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As global attention to climate change intensifies, China, as the world’s largest energy consumer and carbon emitter, faces the dual challenge of improving energy efficiency while the carbon rebound effect (CRE) counteracts efforts to reduce emissions. Based on panel data from Chinese prefecture-level cities from 2010 to 2021. It combines multiple linear regression with machine learning (ensemble and deep learning) to uncover the nonlinear drivers of CRE. Machine learning models capture high-dimensional interactions and threshold effects. SHAP values and ALE plots are employed to quantify the impact pathways of key variables, overcoming the limitations of traditional models. This study employs multiple linear regression, ensemble learning, and deep learning methods, combined with interpretable SHAP values and ALE charts, to systematically identify the core driving mechanisms behind the carbon rebound effect. The main conclusions of this paper are as follows: (1) The CRE exhibits an overall “M”-shaped fluctuation trend, with a spatial pattern of “East > Central > West” and “North > South”; (2) Machine learning models accurately identify DEI, HTD, ER, Water, and UIS2 as the key drivers of CRE. These factors also exhibit significant and consistent threshold effects. This study provides a scientific basis for the formulation of differentiated ecological policies.

The application of semiconductor metal oxides in chemiresistive methane gas sensors has seen significant progress in recent years, driven by their promising sensitivity, miniaturization potential, and cost-effectiveness. This paper presents a comprehensive review of recent developments and future perspectives in this field. The main findings highlight the advancements in material science, sensor fabrication techniques, and integration methods that have led to enhanced methane-sensing capabilities. Notably, the incorporation of noble metal dopants, nanostructuring, and hybrid materials has significantly improved sensitivity and selectivity. Furthermore, innovative sensor fabrication techniques, such as thin-film deposition and screen printing, have enabled cost-effective and scalable production. The challenges and limitations facing metal oxide-based methane sensors were identified, including issues with sensitivity, selectivity, operating temperature, long-term stability, and response times. To address these challenges, advanced material science techniques were explored, leading to novel metal oxide materials with unique properties. Design improvements, such as integrated heating elements for precise temperature control, were investigated to enhance sensor stability. Additionally, data processing algorithms and machine learning methods were employed to improve selectivity and mitigate baseline drift. The recent developments in semiconductor metal oxide-based chemiresistive methane gas sensors show promising potential for practical applications. The improvements in sensitivity, selectivity, and stability achieved through material innovations and design modifications pave the way for real-world deployment. The integration of machine learning and data processing techniques further enhances the reliability and accuracy of methane detection. However, challenges remain, and future research should focus on overcoming the limitations to fully unlock the capabilities of these sensors. Green manufacturing practices should also be explored to align with increasing environmental consciousness. Overall, the advances in this field open up new opportunities for efficient methane monitoring, leak prevention, and environmental protection.

Inorganic halide perovskites such as cesium lead halide are promising due to their excellent thermal stability. Cesium lead iodide (CsPbI 3 ) has a bandgap of 1.73 eV and is very suitable for making efficient tandem solar cells, either with low-bandgap perovskite or silicon. However, the phase instability of CsPbI 3  is hindering the further optimization of device performance. Here, we show that high quality and stable α-phase CsPbI 3 film is obtained via solvent-controlled growth of the precursor film in a dry environment. A 15.7% power conversion efficiency of CsPbI 3 solar cells is achieved, which is the highest efficiency reported for inorganic perovskite solar cells up to now. And more importantly, the devices can tolerate continuous light soaking for more than 500 h without efficiency drop. Cesium lead iodide inorganic perovskite solar cells have great potential but the phase instability hinders their development. Here Wang et al. show a controlled drying process to make phase stable and highly efficient solar cells with power conversion efficiency of 15.7%.

In recent years, the power conversion efficiency of perovskite solar cells has increased to reach over 20%. Finding an effective means of defect passivation is thought to be a promising route for bringing further increases in the power conversion efficiency and the open-circuit voltage (VOC) of perovskite solar cells. Here, we report the use of an organic halide salt phenethylammonium iodide (PEAI) on HC(NH2)2–CH3NH3 mixed perovskite films for surface defect passivation. We find that PEAI can form on the perovskite surface and results in higher-efficiency cells by reducing the defects and suppressing non-radiative recombination. As a result, planar perovskite solar cells with a certificated efficiency of 23.32% (quasi-steady state) are obtained. In addition, a VOC as high as 1.18 V is achieved at the absorption threshold of 1.53 eV, which is 94.4% of the Shockley–Queisser limit VOC (1.25 V).Planar perovskite solar cells that have been passivated using the organic halide salt phenethylammonium iodide are shown to have suppressed non-radiative recombination and operate with a certified power conversion efficiency of 23.3%.

