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In this study, a mixed precursor system of fly ash (50 wt%) and calcium carbide slag (50 wt%) was used to prepare a geopolymer, and the hydration and hardening mechanism of the whole system and the microscopic characterization of the calcium carbide-fly ash based polymer were investigated after the addition of calcium carbide slag. Ca(OH)2 in calcium carbide slag can effectively excite the volcanic ash activity of fly ash, which leads to a more adequate geopolymerization reaction and produces more hydrated calcium silicate (C-S-H) gels. These gels have very high specific surface area and surface adsorption energy. The results showed that the specific surface area of geopolymer was as high as 79.76 m2/g, and through the study of its adsorption capacity of Cu(II) in aqueous solution, the results showed that its removal efficiency of Cu(II) was 97.63% and its adsorption capacity was 58.58 mg/g. By using fly ash and calcium carbide slag as the auxiliary raw materials for the preparation of geopolymer, it not only can promote the reaction of proceeding, but can also act as an excellent adsorption material, and also as an effective way to utilize industrial waste resources.

This study successfully developed Cu-Mn supported catalysts on fly ash geopolymer for the oxidative removal of toluene. The surface characteristics and catalytic performance of catalysts were systematically investigated in this paper. It was found that the Cu/Mn ratio plays a crucial role in determining the catalysts’ performance, with the Geo-3 catalyst showing the highest toluene conversion rate of 90% at 296 ℃. The catalyst also exhibited the lowest activation energy of 123.0 kJ/mol, indicating its superior catalytic efficiency. The findings not only contribute to the development of cost-effective and efficient catalysts for VOCs degradation but also provide valuable insights into the influence of the Cu/Mn ratio on the catalytic properties of geopolymer-supported Cu-Mn catalysts. This research offers a promising approach for the abatement of VOCs in industrial emissions.

The impact of prolonged sports training on athletes’ brain functional activity remains inconclusive. A systematic review and coordinate-based meta-analysis of resting-state functional magnetic resonance imaging (rs-MRI) is necessary to identify functional connectivity changes induced by prolonged sports training. A total of 31 studies were included in the systematic review, and 18 studies with 347 non-athletes and 327 athletes were analyzed using coordinate-based activation likelihood estimation (ALE) and seed-based d mapping with permutation of subject images (SDM-PSI) meta-analysis. Results revealed that greater functional connectivity in athletes in the paracentral lobule, medial frontal gyrus, precuneus, inferior parietal lobule, supramarginal gyrus, inferior frontal gyrus, Rolandic operculum, and median cingulate/paracingulate gyri (DCG). Consistent changes in the DCG were identified in closed-skill sports athletes. Furthermore, increased regional functional activity was observed in the posterior cingulate gyrus, lingual gyrus and cerebellum. Both meta-analytical methods emphasize altered functional activity within the default mode network (DMN), cerebellar network (CBN), ventral attention network (VAN), visual network (VA), and sensorimotor network (SMN). These findings suggest that long-term sport training optimizes connection strength and efficiency in brain regions associated with visual attention, cognitive and motor control in athletes. Overall, our study reveals shared neural adaptations across different types of athletes, offering new insights into the effects of long-term specific training on brain functional connectivity in specialists.

Background The neutrophil percentage-to-albumin ratio (NPAR), which is defined as the percentage of neutrophils divided by the concentration of albumin, is a cost-effective and readily available biomarker of inflammation. This study aimed to evaluate the association between the NPAR and the severity of coronary atherosclerosis in patients with chronic kidney disease (CKD). Methods A total of 280 CKD patients who underwent coronary angiography were retrospectively enrolled in this study. The severity of coronary atherosclerosis was evaluated using the Gensini score (GS). Patients were divided into low-, medium- and high-NPAR groups according to the tertiles of the NPAR values. Logistic regression analysis was conducted to analyze the relationship between the NPAR and the GS. The cutoff points for the sensitivity and specificity of the NPAR in predicting the GS were estimated via receiver operating characteristic (ROC) analysis. Results There was a higher prevalence of coronary artery disease (CAD) among CKD patients with higher NPARs ( P =0.041). More patients in the high-NPAR group had complex CAD (triple-vessel disease and/or left main coronary artery stenosis) and chronic total occlusion lesions, and more of these patients required revascularization therapy (P<0.05). Multivariate logistic regression analysis revealed a significant positive correlation between the NPAR and the severity of coronary stenosis (adjusted OR 2.68, 95% CI 1.25-5.76, p =0.012), particularly among female and older (age ≥65) patients. The ROC analysis indicated that the optimal cutoff value for the NPAR in predicting severe coronary artery stenosis (GS>60) in CKD patients was 1.91 (sensitivity 0.495, specificity 0.749), with an area under the curve (AUC) of 0.650 (95% CI 0.581-0.719, P <0.001). A subgroup analysis according to sex revealed that the NPAR exhibited stronger predictive value in female patients (AUC 0.730, 95% CI 0.643-0.817) than in male patients (AUC 0.565, 95% CI 0.460-0.670) ( P <0.001), and the optimal cutoff value for the NPAR in female patients was 1.80 (sensitivity 0.667, specificity 0.705). Conclusions Our study demonstrated that the NPAR is independently associated with the severity of coronary atherosclerosis in CKD patients, especially in female and elderly patients (≥65 years old). Moreover, the NPAR can effectively predict the severity of coronary atherosclerosis, exhibiting greater predictive value in females than in males.

