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Under the guidance of energy-saving and emission reduction goals, a low-carbon economic operation method for integrated energy systems (IES) has been proposed. This strategy aims to enhance energy utilization efficiency, bolster equipment operational flexibility, and significantly cut down on carbon emissions from the IES. Firstly, a thorough exploration of the two-stage operational framework of Power-to-Gas (P2G) technology is conducted. Electrolyzers, methane reactors, and hydrogen fuel cells (HFCs) are introduced as replacements for traditional P2G equipment, with the objective of harnessing the multiple benefits of hydrogen energy. Secondly, a cogeneration and HFC operational strategy with adjustable heat-to-electricity ratio is introduced to further enhance the IES’s low-carbon and economic performance. Finally, a step-by-step carbon trading mechanism is introduced to effectively steer the IES towards carbon emission control.

Owing to the fragile ecological environment and the prominent human–land conflict relationship, the competition between land multifunctionality in the karst region has increased. In this context, the coordination and stability of production–living–ecological space (PLES) have been severely affected, and the scientific diagnosis of PLES conflict characteristics can provide an important basis for optimizing national land allocation. Using land use data, this study used a spatial conflict measurement model to examine the spatial and temporal variations of PLES conflicts across mountain–basin areas of various geomorphological in karst types. Meanwhile, the factors influencing their evolution were also investigated. The study results showed that the ecological space (ES) area was the largest in the study area and had an increasing trend from 2010 to 2020. PLES conflicts were dominated by stable and controllable, and light conflicts, and the overall conflict level had increased. Influenced by the natural environment, socioeconomics, and policies, there were significant differences in the development of PLES conflicts in the mountain–basin areas. The conflict level in the basin area was higher than in the mountain area. The intensity of PLES conflicts in the mountain–basin areas in the karst valleys geomorphic types and in the mountain areas in the karst fault basin showed a decreasing trend. The level of conflicts in the mountain–basin areas in the karst plateau, karst peak–cluster depression, karst trough valley geomorphic and in the basin area of the karst fault basin geomorphic type showed an increasing trend. There were also common evolutionary characteristics for PLES conflicts in mountain–basin areas. The findings have provided a reference to support differentiated land management and decision-making in karst areas, which can contribute to the strategic sustainable development at the regional level.

Acidic bacterial biofilms-associated enamel white spot lesions (WSLs) are one of the hallmarks of early caries, causing demineralization and decomposition of dental hard tissues. Therefore, to effectively prevent and treat WSLs, it is important to inhibit the activity of cariogenic bacteria while promoting the remineralization of demineralized enamel. Amorphous calcium phosphate (ACP) favors hard tissue remineralization due to its biological activity and ability to release large amounts of Ca2+ and PO43-. However, ACP-based biomineralization technology is not effective due to its lack of antimicrobial properties. Here, carboxymethyl chitosan (CMCS) was employed as a reducing agent and stabilizer, and dual-functional nanohybrids CMCS/AuNPs/ACP with biofilm resistance and mineralization properties were successfully synthesized. The addition of AuNPs enhances the antimicrobial activity and participates in regulating the formation of hydroxyapatite (HAp). The nanohybrids exhibited significant destructive effects against cariogenic bacteria and their biofilms and showed bactericidal activity under bacteria-induced acidic conditions. More importantly, this nanohybrids showed superior results in promoting the remineralization of demineralized enamel, compared to fluoride and CMCS/ACP in vitro. The CMCS/AuNPs/ACP nanohybrids not only reverse the cariogenic microenvironment at the microbial level, but also promote self-repairing of enamel WSLs regarding the microstructure. The present work offers a theoretical and experimental basis for using the CMCS/AuNPs/ACP nanohybrids as a potential dual-functional agent for the clinical treatment of enamel WSLs.