In this contribution, we present a GPS+GLONASS+BeiDou+Galileo four-system model to fully exploit the observations of all these four navigation satellite systems for real-time precise orbit determination, clock estimation and positioning. A rigorous multi-GNSS analysis is performed to achieve the best possible consistency by processing the observations from different GNSS together in one common parameter estimation procedure. Meanwhile, an efficient multi-GNSS real-time precise positioning service system is designed and demonstrated by using the multi-GNSS Experiment, BeiDou Experimental Tracking Network, and International GNSS Service networks including stations all over the world. The statistical analysis of the 6-h predicted orbits show that the radial and cross root mean square (RMS) values are smaller than 10 cm for BeiDou and Galileo, and smaller than 5 cm for both GLONASS and GPS satellites, respectively. The RMS values of the clock differences between real-time and batch-processed solutions for GPS satellites are about 0.10 ns, while the RMS values for BeiDou, Galileo and GLONASS are 0.13, 0.13 and 0.14 ns, respectively. The addition of the BeiDou, Galileo and GLONASS systems to the standard GPS-only processing, reduces the convergence time almost by 70 %, while the positioning accuracy is improved by about 25 %. Some outliers in the GPS-only solutions vanish when multi-GNSS observations are processed simultaneous. The availability and reliability of GPS precise positioning decrease dramatically as the elevation cutoff increases. However, the accuracy of multi-GNSS precise point positioning (PPP) is hardly decreased and few centimeter are still achievable in the horizontal components even with 40 ∘ elevation cutoff. At 30 ∘ and 40 ∘ elevation cutoffs, the availability rates of GPS-only solution drop significantly to only around 70 and 40 %, respectively. However, multi-GNSS PPP can provide precise position estimates continuously (availability rate is more than 99.5 %) even up to 40 ∘ elevation cutoff (e.g., in urban canyons).

Background Although anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms. Methods A two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran’s Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features ( P  < 1.03 × 10 –4 ). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study. Results In MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (OR fixed-effect IVW  = 1.04; 95% CI 1.021–1.06; P fixed-effect IVW  = 4.3 × 10 –5 ) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a “leave-one-out” analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = − 0.0013, SE = 0.0006, P  = 0.06). No heterogeneity was detected by Cochran’s Q test (MR-Egger. Q = 7.68, P  = 0.742; IVW. Q = 12.12, P  = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction ( P  < 0.001). In addition, two significant pathways, the “primary bile acid biosynthesis” pathway ( P  = 0.008) and the “valine, leucine, and isoleucine biosynthesis” pathway ( P  = 0.03 ) , were identified through metabolic pathway analysis. Conclusion This study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.

Some of MADS-box transcription factors (TFs) have been shown to play essential roles in the adaptation of plant to abiotic stress. Still, the mechanisms that MADS-box proteins regulate plant stress response are not fully understood. Here, a stress-responsive MADS-box TF OsMADS23 from rice conferring the osmotic stress tolerance in plants is reported. Overexpression of OsMADS23 remarkably enhanced, but knockout of the gene greatly reduced the drought and salt tolerance in rice plants. Further, OsMADS23 was shown to promote the biosynthesis of endogenous ABA and proline by activating the transcription of target genes OsNCED2 , OsNCED3 , OsNCED4 and OsP5CR that are key components for ABA and proline biosynthesis, respectively. Then, the convincing evidence showed that the OsNCED2 -knockout mutants had lower ABA levels and exhibited higher sensitivity to drought and oxidative stress than wild type, which is similar to osmads23 mutant. Interestingly, the SnRK2-type protein kinase SAPK9 was found to physically interact with and phosphorylate OsMADS23, and thus increase its stability and transcriptional activity. Furthermore, the activation of OsMADS23 by SAPK9-mediated phosphorylation is dependent on ABA in plants. Collectively, these findings establish a mechanism that OsMADS23 functions as a positive regulator in response to osmotic stress by regulating ABA biosynthesis, and provide a new strategy for improving drought and salt tolerance in rice.