In this work, using fly ash and calcium carbide slag as mixed precursors, hydrogen peroxide and sodium dodecyl sulfate as mixed foaming agents, porous microsphere geopolymer was prepared by suspension curing technology to remove Cu (II) from aqueous solution. Because the Ca (OH) 2 in calcium carbide slag effectively improves the pozzolana activity of fly ash, more C-S-H gel is produced in the geopolymerization process, and the foaming process makes the material have larger specific surface area and better adsorption properties. The results show that the specific surface area of the material is 63.46 m 2 ·g -1 , the product regeneration rate is 83.20%, the optimal adsorption conditions are pH = 5, and the adsorption capacity was 118.48 mg·g -1 . At the same time, it is found that the pseudo-first-order kinetic model and the Langmuir isotherm model can be applied to adsorption kinetics and isothermal adsorption respectively. Adsorption mechanisms may include physisorption and chemisorption. The adsorption capacity of this product for Cu(II) is better than that of other similar products, which has great potential in the field of heavy metal adsorption and provides a new idea for the resourceful use of industrial by-products.

With the rapid development of the social economy, monumental changes have taken place in the urban and rural environments. Urban and rural areas play a vital role in the interactions between humans and society. Traditional machine learning methods are used to perceive the massive changes in the urban and rural areas, though it is easy to overlook the detailed information about the changes made to the intentional target. As a result, the perception accuracy needs to be improved. Therefore, based on a deep neural network, this paper proposes a method to perceive the spatiotemporal changes in urban and rural intentional connotations through the perspective of remote sensing. The framework first uses multibranch DenseNet to model the multiscale spatiotemporal information of the intentional target and realizes the interaction of high-level semantics and low-level details in the physical appearance. Second, a multibranch and cross-channel attention module is designed to refine and converge multilevel and multiscale temporal and spatial semantics to perceive the subtle changes in the urban and rural intentional targets through the semantics and physical appearance. Finally, the experimental results show that the multibranch perception framework proposed in this paper has the best performance on the two baseline datasets A and B, and its F-Score values are 88.04% and 53.72%, respectively.

There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.

Objective This study aimed to conduct a bibliometric analysis of research articles on the relationship between inflammatory bowel disease (IBD) and colorectal cancer (CRC) using CiteSpace to summarize the current research status, hotspots, and trends in this field and present the results visually. Method Research articles on the relationship between IBD and CRC published from 2000 to 2023 and in English were selected from the Web of Science Core Collection (Woscc) database. The articles were downloaded as “full record and references”. CiteSpace was used to conduct cooperative, cluster, co-citation, and burst analyses. Results The literature search revealed 4244 articles; of which, 5 duplicates were removed, resulting in the inclusion of 4239 articles in this study. The United States of America had the highest number of publications, with Mayo Clinic and Harvard University being the most active institutions, and Bas Oldenburg being the most active author. Collaboration among core authors was inadequate. JA Eaden was the most cited author, and CRC was the most common keyword. Burst analysis indicated that Sun Yat-sen University might be one of the institutions with a large contribution to this research field in the future. Cluster analysis showed that earlier research focused more on microsatellite instability, whereas “gut microbiota” and “oxidative stress” are considered current research hotspots and trends. Conclusion At present, the primary focus areas of research are “gut microbiota” and “oxidative stress”. With the improvement of healthcare policies and standards, regular endoscopic monitoring of patients with IBD has become an indispensable diagnostic and therapeutic practice. More drugs will be developed to reduce the risk of progression from IBD to CRC. The findings of this study provide valuable insights into the relationship between IBD and CRC for researchers in the same field.

Understanding building energy demand is critical for addressing climate uncertainty challenges and ensuring sustainable urban growth. This study develops a building energy demand (BED) model to explore how climate variation and urban expansion affect residential and commercial space heating and cooling demands in Morocco for three scenarios, namely, 2005, 2018, and 2018 + 1.5 °C. The results show that coastal cities have lower heating and cooling needs due to the oceanic influence, while interior cities require significantly higher heating demand per-unit-floorspace. Between 2005 and 2018, urban growth increased total heating and cooling demand by 218.8 GWh, particularly in northern and coastal regions, despite per-unit-floorspace reductions in milder climates and improved building efficiency in 2018. Residential heating remains the dominant energy use, though commercial demand is significant in urban centers. Under the 2018 + 1.5 °C hypothetical scenario, heating demand across Morocco declines by 335.8 GWh compared to 2018, with urban areas amplifying this trend. Meanwhile, cooling demand increases slightly by 44.4 GWh, with major cities experiencing relative increases of up to 50%. These findings highlight a trade-off where reduced winter heating needs are partly offset by increased summer cooling demands in densely urbanized areas. The study identifies key urban hotspots for targeted interventions, emphasizing the need for energy-efficient building designs, climate-adaptive urban planning, and resilient energy management strategies to sustainably address shifting seasonal energy patterns.

Rockfill dams are among the most complex, significant, and costly infrastructure projects of great national importance. A key issue in their design is the construction stage and zone optimization. However, a detailed flow shop construction scheme that considers the opinions of decision makers cannot be obtained using the current rock-fill dam construction stage and zone optimization methods, and the robustness and efficiency of existing construction stage and zone optimization approaches are not sufficient. This research presents a construction stage and zone optimization model based on a data-driven analytical hierarchy process extended by D numbers (D-AHP) and an enhanced whale optimization algorithm (EWOA). The flow shop construction scheme is optimized by presenting an automatic flow shop construction scheme multi-criteria decision making (MCDM) method, which integrates the data-driven D-AHP with an improved construction simulation of a high rockfill dam (CSHRD). The EWOA, which uses Levy flight to improve the robustness and efficiency of the whale optimization algorithm (WOA), is adopted for optimization. This proposed model is implemented to optimize the construction stages and zones while obtaining a preferable flow shop construction scheme. The effectiveness and advantages of the model are proven by an example of a large-scale rockfill dam.