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Achieving low-carbon development of the cement industry in the developing countries is fundamental to global emissions abatement, considering the local construction industry’s rapid growth. However, there is currently a lack of systematic and accurate accounting and projection of cement emissions in developing countries, which are characterized with lower basic economic country condition. Here, we provide bottom-up quantifications of emissions from global cement production and reveal a regional shift in the main contributors to global cement CO 2 emissions. The study further explores cement emissions over 2020-2050 that correspond to different housing and infrastructure conditions and emissions mitigation options for all developing countries except China. We find that cement emissions in developing countries except China will reach 1.4-3.8 Gt in 2050 (depending on different industrialization trajectories), compared to their annual emissions of 0.7 Gt in 2018. The optimal combination of low-carbon measures could contribute to reducing annual emissions by around 65% in 2050 and cumulative emissions by around 48% over 2020-2050. The efficient technological paths towards a low carbon future of cement industry vary among the countries and infrastructure scenarios. Our results are essential to understanding future emissions patterns of the cement industry in the developing countries and can inform policies in the cement sector that contribute to meeting the climate targets set out in the Paris Agreement. The rapid deployment of low-carbon measures is urgently needed to reduce cement emissions as cement CO 2 emissions from developing countries will almost deplete the remaining cement emissions budget within climate targets.

The highly energy-intensive iron and steel industry contributed about 25% (ref.  1 ) of global industrial CO 2 emissions in 2019 and is therefore critical for climate-change mitigation. Despite discussions of decarbonization potentials at national and global levels 2 – 6 , plant-specific mitigation potentials and technologically driven pathways remain unclear, which cumulatively determines the progress of net-zero transition of the global iron and steel sector. Here we develop a CO 2 emissions inventory of 4,883 individual iron and steel plants along with their technical characteristics, including processing routes and operating details (status, age, operation-years etc.). We identify and match appropriate emission-removal or zero-emission technologies to specific possessing routes, or what we define thereafter as a techno-specific decarbonization road map for every plant. We find that 57% of global plants have 8–24 operational years, which is the retrofitting window for low-carbon technologies. Low-carbon retrofitting following the operational characteristics of plants is key for limiting warming to 2 °C, whereas advanced retrofitting may help limit warming to 1.5 °C. If each plant were retrofitted 5 years earlier than the planned retrofitting schedule, this could lead to cumulative global emissions reductions of 69.6 (±52%) gigatonnes (Gt) CO 2 from 2020 to 2050, almost double that of global CO 2 emissions in 2021. Our results provide a detailed picture of CO 2 emission patterns associated with production processing of iron and steel plants, illustrating the decarbonization pathway to the net-zero-emissions target with the efforts from each plant. A CO 2 emissions inventory of 4,883 individual iron and steel plants along with their technical characteristics is described, allowing the identification and guidance of the most appropriate emissions mitigation and decarbonization pathways for each plant.

Tripling renewable energy capacity by 2030 requires increasing technology production capacity, including solar photovoltaics (PV). Current supply chains rely heavily on Chinese production; however, this situation is not aligned with regions aiming to increase self-sufficiency, decrease supply chain emissions, and increase local job opportunities. Here, we apply a supply chain optimization model to perform scenario analysis of the PV supply chain development through 2021-2030 considering various European economic and job creation goals. Irrespective of regional goals, we find that China is poised to remain a globally dominant supplier through 2030, especially in terms of lower-value PV components, given that future demand requires increasing global production capacity by a factor of at least 1.5. We find that some regional supply chain goals can be co-beneficial, for example in terms of joint job gains and increased regional self-sufficiency. However, pursuing highly isolationist policies can introduce cost-significant inefficiencies. Our results highlight that an open trade policy is key to minimizing costs, even when considering security and environmental supply chain objectives. Cui et al. find that open trade policy is a key factor for achieving low-cost solar photovoltaic supply chains. This conclusion holds even for regions, like Europe, that seek to localize solar production capacity.