Plant roots are constantly exposed to a variety of abiotic stresses, and high salinity is one of the major limiting conditions that impose constraints on plant growth. In this study, we describe that OsMADS25 is required for the root growth as well as salinity tolerance, via maintaining ROS homeostasis in rice (Oryza sativa). Overexpression of OsMADS25 remarkably enhanced the primary root (PR) length and lateral root (LR) density, whereas RNAi silence of this gene reduced PR elongation significantly, with altered ROS accumulation in the root tip. Transcriptional activation assays indicated that OsMADS25 activates OsGST4 (glutathione S-transferase) expression directly by binding to its promoter. Meanwhile, osgst4 mutant exhibited repressed growth and high sensitivity to salinity and oxidative stress, and recombinant OsGST4 protein was found to have ROS-scavenging activity in vitro. Expectedly, overexpression of OsMADS25 significantly enhanced the tolerance to salinity and oxidative stress in rice plants, with the elevated activity of antioxidant enzymes, increased accumulation of osmoprotective solute proline and reduced frequency of open stoma. Furthermore, OsMADS25 specifically activated the transcription of OsP5CR, a key component of proline biosynthesis, by binding to its promoter. Interestingly, overexpression of OsMADS25 raised the root sensitivity to exogenous ABA, and the expression of ABA-dependent stress-responsive genes was elevated greatly in overexpression plants under salinity stress. In addition, OsMADS25 seemed to promote auxin signaling by activating OsYUC4 transcription. Taken together, our findings reveal that OsMADS25 might be an important transcriptional regulator that regulates the root growth and confers salinity tolerance in rice via the ABA-mediated regulatory pathway and ROS scavenging.

Background The prognostic significance of high mobility group box 1 ( HMGB1 ) expression in the non-small cell lung cancer (NSCLC) population remains controversial. This study endeavors to systematically evaluate the relation of HMGB1 expression levels to NSCLC prognosis via a comprehensive meta-analysis. Methods Embase, the Cochrane Library, Web of Science, as well as PubMed were retrieved for eligible studies until September 18, 2025. Two reviewers independently extracted relevant data and appraised the study quality. The study quality was examined via the Newcastle-Ottawa Scale (NOS). Hazard Ratio (HR) with corresponding confidence intervals (CIs) for survival outcomes were calculated and summarized respectively. Subgroup analysis, regression analysis, sensitivity analysis and publication bias were conducted to investigate the findings further. Results 11 studies on 4,527 NSCLC patients were encompassed. All eligible studies had high methodological quality. The pooled HR of OS was 1.08 (95% CI: 0.91–1.29, P  = 0.356), suggesting no significant association of HMGB1 expression levels with NSCLC prognosis. Subgroup analysis of studies with sample sizes < 100 revealed a significant relation of high HMGB1 expression to poorer OS (HR: 1.51, 95% CI: 1.22–1.87). Conversely, when HMGB1 expression level was detected before chemotherapy, high HMGB1 expression was linked to improved OS (HR: 0.96, 95% CI: 0.93–0.99). Conclusion This meta-analysis demonstrated that HMGB1 expression was not significantly associated with OS in the overall NSCLC population, but may have context-dependent prognostic value warranting further investigation.

The PPP–RTK method, which combines the concepts of Precise of Point Positioning (PPP) and Real-Time Kinematic (RTK), is proposed to provide a centimeter-accuracy positioning service for an unlimited number of users. Recently, the PPP–RTK technique is becoming a promising tool for emerging applications such as autonomous vehicles and unmanned logistics as it has several advantages including high precision, full flexibility, and good privacy. This paper gives a detailed review of PPP–RTK focusing on its implementation methods, recent achievements as well as challenges and opportunities. Firstly, the fundamental approach to implement PPP–RTK is described and an overview of the research on key techniques, such as Uncalibrated Phase Delay (UPD) estimation, precise atmospheric correction retrieval and modeling, and fast PPP ambiguity resolution, is given. Then, the recent efforts and progress are addressed, such as improving the performance of PPP–RTK by combining multi-GNSS and multi-frequency observations, single-frequency PPP–RTK for low-cost devices, and PPP–RTK for vehicle navigation. Also, the system construction and applications based on the PPP–RTK method are summarized. Moreover, the main issues that impact PPP–RTK performance are highlighted, including signal occlusion in complex urban areas and atmosphere modeling in extreme weather events. The new opportunities brought by the rapid development of low-cost markets, multiple sensors, and new-generation Low Earth Orbit (LEO) navigation constellation are also discussed. Finally, the paper concludes with some comments and the prospects for future research.