Mutation of the MAPK7 gene was related to human scoliosis. Mapk7 regulated the development of limb bones and skulls in mice. However, the role of MAPK7 in vertebral development is still unclear. In this study, we constructed Col2a1-cre; Mapk7f/f transgenic mouse model to delete Mapk7 in cartilage, which displayed kyphosis and osteopenia. Mechanistically, Mapk7 loss decreased MEF2C expression and thus activated PTEN to oppose PI3K/AKT signaling in vertebral growth plate chondrocytes, which impaired chondrocyte hypertrophy and attenuated vertebral ossification. In vivo, systemic pharmacological activation of AKT rescued impaired chondrocyte hypertrophy and alleviated mouse vertebral defects caused by Mapk7 deficiency. Our study firstly clarified the mechanism by which MAPK7 was involved in vertebral development, which might contribute to understanding the pathology of spinal deformity and provide a basis for the treatment of developmental disorders of the spine.

With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few samples of power supply risk and it is difficult to identify the power supply risk by using conventional deep learning methods. In order to achieve high accuracy of self-provided power supply risk identification with small samples, this paper proposes a combination of transfer learning, convolutional block attention module (CBAM), and convolutional neural network (CNN) to identify the risk of self-provided power supply in an active distribution network. Firstly, in order to be able to further identify whether or not a risk will be caused based on completing the identification of the faulty line, we propose that it is necessary to identify whether or not the captive power supply on the faulty line is in operation. Second, in order to achieve high-precision identification and high-efficiency feature extraction, we propose to embed the CBAM into a CNN to form a CBAM-CNN model, so as to achieve high-efficiency feature extraction and high-precision risk identification. Finally, the use of transfer learning is proposed to solve the problem of low risk identification accuracy due to the small number of actual fault samples. Simulation experiments show that compared with other methods, the proposed method has the highest recognition accuracy and the best effect, and the risk recognition accuracy of active distribution network backup power is high in the case of fewer samples.

Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and temporal distribution. Initially, the parking generation rate model predicts parking demand, establishing the spatial and temporal distribution model for electric vehicle parking needs across various vehicle types and destinations. Subsequently, analyzing daily mileage and parking demand distributions of electric vehicles informs charging demand assessment. Using the Monte Carlo simulation method, large-scale electric vehicle behaviors in different spatial and temporal contexts—parking, driving, and charging—are simulated to predict charging load distributions. Optimization of electric vehicle charging and discharging enhances grid stability, cost management, charging efficiency, and user experience, supporting smart grid development. Furthermore, charging load forecasting examples under diverse scenarios validate the model’s feasibility and effectiveness.

As an advanced technology that efficiently aggregates and optimizes renewable energy, controllable loads, and energy storage systems, virtual power plants (VPPs) can effectively promote the green and low-carbon transformation of power systems. A comprehensive assessment of VPPs is important for VPP investment and operation. However, most of the existing evaluation methods focus on the reliability, economy, and mobilizability of VPPs. In this paper, in order to better address the characteristics of demand response-oriented VPPs, three aspects of VPP operation indices, new energy indices, and demand response indices are analyzed. In this manner, it is possible to meet the principles of the construction of VPP evaluation system and also to measure the effect of demand response of the VPP. Then, on the basis of AdaBoost algorithm, combined with back propagation (BP) neural network for the evaluation and classification of demand response-oriented VPPs. The entropy value method and gray correlation are also compared to validate the superiority of the proposed method.

Rockburst is an extremely hazardous geological disaster. In order to accurately predict the hazardous degree of rockbursts, this paper proposes eight new classification models for predicting the intensity level of rockbursts based on intelligent optimisation algorithms and deep learning techniques and collects 287 sets of real rockburst data to form a sample database, in which six quantitative indicators are selected as feature parameters. In order to validate the effectiveness of the constructed eight machine learning prediction models, the study selected Accuracy, Precision, Recall and F1 Score to evaluate the prediction performance of each model. The results show that the NGO-CNN-BiGRU-Attention model has the best prediction performance, with an accuracy of 0.98. Subsequently, engineering validation of the model is carried out using eight sets of real rockburst data from Daxiangling Tunnel, and the results show that the model has a strong generalisation ability and can satisfy the relevant engineering applications. In addition, this paper also uses SHAP technology to quantify the impact of different factors on the rockburst intensity level and found that the elastic strain energy index and stress ratio have the greatest impact on the rockburst intensity level.