In the realm of marine environmental engineering, the swift and accurate detection of underwater targets is of considerable significance. Recently, methods based on Convolutional Neural Networks (CNN) have been applied to enhance the detection of such targets. However, deep neural networks usually require a large number of parameters, resulting in slow processing speed. Meanwhile, existing methods present challenges in accurate detection when facing small and densely arranged underwater targets. To address these issues, we propose a new neural network model, YOLOv8-LA, for improving the detection performance of underwater targets. First, we design a Lightweight Efficient Partial Convolution (LEPC) module to optimize spatial feature extraction by selectively processing input channels to improve efficiency and significantly reduce redundant computation and storage requirements. Second, we developed the AP-FasterNet architecture for small targets that are commonly found in underwater datasets. By integrating depth-separable convolutions with different expansion rates into FasterNet, AP-FasterNet enhances the model’s ability to capture detailed features of small targets. Finally, we integrate the lightweight and efficient content-aware reorganization (CARAFE) up-sampling operation into YOLOv8 to enhance the model performance by aggregating contextual information over a large perceptual field and mitigating information loss during up-sampling.Evaluation results on the URPC2021 dataset show that the YOLOv8-LA model achieves 84.7% mean accuracy (mAP) on a single Nvidia GeForce RTX 3090 and operates at 189.3 frames per second (FPS), demonstrating that it outperforms existing state-of-the-art methods in terms of performance. This result demonstrates the model’s ability to ensure high detection accuracy while maintaining real-time processing capabilities.

Displacement metrology with nanometer-level precision over macroscopic ranges is a key foundation for modern science and engineering. This review provides a comparative overview of Precision Nanometrology, covering measurement ranges from micrometers to meters and accuracies between 0.1 nm and 100 nm. Three main technologies are discussed: the Laser Interferometer (LI), the Grating Interferometer (GI), and the Time Grating Sensor (TGS). The LI is widely regarded as the traceable benchmark for highest resolution; the GI has been developed into a compact and stable solution based on diffraction gratings; and the TGS has emerged as a new approach that converts spatial displacement into the time domain, offering strong resilience to environmental fluctuations. For each technique, the principles, recent progress, and representative systems from the past two decades are reviewed. Particular attention is given to the trade-offs between resolution, robustness, and scalability, which are decisive for practical deployment. The review concludes with a comparative analysis of performance indicators and a perspective on future directions, highlighting hybrid architectures and application-driven requirements in precision manufacturing and advanced instrumentation.

This study on the simplification of brand visual identity primarily explores the necessity of simplifying brand logos and visual elements and their impact on brand recognition. The findings indicate that a simplified visual identity not only enhances brand recognition in digital environments but also improves consumer memory and associations with the brand. By reducing extraneous design elements, brand imagery can more intuitively convey core values while maintaining consistency across multiple platforms. Additionally, minimalist design aligns with modern consumers’ aesthetic preferences, projecting professionalism and a sense of trendiness, thereby enhancing the brand’s market competitiveness. Simplified design also facilitates cross-cultural communication for brands in the global market, increasing their international influence. This paper summarizes the various advantages of simple visual design and offers several practical recommendations, providing theoretical support for brands seeking sustainable development in a complex and dynamic market environment. The study employs literature review and case analysis methods. Using the well-known automotive brand Audi as a case study, it analyzes the need to balance visual simplicity with brand uniqueness during the simplification process to avoid the loss of brand personality.

Composed of obesity and lipid parameters, the cardiometabolic index (CMI) has emerged as a novel diagnostic tool. Originally developed for diabetes diagnosis, its application has expanded to identifying patients with cardiovascular diseases, such as atherosclerosis and hypertension. However, the relationship between CMI and non-alcoholic fatty liver disease (NAFLD) and liver fibrosis in the US population remains unclear. This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning 2017–2020, involving 2996 participants aged 20 years or older. Vibration controlled transient elastography using a FibroScan® system (model 502, V2 Touch) with controlled attenuation parameter measurements identified NAFLD at a threshold of ≥ 274 dB/m, while liver stiffness measurement (LSM) results (median, ≥ 8.2 kPa) indicated fibrosis. A multifactorial logistic regression model explored the relationship between CMI and NAFLD and fibrosis. The effectiveness of CMI in detecting NAFLD and liver fibrosis was assessed through receiver operating characteristic curve analysis. Controlling for potential confounders, CMI showed a significant positive association with NAFLD (adjusted OR = 1.44, 95% CI 1.44–1.45) and liver fibrosis (adjusted OR = 1.84, 95% CI 1.84–1.85). The Areas Under the Curve for predicting NAFLD and fibrosis were 0.762 (95% CI 0.745 ~ 0.779) and 0.664(95% CI 0.633 ~ 0.696), respectively, with optimal cut-off values of 0.462 and 0.527. There is a positive correlation between CMI and NAFLD and fibrosis, which is a suitable and simple predictor of NAFLD and fibrosis.

