experience, and improve market acceptance.

Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km 2 across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively. Measurement(s) building rooftop area Technology Type(s) computational modeling technique Sample Characteristic - Environment city Sample Characteristic - Location China

The NLR family pyrin domain containing 3 (NLRP3) inflammasome in microglia is intimately linked to the pathogenesis of Alzheimer’s disease (AD). Although NLRP3 inflammasome activity is regulated by cellular metabolism, the underlying mechanism remains elusive. Here, we found that under the pathological conditions of AD, the activation of NLRP3 inflammasome in microglia is accompanied by increased glutamine metabolism. Suppression of glutaminase, the rate limiting enzyme in glutamine metabolism, attenuated the NLRP3 inflammasome activation both in the microglia of AD mice and cultured inflammatory microglia. Mechanistically, inhibiting glutaminase blocked the anaplerotic flux of glutamine to the tricarboxylic acid cycle and amino acid synthesis, down-regulated mTORC1 signaling by phosphorylating AMPK, which stimulated mitophagy and limited the accumulation of intracellular reactive oxygen species, ultimately prevented the activation of NLRP3 inflammasomes in activated microglia during AD. Taken together, our findings suggest that glutamine metabolism regulates the activation of NLRP3 inflammasome through mitophagy in microglia, thus providing a potential therapeutic target for AD treatment.

Background Accurate identification of protein-protein interaction sites (PPIS) is critical for elucidating biological mechanisms and advancing drug discovery. However, existing methods still face significant challenges in leveraging structural information, including inadequate equivariant modeling, coarse graph representations, and limited multimodal fusion strategies. Results In this study, we propose a novel multimodal and multiscale deep learning framework, EDG-PPIS, that achieves efficient PPIS prediction by jointly enhancing structural and geometric representations. Specifically, a 3D equivariant graph neural network (LEFTNet) is employed to capture the global spatial geometry of proteins. For structural modeling, a dual-scale graph neural network is constructed to extract protein structural features from both local and remote perspectives. Finally, an attention mechanism is utilized to dynamically fuse structural and geometric features, enabling cross-modal integration. Experimental results demonstrate that EDG-PPIS achieves superior performance across multiple benchmark datasets. Conclusions EDG-PPIS provides an effective and robust computational tool for target identification and protein function analysis, addressing existing challenges in PPIS prediction and offering a promising approach for advancing the understanding of PPIS.

Aqueous activities on Mars have gradually declined since the Noachian (>3.7 Ga). Although water can be stored in the subsurface during the latest epochs, geomorphological evidence is still limited. In this study, we used in situ imaging and spectral data acquired by China's Zhurong rover, as well as high‐resolution remote‐sensing data, to investigate the transverse aeolian ridges (TARs) in the Zhurong landing region of Utopia Planitia. A two‐stage evolutionary scenario of the TARs is proposed and polygonal features with hydrated minerals are identified for the first time on the surface of Martian TARs. We discussed the possible formation mechanisms of the polygonal features, and proposed that they could be related to recent aqueous activity and atmosphere‐surface water exchange on Mars, which sheds light on the hydrological cycle of Mars in current cold and dry climate. Plain Language Summary The history of water on the surface of Mars has been studied for a long time. Since about 3.7 billion years ago, the role of water has gradually declined. Although the existence of subsurface ice on present‐day Mars has been confirmed, evidence for surface water is still limited. Transverse aeolian ridges (TARs), a kind of ripple‐like aeolian landform, are widely distributed on Mars and usually thought to be active within the last ∼3 million years. They are also identified in southern Utopia Planitia, the landing region of China's Mars exploration rover Zhurong. We analyzed the morphology and evolution of the TARs in the Zhurong landing region, and found some polygonal features with hydrated minerals such as gypsum on the surface of the latest‐formed TARs. We discussed the possible origins of these polygons, and proposed that they represent very recent aqueous activity on the Martian surface, which will help us better understand the hydrological cycle on current Mars. Key Points Morphology and evolution of the transverse aeolian ridges (TARs) in the Zhurong landing region are studied Polygonal features with hydrated minerals are identified on some of the TARs investigated by the Zhurong rover The polygons could be related to very recent aqueous activity and atmosphere‐surface water exchange on Mars