Abstract The harmonious functioning of growth plate chondrocytes is crucial for skeletogenesis. These cells rely on an appropriate intensity of glycolysis to maintain survival and function in an avascular environment, but the underlying mechanism is poorly understood. Here we show that Hnrnpk orchestrates growth plate development by maintaining the appropriate intensity of glycolysis in chondrocytes. Ablating Hnrnpk causes the occurrence of dwarfism, exhibiting damaged survival and premature differentiation of growth plate chondrocytes. Furthermore, Hnrnpk deficiency results in enhanced transdifferentiation of hypertrophic chondrocytes and increased bone mass. In terms of mechanism, Hnrnpk binds to Hif1a mRNA and promotes its degradation. Deleting Hnrnpk upregulates the expression of Hif1α, leading to the increased expression of downstream glycolytic enzymes and then exorbitant glycolysis. Our study establishes an essential role of Hnrnpk in orchestrating the survival and differentiation of chondrocytes, regulating the Hif1α-glycolysis axis through a post-transcriptional mechanism during growth plate development.

Abstract The intervertebral disc (IVD) acts as a fibrocartilaginous joint to anchor adjacent vertebrae. Although several studies have demonstrated the cellular heterogeneity of adult mature IVDs, a single‐cell transcriptomic atlas mapping early IVD formation is still lacking. Here, the authors generate a spatiotemporal and single cell‐based transcriptomic atlas of human IVD formation at the embryonic stage and a comparative mouse transcript landscape. They identify two novel human notochord (NC)/nucleus pulposus (NP) clusters, SRY‐box transcription factor 10 (SOX10)+ and cathepsin K (CTSK)+, that are distributed in the early and late stages of IVD formation and they are validated by lineage tracing experiments in mice. Matrisome NC/NP clusters, T‐box transcription factor T (TBXT)+ and CTSK+, are responsible for the extracellular matrix homeostasis. The IVD atlas suggests that a subcluster of the vertebral chondrocyte subcluster might give rise to an inner annulus fibrosus of chondrogenic origin, while the fibroblastic outer annulus fibrosus preferentially expresseds transgelin and fibromodulin . Through analyzing intercellular crosstalk, the authors further find that notochordal secreted phosphoprotein 1 (SPP1) is a novel cue in the IVD microenvironment, and it is associated with IVD development and degeneration. In conclusion, the single‐cell transcriptomic atlas will be leveraged to develop preventative and regenerative strategies for IVD degeneration.

Learning patterns are crucial for predicting student dropout in educational settings, providing insights into students’ behaviors and motivations. However, existing mainstream dropout prediction models have limitations in effectively mining these learning patterns and cannot mine these learning patterns in large-scale, distributed educational datasets. In this study, we analyze the representations of mainstream models and identify their inability to capture students’ distinct learning patterns and personalized variations across courses. Addressing these challenges, our study adopts a federated learning approach, tailoring the analysis to leverage distributed data while maintaining privacy and decentralization. We introduce the Federated Learning Pattern Aware Dropout Prediction Model (FLPADPM), which utilizes a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term memory (LSTM) layer within a federated learning framework. This model is designed to effectively capture nuanced learning patterns and adapt to variations across diverse educational settings. To evaluate the performance of LPADPM, we conduct an empirical evaluation using the KDD Cup 2015 and XuetangX datasets. Our results demonstrate that LPADPM outperforms state-of-the-art models in accurately predicting student dropout behavior. Furthermore, we visualize the representations generated by LPADPM, which confirm its ability to effectively mine learning patterns in different courses. Our results showcase the model’s ability to capture and analyze learning patterns across various courses and institutions within a federated learning context.