Background: Parkinson’s disease (PD) is a common neurodegenerative disorder, accompanied by motor deficits as well as gastrointestinal dysfunctions. Recent studies have proved that the disturbance of gut microbiota and metabolism contributes to the pathogenesis of PD; however, the mechanisms underlying these effects have yet to be elucidated. Curcumin (CUR) has been reported to provide neuroprotective effects on neurological disorders and modulate the gut flora in intestinal-related diseases. Therefore, it is of significant interest to investigate whether CUR could exert a protective effect on PD and whether the effect of CUR is dependent on the intestinal flora and subsequent changes in metabolites.Methods: In this study, we investigated the neuroprotective effects of CUR on a mouse model of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). 16S rRNA sequencing was performed to explore the profile of the gut microbiota among controls, MPTP-treated mice and CUR-treated mice. Then, antibiotic treatment (ABX) and fecal microbiota transplantation (FMT) experiments were conducted to examine the role of intestinal microbes on the protective effects of CUR in PD mice. Furthermore, ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS)-based metabolomics analysis was used to identify the landscape of the CUR-driven serum metabolome. Finally, Pearson’s analysis was conducted to investigate correlations between the gut flora-metabolite axis and CUR-driven neuroprotection in PD.Results: Our results showed that CUR intervention effectively improved motor deficits, glial cell activation, and the aggregation of α-synuclein (α-syn) in MPTP-treated mice. 16S rRNA sequencing showed elevated abundances of Muribaculaceae, Lactobacillaceae, Lachnospiraceae and Eggerthellaceae but depleted abundances of Aerococcaceae and Staphylococcaceae in CUR-treated mice when compared with MPTP mice. ABX and FMT experiments further confirmed that the gut microbiota was required for CUR-induced protection in PD mice. Serum metabolomics analysis showed that CUR notably upregulated the levels of tyrosine, methionine, sarcosine and creatine. Importantly, strong correlations were identified among crucial taxa (Aerococcaceae, Staphylococcaceae, Muribaculaceae, Lactobacillaceae, Lachnospiraceae and Eggerthellaceae), pivotal metabolites (tyrosine, methionine, sarcosine and creatine) and the motor function and pathological results of mice. CUR treatment led to a rapid increase in the brain levels of tyrosine and levodopa (dopa) these changes were related to the abundances of Lactobacillaceae and Aerococcaceae.Conclusions: CUR exerts a protective effect on the progression of PD by modulating the gut microbiota-metabolite axis. Lactobacillaceae and Aerococcaceae, along with key metabolites such as tyrosine and dopa play a dominant role in CUR-associated neuroprotection in PD mice. Our findings offer unique insights into the pathogenesis and potential treatment of PD.

Automatic detection of clue cells is crucial for rapid diagnosis of bacterial vaginosis (BV), but existing algorithms suffer from low sensitivity. This is because clue cells are highly similar to normal epithelial cells in terms of macroscopic size and shape. The key difference between clue cells and normal epithelial cells lies in the surface texture and edge morphology. To address this specific problem, we propose an clue cell detection algorithm named CA-YOLO. The contributions of our approach lie in two synergistic and custom-designed feature extraction modules: the context-aware module (CAM) extracts and captures bacterial distribution patterns on the surface of clue cells; and the shuffle global attention mechanism (SGAM) enhances cell edge features and suppresses irrelevant information. In addition, we integrate focal loss into the classification loss to alleviate the severe class imbalance problem inherent in clinical samples. Experimental results show that the proposed CA-YOLO achieves a sensitivity of 0.778, which is 9.2% higher than the baseline model, making the automated BV detection more reliable and feasible.

Diabetic peripheral neuropathy (DPN) is a major complication of diabetes mellitus with a high incidence. Oxidative stress, which is a crucial pathophysiological pathway of DPN, has attracted much attention. The distortion in the redox balance due to the overproduction of reactive oxygen species (ROS) and the deregulation of antioxidant defense systems promotes oxidative damage in DPN. Therefore, we have focused on the role of oxidative stress in the pathogenesis of DPN and elucidated its interaction with other physiological pathways, such as the glycolytic pathway, polyol pathway, advanced glycosylation end products, protein kinase C pathway, inflammation, and non-coding RNAs. These interactions provide novel therapeutic options targeting oxidative stress for DPN. Furthermore, our review addresses the latest therapeutic strategies targeting oxidative stress for the rehabilitation of DPN. Antioxidant supplements and exercise have been proposed as fundamental therapeutic strategies for diabetic patients through ROS-mediated mechanisms. In addition, several novel drug delivery systems can improve the bioavailability of antioxidants and the efficacy of DPN.