The stock market is characterized by its high nonlinearity and complexity, making traditional methods ineffective in capturing its nonlinear features and complex market dynamics. This paper proposes a novel stock price forecasting model—the Variational Mode Decomposition—Triangulated Maximally Filtered Graph—Long Short-Term Memory (VMD–TMFG–LSTM) combined model—aimed at improving prediction accuracy, stability, and computational efficiency. The proposed model first employs Variational Mode Decomposition (VMD) to decompose the stock price time series into multiple smooth intrinsic mode functions (IMFs), reducing data complexity and mitigating noise interference. Subsequently, the TMFG algorithm is utilized for feature selection, simplifying the input data and accelerating the iterative convergence process. Finally, the filtered features are modeled and predicted using a Long Short-Term Memory (LSTM) network. Experimental results demonstrate that the VMD–TMFG–LSTM model significantly outperforms AutoRegressive Integrated Moving Average (ARIMA), Neural Network (NN), Deep Neural Network (DNN), Convolutional Neural Network (CNN), as well as single LSTM, TMFG–LSTM, and VMD–LSTM models in forecasting the closing prices of multiple stocks. Specifically, for Shanghai International Airport Co., Ltd. (sh600009), the VMD–TMFG–LSTM model achieves a 69.76% reduction in Root Mean Squared Error (RMSE), a 71.41% reduction in Mean Absolute Error (MAE), a 46.28% reduction in runtime, and an improvement of 0.2184 in R-squared (R 2 ), indicating significantly higher prediction accuracy. In conclusion, the combined model proposed in this paper enhances the accuracy, efficiency, and stability of stock price prediction, providing a robust and efficient solution for forecasting stock market trends.

Photovoltaic (PV) modules are exposed to the outside, which is affected by radiation, the temperature of the PV module back-surface, relative humidity, atmospheric pressure and other factors, which makes it difficult to test and analyze the performance of photovoltaic modules. Traditionally, the equivalent circuit method is used to analyze the performance of PV modules, but there are large errors. In this paper—based on machine learning methods and large amounts of photovoltaic test data—convolutional neural network (CNN) and multilayer perceptron (MLP) neural network models are established to predict the I–V curve of photovoltaic modules. Furthermore, the accuracy and the fitting degree of these methods for current–voltage (I–V) curve prediction are compared in detail. The results show that the prediction accuracy of the CNN and MLP neural network model is significantly better than that of the traditional equivalent circuit models. Compared with MLP models, the CNN model has better accuracy and fitting degree. In addition, the error distribution concentration of CNN has better robustness and the pre-test curve is smoother and has better nonlinear segment fitting effects. Thus, the CNN is superior to MLP model and the traditional equivalent circuit model in complex climate conditions. CNN is a high-confidence method to predict the performance of PV modules.

A major challenge in maize ( Zea mays ) production is to achieve high grain yield (yield hereafter) by improving resource use efficiency. Using a dataset synthesized from 83 peer-reviewed articles, this study mainly investigated the effects of water and/or nitrogen (N) input on maize yield, water productivity (WP), and N use efficiency (NUE); and evaluated the effects caused by planting density, environmental (temperature, soil texture), and managerial factors (water and/or N input). The input of water increased maize yield, WP, and NUE only when the input was less than 314, 709, and 311 mm, respectively; input of N increased maize yield, WP, and NUE until input was greater than 250, 128, and 196 kg ha −1 , respectively. Additionally, results of the mixed-effects model and random forest analysis suggested that mean annual temperature (MAT) was the most critical factor for narrowing gaps (between the actual and attainable variable, which was indicated as response ratio of the treatment relative to the control) of yield ( RR Y ), WP ( RR WP ), and NUE ( RR NUE ), respectively. Specifically, RR Y , RR WP , or RR NUE were negatively correlated to MAT when MAT was higher than 15 °C. Additionally, the structural equation model showed that water input and RR WP with the higher coefficient were more important than N input and RR NUE in improving RR Y . These findings provide new insights into the causes and limitations of global maize production and offer some guidances for water and/or N managements.