Knowledge proficiency refers to the extent to which students master knowledge and reflects their cognitive status. To accurately assess knowledge proficiency, various pedagogical theories have emerged. Bloom’s cognitive theory, proposed in 1956 as one of the classic theories, follows the cognitive progression from foundational to advanced levels, categorizing cognition into multiple tiers including “knowing”, “understanding”, and “application”, thereby constructing a hierarchical cognitive structure. This theory is predominantly employed to frame the design of teaching objectives and guide the implementation of teaching activities. Additionally, due to the large number of students in real-world online education systems, the time required to calculate knowledge proficiency is significantly high and unacceptable. To ensure the applicability of this method in large-scale systems, there is a substantial demand for the design of a parallel prediction model to assess knowledge proficiency. The research in this paper is grounded in Bloom’s Cognitive theory, and a Bloom Cognitive Diagnosis Parallel Model (BloomCDM) for calculating knowledge proficiency is designed based on this theory. The model is founded on the concept of matrix decomposition. In the theoretical modeling phase, hierarchical and inter-hierarchical assumptions are initially established, leading to the abstraction of the mathematical model. Subsequently, subject features are mapped onto the three-tier cognitive space of “knowing”, “understanding”, and “applying” to derive the posterior distribution of the target parameters. Upon determining the objective function of the model, both student and topic characteristic parameters are computed to ascertain students’ knowledge proficiency. During the modeling process, in order to formalize the mathematical expressions of “understanding” and “application”, the notions of “knowledge group” and “higher-order knowledge group” are introduced, along with a parallel method for identifying the structure of higher-order knowledge groups. Finally, the experiments in this paper validate that the model can accurately diagnose students’ knowledge proficiency, affirming the scientific and meaningful integration of Bloom’s cognitive hierarchy in knowledge proficiency assessment.

When the T-connected transmission line fails, there is a certain relationship between the phases of the three-terminal power frequency fault current, which can be applied to protect the current differential of the T-connected line. In this paper, using the MATLAB/Simulink electric power system simulation software, when the T-connected transmission line is faulty, the power frequency fault current variation law monitored at the installation point is deeply studied, and the judgment method of the T-connected transmission line fault branch based on the power frequency current is proposed.

Governments, enterprises, and customers have become more concerned about environmental protection. Following the world’s largest carbon trading market (EU ETS), China has also implemented a market-based carbon trading mechanism (CAT) to reduce CO2. Simultaneously, customers have low-carbon preferences for environmental products. Thus, the enterprises’ strategic decisions and collaboration modes have changed. This article develops the Stackelberg game model to explore the impacts of CAT and customers’ low-carbon preference on the carbon emission reduction and promotion strategies in a retailer-led supply chain (such as RT-Mart, Walmart, Amazon, etc.). In this model, the retailer decides whether to promote environmentally safe products and the manufacturer decides whether to reduce CO2. We find that carbon trading market price and customers’ low-carbon preference are key factors influencing the retail price, total carbon emissions, and social welfare. Interestingly, there is not always a positive correlation between customers’ low-carbon preference and social welfare. To achieve Pareto improvement of social welfare, manufacturers and retailers require co-optimization. Theoretically, our research enriches the research streams of the CAT policy and socially responsible operations of the supply chain. Moreover, managerial insights are provided for retailer-led supply chain stakeholders and emission reduction regulators, which contribute to enhancing the social and environmental benefits of the supply chains.

In online intelligent education systems, to offer proactive studying services to students (e.g., learning path recommendation), a crucial demand is to track students’ knowledge mastery levels over time. However, existing works ignore the impact of learning transfer on knowledge tracing and only track knowledge proficiency. Knowledge proficiency alone cannot fully reflect students’ knowledge mastery levels. A student’s knowledge structure (the similarities and differences within knowledge concepts) and abstract principle mastery level (common attributes among knowledge concepts, such as learning methods) also need to be tracked. To this end, we propose a novel multilevel Knowledge Tracing model with Learning Transfer (KTLT) to track students’ knowledge mastery levels. First, we clarify the relationships among abstract principles, knowledge structure, and knowledge proficiency by utilizing the learning transfer theory in educational psychology. Then, we associate each problem with a knowledge vector in which each element represents an explicit knowledge concept by leveraging educational priors (i.e., a Q-matrix). Correspondingly, each student is represented as a knowledge vector (knowledge proficiency) and an abstract principle vector (abstract principle mastery). Given a student’s knowledge and abstract principle vector over time, we use the learning and forgetting curve as priors to capture the student’s knowledge proficiency and abstract principle mastery level over time. Furthermore, we embed the knowledge concept by using a student’s abstract principle mastery level and obtain a personalized knowledge relevance matrix (a student’s knowledge structure) by calculating the cosine similarity among the knowledge embedding results. Finally, we design a probabilistic matrix factorization framework by combining student and problem priors for tracking a student’s knowledge mastery. Extensive experiments on two real-world datasets demonstrate both the effectiveness and explanatory ability of the KTLT.

