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In order to enhance the utilization rate of waste powder and improve the sulfate corrosion resistance of limestone powder concrete, the evolution law of compressive strength, porosity, sulfate corrosion resistance of limestone powder and waste glass powder mixed concrete with different proportions was studied. It is found through that the early strength of limestone powder concrete cannot be enhanced by waste glass powder, but its late strength can be improved (curing age of 90d). The compressive strength can be improved by adding 5% ~ 15% waste glass powder to the concrete with 10% limestone powder or 5% waste glass powder to the concrete with 20% limestone powder. The pozzolanic activity of waste glass powder is the main factor to increase the late strength of limestone powder concrete. Waste glass powder can be used to improve the sulfate corrosion resistance of limestone powder concrete, and the best combination is 10% limestone powder concrete mixed with 5% waste glass powder. The corrosion products showed that waste glass powder could improve the sulfate resistance of limestone powder concrete from the following three aspects: consuming part of CH, reducing the degree of sulfate corrosion reaction, and improving the limestone powder to inhibit the later hydration reaction of concrete. This study provides a valuable reference for the further utilization of limestone powder and waste glass powder.

Normally, an obvious antagonism exists between pain and itch. In normal conditions, painful stimuli suppress itch sensation, whereas pain killers often generate itch. Although pain and itch are mediated by separate pathways under normal conditions, most chemicals are not highly specific to one sensation in chronic pathologic conditions. Notably, in patients with neuropathic pain, histamine primarily induces pain rather than itch, while in patients with atopic dermatitis, bradykinin triggers itch rather than pain. Accordingly, repetitive scratching even enhances itch sensation in chronic itch conditions. Physicians often prescribe pain relievers to patients with chronic itch, suggesting common mechanisms underlying chronic pain and itch, especially peripheral and central sensitization. Rather than separating itch and pain, studies should investigate chronic itch and pain including neuropathic and inflammatory conditions. Here, we reviewed chronic sensitization leading to chronic pain and itch at both peripheral and central levels. Studies investigating the connection between pain and itch facilitate the development of new therapeutics against both chronic dysesthesias based on the underlying pathophysiology.

Existing methods plan the distribution network and sub-transmission network separately. This paper proposes a collaborative renewable energy resource siting and sizing planning method for distribution and sub-transmission networks to increase the renewable energy ratio in high-load density industrial parks and promote the hosting capacity of the power grid. First, to accurately measure planning effectiveness, a renewable energy ratio calculation method is proposed, which comprehensively considers the contributions of green electricity from the power grid and renewable energy generation inside and outside the industrial park. Second, a collaborative planning model is proposed, which optimizes access points and access capacity in the distribution and sub-transmission networks for renewable energy around the park. The net load is better matched with the output of renewable energy outside the park through demand response, thereby maximizing the utilization of the park load to host more renewable energy. Finally, the proposed method is verified in a real industrial park. The method outperforms traditional planning methods in terms of renewable energy ratio in the park and renewable energy hosting capacity outside the park.

The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization.

Accurate photovoltaic power prediction is of great significance to the stable operation of the electric power system with renewable energy as the main body. In view of the different influence mechanisms of meteorological factors on photovoltaic power generation in different irradiation intervals and that the data-driven algorithm has the problem of regression to the mean, in this article, a prediction method based on irradiation interval distribution and Transformer-long short-term memory (IID-Transformer-LSTM) is proposed. Firstly, the irradiation interval distribution is calculated based on the boxplot. Secondly, the distributed data of each irradiation interval is input into the Transformer-LSTM model for training. The self-attention mechanism of the Transformer is applied in the coding layer to focus more important information, and LSTM is applied in the decoding layer to further capture the potential change relationship of photovoltaic power generation data. Finally, sunny data, cloudy data, and rainy data are selected as test sets for case analysis. Through experimental verification, the method proposed in this article has a certain improvement in prediction accuracy compared with the traditional methods under different weather conditions. In the case of local extrema and large local fluctuations, the prediction accuracy is clearly improved.

Angiopoietin-1/Tek signaling is a critical regulator of blood vessel development, with conventional knockout of angiopoietin-1 or Tek in mice being embryonically lethal due to vascular defects. In addition, angiopoietin-1 is thought to be required for the stability of mature vessels. Using a Cre-Lox conditional gene targeting approach, we have studied the role of angiopoietin-1 in embryonic and adult vasculature. We report here that angiopoietin-1 is critical for regulating both the number and diameter of developing vessels but is not required for pericyte recruitment. Cardiac-specific knockout of angiopoietin-1 reproduced the phenotype of the conventional knockout, demonstrating that the early vascular abnormalities arise from flow-dependent defects. Strikingly, deletion in the entire embryo after day E13.5 produced no immediate vascular phenotype. However, when combined with injury or microvascular stress, angiopoietin-1 deficiency resulted in profound organ damage, accelerated angiogenesis, and fibrosis. These findings redefine our understanding of the biological roles of angiopoietin-1: it is dispensable in quiescent vessels but has a powerful ability to modulate the vascular response after injury.