AbstractBackground Acute respiratory distress syndrome (ARDS) is characterized by severe local and systemic inflammation. Ac2-26, an Annexin A1 Peptide, can reduce the lung injury induced by reperfusion via the inhibition of inflammation. The present study aims to evaluate the effect and mechanism of Ac2-26 in ARDS.Methods Thirty-two rats were anaesthetized and randomized into four groups: sham (S), ARDS (A), ARDS/Ac2-26 (AA), and ARDS/Ac2-26/BOC-2 (AAB) groups. Rats in the S group received saline for intratracheal instillation, while rats in the other three groups received endotoxin for intratracheal instillation, in order to prepare the ARDS and inject the saline, Ac2-26, and Ac2-26 combined with BOC-2. After 24 h, the PaO2/FiO2 ratio was calculated. The lung tissue wet-to-dry weight ratio and the protein level in bronchoalveolar lavage fluid (BALF) were tested. Then, the cytokines in BALF and serum, and the inflammatory cells in BALF were investigated. Afterwards, the oxidative stress response and histological injury was evaluated. Subsequently, the epithelium was cultured and analyzed to estimate the effect of Ac2-26 on apoptosis.Results Compared to the S group, all indexes worsened in the A, AA, and AAB groups. Furthermore, compared to the S group, Ac2-26 significantly improved the lung injury and alveolar-capillary permeability, and inhibited the oxidative stress response. In addition, Ac2-26 reduced the local and systemic inflammation through the regulation of pro- and anti-inflammatory cytokines, and the decrease in inflammatory cells in BALF. Moreover, Ac2-26 inhibited the epithelium apoptosis induced by LPS through the modulation of apoptosis-regulated proteins. The protective effect of Ac2-26 on ARDS was partially reversed by the FPR inhibitor, BOC-2.Conclusion Ac2-26 reduced the lung injury induced by LPS, promoted alveolar-capillary permeability, ameliorated the local and systemic inflammation, and inhibited the oxidative stress response and apoptosis. The protection of Ac2-26 on ARDS was mainly dependent on the FPR pathway.

Location-based social networks (LBSNs) have collected massive geo-tagged information, enabling the derivation of user preference for point of interests (POIs) in support of personalized recommendation. The existing embedding techniques deal with multiple factors by embedding a separate model for each factor. As a result, the interaction amongst various factors cannot be captured properly. In addition, we notice that the effectiveness of personalized recommendation is closely related to the current time and location. It is obvious that users would check into a POI which fits their interests, even if the current location is far away from the POI or the time is inappropriate. Therefore, it is necessary to recommend the right POI according to the time and geographic location of the user. In other words, it is necessary to predict the most likely visiting event, including users, POI, event time, and event location. In this paper, we propose a probabilistic embedding model called Topic And Region Embedding (TARE), which embeds events by simulating the users’ decision-making process. The results of TARE not only take various factors and their interaction into consideration but also consider the time and geographic location of events. Extensive experiments on three location-based social network datasets show that TARE achieves better performance in recommendation accuracy than existing state-of-the-art methods.

This study is prompted by the reality that the forging velocity significantly impacts the temperature and phases of titanium forged parts, further directly affecting the ultimate mechanical properties. In present work, a 3D FE model combined with phase transition equations is established to predict the characteristics of temperature and phases in Ti-6Al-4V blade forging procedure. Then, based on four evaluation indexes, the effect of forging velocity on temperature and phases in terms of extreme values, general levels and distribution uniformities are analyzed quantitatively. Finally, the validation of simulation results is confirmed by comparing with experimental ones. The results reveal that unlike little influence on lamellar α + β phase, enhancing the forging velocity can obviously increase the general levels of temperature and β phase, while decrease the distribution uniformities of α and β phases within the forged blade.

BackgroundTelemedicine plays an important role in the management of inflammatory bowel disease (IBD), particularly during a pandemic such as COVID-19. However, the effectiveness and efficiency of telemedicine in managing IBD are unclear. ObjectiveThis systematic review and meta-analysis aimed to compare the impact of telemedicine with that of standard care on the management of IBD. MethodsWe systematically searched the PubMed, Cochrane Library, EMBASE, Web of Science, and Scopus databases on April 22, 2020. Randomized controlled trials comparing telemedicine with standard care in patients with IBD were included, while conference abstracts, letters, reviews, laboratory studies, and case reports were excluded. The IBD-specific quality of life (QoL), disease activity, and remission rate in patients with IBD were assessed as primary outcomes, and the number of in-person clinic visits per patient, patient satisfaction, psychological outcome, and medication adherence were assessed as secondary outcomes. Review Manage 5.3 and Stata 15.1 were used for data analysis. ResultsA total of 17 randomized controlled trials (2571 participants) were included in this meta-analysis. The telemedicine group had higher IBD-specific QoL than the standard care group (standard mean difference 0.18, 95% CI 0.01 to 0.34; P.03). The number of clinic visits per patient in the telemedicine group was significantly lower than that in the standard care group (standard mean difference −0.71, 95% CI −1.07 to −0.36; P

Knowledge tracing, which is used to predict students’ performance based on their previous practices, has attracted many researchers’ attention. Especially in this rising period of intelligent education, many knowledge tracing methods have been developed. However, most of the existing knowledge tracing methods focus on the personality of practices and knowledge concepts but ignore the contexts related to the studying process. In this paper, we propose a context-based knowledge tracing model, which combines students’ historical performance and their studying contexts during knowledge mastery. To be specific, we first define five studying contexts for performance prediction. The basic context is the current knowledge state of a student, which is described by their practice sequences. Then, a QR-matrix is defined to represent the relationship among questions, knowledge concepts, and responses, which describes the contexts of questions and knowledge. Furthermore, an improved LSTM model is proposed to capture the context of students’ memory and forgetness, and a multi-head attention mechanism is designed to capture the context of students’ behaviors. Finally, based on the captured contexts, the prediction model ContextKT is established. Our prediction model is evaluated on two real educational datasets. The experimental results show our model is effective and efficient in student performance prediction, and it outperforms the other existing methods.

Background and aims: Lung cancer patients suffer from deterioration in their physical and psychological function, which exerts a negative influence on their quality of life (QOL). Telemedicine has been proven to be an effective intervention for patients with several chronic diseases. The aim of this systematic review and meta-analysis was to investigate the efficacy of telemedicine in improving QOL in lung cancer patients. Methods: PubMed, Cochrane Library, EMBASE, Web of Science and Scopus databases were searched for randomized controlled trials that investigated the effectiveness of telemedicine in lung cancer patients. Review Manager 5.3 and Stata 15.1 were used to perform data analysis. Results: Our meta-analysis included eight clinical trials with a total of 635 lung cancer patients. The results showed that the telemedicine group had significantly higher QOL than the usual care group [standard mean difference (SMD) 0.96, 95% confidence interval (CI) 0.29–1.63, I 2 = 91%]. In addition, the telemedicine group had lower anxiety (SMD −0.44, 95% CI −0.66 to −0.23, I 2 = 3%) and depression scores (SMD −0.48, 95% CI −0.91 to −0.05, I 2 = 66%) than the usual care group. However, no significant differences were found in fatigue and pain outcomes between the two groups. Conclusion: Telemedicine may be an effective method of improving QOL in lung cancer patients and the further development and use of telemedicine care is recommended.

This paper considers the estimation of ruin probability in an insurance risk model with stochastic premium income. We first show that the ruin probability can be approximated by the complex Fourier series (CFS) expansion method. Then, we construct a nonparametric estimator of the ruin probability and analyze its convergence. Numerical examples are also provided to show the efficiency of our method when the sample size is finite.