ObjectiveTo propose and test an innovative model by integrating the Unified Theory of Acceptance and Use of Technology and Knowledge-Attitude-Practice model to explain the mechanisms influencing the adoption of digital health technologies by elderly patients with chronic diseases from the perspective of both internal and external factors, promoting the acceptance and utilisation of digital health technologies among elderly chronically ill patients.Study designA face-to-face questionnaire survey was conducted from July to September 2023.Study settingThe study was conducted in 12 medical institutions in Shanghai, including 6 tertiary hospitals, 3 secondary hospitals and 3 community hospitals.Participants1222 participants aged 60 years or more, diagnosed with one or more of the following chronic diseases: essential hypertension, type 2 diabetes, coronary atherosclerotic heart disease, stroke and chronic obstructive pulmonary disease, were involved in the study using convenience sampling. Critically ill emergency patients and those who were involved in medical disputes were excluded.Outcome measureThe behavioural intention and usage behaviour of older patients with chronic diseases to use digital health technologies.ResultsThe explanatory power of the proposed model for behavioural intention was 72.9%. There is a significant negative association between technology anxiety and the intention to use digital health technologies among older patients with chronic diseases (β=−0.224, p<0.001); effort expectancy (β=0.530, p<0.001) and performance expectancy (β=0.193, p<0.001) were also significantly associated with intention to use digital health technologies. Men (β=−0.104, p=0.016), relatively younger (β=−0.061, p=0.005), with experience in using digital health technologies (β=−0.452, p<0.001) were more likely to translate behavioural intention into use behaviour.ConclusionsAcceptance of digital health technologies among older patients with chronic diseases was associated with a combination of internal and external factors, with the former playing a dominant role. These valuable findings provided insights and inspiration for improving digital health technologies acceptance and utilisation among older patients with chronic diseases.

Background Hylurgus ligniperda , a major international forestry quarantine pest, was recently found to have invaded and posed a serious threat to the Pinus forests of the Jiaodong Peninsula in China. Continuous monitoring and vigilance of the early population is imperative, and rapid molecular detection technology is urgently needed. We focused on developing a single-gene-based species-specific PCR (SS-PCR) method. Results We sequenced and assembled the mitochondrial genome of H. ligniperda to identify suitable target genes. We identified three closely related species for detecting the specificity of SS-PCR through phylogenetic analysis based on 13 protein-coding genes (PCGs). Subsequently, we analyzed the evolution of 13 PCGs and selected four mitochondrial genes to represent slow-evolving gene ( COI ) and faster-evolving genes (e.g. ND2 , ND4 , and ND5 ), respectively. We developed four species-specific primers targeting COI , ND2 , ND4 , and ND5 to rapidly identify H. ligniperd a. The results showed that the four species-specific primers exhibited excellent specificity and sensitivity in the PCR assays, with consistent performance across a broader range of species. This method demonstrates the ability to identify beetles promptly, even during their larval stage. The entire detection process can be completed within 2–3 h. Conclusions This method is suitable for large-scale species detection in laboratory settings. Moreover, the selection of target genes in the SS-PCR method is not affected by the evolutionary rate. SS-PCR can be widely implemented at port and forestry workstations, significantly enhancing early management strategies and quarantine measures against H. ligniperda . This approach will help prevent the spread of the pest and effectively preserve the resources of Chinese pine forests.

Diabetes mellitus, particularly type 2 diabetes, has been associated in some studies with increased osteoarthritis (OA) risk, greater symptom burden, and structural progression; however, epidemiological evidence remains heterogeneous, and the extent to which diabetes independently contributes to OA after accounting for obesity, adiposity, physical inactivity, and other metabolic confounders remains debated. In metabolically susceptible patients, chronic hyperglycemia, insulin resistance, carbonyl stress, and low-grade inflammation may contribute to disruption of the joint microenvironment. One proposed mechanism involves the accumulation of advanced glycation end products (AGEs), which can cross-link with type I/II collagen and may increase extracellular matrix crosslinking and stiffness. Through these biochemical and biomechanical effects, the AGE-collagen axis may influence chondrocyte mechanotransduction, inflammatory signaling, matrix turnover, and cell survival, thereby providing a plausible but not yet fully validated link between systemic metabolic dysfunction and OA-relevant structural degeneration. Recent advances in biomaterials may offer experimental and translational strategies to modulate the glycated, inflamed, and mechanically altered joint microenvironment. These approaches include stiffness-tunable and stimuli-responsive hydrogels, nanocarriers designed to deliver anti-AGE agents or AGE-cleaving enzymes, osteochondral gradient scaffolds that mimic native tissue transitions, and immunomodulatory materials that may attenuate local inflammation. At present, these interventions should be viewed primarily as preclinical or early translational strategies rather than established disease-modifying therapies. This review discusses the potential molecular and biomechanical implications of the AGE-collagen axis in diabetes-related OA, critically evaluates emerging biomaterials-based therapeutic approaches, and highlights preclinical evaluation models and outcome measures. Finally, we outline key translational challenges-including targeted delivery, long-term safety of degradation products, metabolic heterogeneity, patient stratification, and integration with systemic therapies-and propose methodological frameworks to support the future development of clinically testable biomaterials interventions.

The angiopoietin (ANGPT)–TIE2/TEK signaling pathway is essential for blood and lymphatic vascular homeostasis. ANGPT1 is a potent TIE2 activator, whereas ANGPT2 functions as a context-dependent agonist/antagonist. In disease, ANGPT2-mediated inhibition of TIE2 in blood vessels is linked to vascular leak, inflammation, and metastasis. Using conditional knockout studies in mice, we show TIE2 is predominantly activated by ANGPT1 in the cardiovascular system and by ANGPT2 in the lymphatic vasculature. Mechanisms underlying opposing actions of ANGPT2 in blood vs. lymphatic endothelium are poorly understood. Here we show the endothelial-specific phosphatase VEPTP (vascular endothelial protein tyrosine phosphatase) determines TIE2 response to ANGPT2. VEPTP is absent from lymphatic endothelium in mouse in vivo, permitting ANGPT2/TIE2-mediated lymphangiogenesis. Inhibition of VEPTP converts ANGPT2 into a potent TIE2 activator in blood endothelium. Our data support a model whereby VEPTP functions as a rheostat to modulate ANGPT2 ligand effect on TIE2.