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Abstract Electrification plays a crucial role in deep decarbonization. However, electrification and power infrastructure can cause mutual challenges. We use nationwide power outage and electric vehicle adoption data in China to provide empirical evidence on how power infrastructure failures can deter electrification. We find that when the number of power outages per district increases by 1 in a given month, the number of new electric vehicles adopted per month decreases by 0.99%. A doubling of power outages in one year on average across the nation can create a depressed adoption rate for up to a decade, implying a decline of more than $ 31.3 million per year in carbon reduction benefits from electric vehicle adoptions. This paper adds to the policy discussion of the costs of increased power outages due to extreme weather and natural disasters, and the urgency for policy to address this issue to facilitate wide adoption of electrification.

In the present work, the modification effect of Te and the corresponding growth process of Mg2Si were clarified. With 0.5 wt% Te addition, the dendritic primary Mg2Si transformed to polygons, accompanied by a reduction in size from over ∼165 μm–∼18 μm. However, 2.0 wt% Te addition resulted in degradation of modification effect, and the morphology of Mg2Si changed to dendrite. Three-dimensional morphology of primary Mg2Si indicated that Te altered the morphology of primary Mg2Si from dendrites to polyhedrons surrounded by {111} and {100}. The modification mechanism of Te can be attributed to the heterogeneous nucleation and the doping of Te during the growth process of primary Mg2Si, which greatly contributed to dimension decrease and morphology evolution of primary Mg2Si respectively. With Te corporation, the relative surface energy between the {100} and {111} (r) drops, contributing to the exposure of {100}. Our work shows that the solute concentration under non-equilibrium solidification plays an important role in determining the final morphology of Mg2Si. The local segregation of Mg and Si solutes results in the degradation of dendrite arms along certain directions during the initial stage of crystal growth. Combined with the modifying effect of Te, which alters the surface energy of the crystal, the final crystal morphology deviates from its equilibrium state. Our study demonstrates that ultimate morphology and dimensions of primary Mg2Si can be determined simultaneously with the addition of certain modifiers, which can be achieved by regulating the growth and nucleation of Mg2Si at the same time.

Summary: Rooftop photovoltaics (RPVs) are crucial for decarbonizing the power sector and achieving carbon neutrality, with expected future capacity increases. The growth of RPVs necessitates substantial aluminum (Al) resources, contributing significantly to carbon dioxide (CO2) emissions from Al production. Given China’s bauxite shortage, recycling Al from waste RPV panels presents an effective solution to enhance resource security and mitigate CO2 emissions. We developed a framework to project waste RPV quantities and assess the recycling potential of Al and its impact on CO2 emissions from 2020 to 2060. Our findings indicate potential waste flows of 95–221 million tonnes (Mt) and recycled Al ranging from 5 to 28 Mt, with a primary Al supply gap of 25–43 Mt. Recycling could reduce CO2 emissions by 35–207 Mt over the period. This research underscores the importance of Al resource security and sustainable RPV industry development in China’s pursuit of carbon neutrality.

Heat-moisture treatment (HMT) could improve the structure and physicochemical characteristics of rice starch, the structural changes of amylose and amylopectin needed to be further investigated. Hence, the starch, amylose and amylopectin were modified by HMT with different moisture contents (MC). As MC increased, starch granules became irregular, amylose appeared molten while amylopectin was less damaged. The crystal structure of starch was disrupted by HMT. The increase in the double helix structure of amylose exhibited more drastic tendency towards molecular rearrangement than amylopectin did. In addition, the reduced proportion of amylopectin A chain could affect the rearrangement of amylose. The solubility and pasting temperature improved, but the enthalpy decreased. Moderate MC (20 %, 25 %) could enhance the viscosity and dynamic viscoelasticity. HMT promoted the conversion of RDS to RS, which was significantly increased by 85.26 % at HMT-40 %. These findings contributed to a better understanding of the mechanisms by which HMT affected starch.

Cannibalism is a significant factor contributing to individual disability, mortality, and reduced shrimp production in commercial culture. The primary challenge of controlling cannibalism is the real-time and efficient monitoring of its occurrence. Here, we simultaneously captured high-speed video and feeding acoustics to match sound signals to feeding behaviors in Penaeus vannamei. Building upon a feeding acoustics signal acquisition system, passive acoustic monitoring (PAM) was utilized to detect differences in the acoustic signals produced when shrimps fed on broken feed, pellet feed, and meat fragments of P. vannamei (cannibalism). The morphological and structural characteristics of P. vannamei mandibles were analyzed using micro-CT, nano-indentation, and Raman testing. The main findings were: (1) Feeding on meat fragments resulted in a distinct high-frequency impulse train signal that may serve as an effective indicator for monitoring cannibalistic behavior during culture. (2) Shrimps produced similar and stable acoustic signals while consuming pellet feed, broken feed, or meat fragments; however, there were significant differences in the numbers of generated pulses (P < 0.05), with broken feed < pellet feed < meat fragments. (3) The mandibles consisted of thin anterior incisors and flat thick posterior molar processes, both with stratified structures; the mandibles exhibited greater elastic modulus and hardness values in their outer layers than inner layers, and calcium phosphate deposits were present in both incisors and molar processes. The collisions between incisors showed patterns that were highly synchronized with the acoustic feeding signals. This study has expanded research on passive acoustics monitoring in crustaceans and provided crucial data for the development of precious feeding technology in crustaceans.

Introduction: Sodium zirconium cyclosilicate (SZC) is a nonabsorbed cation-exchanger approved in China for the treatment of hyperkalemia [HK; serum potassium (sK+) levels >5.0 mmol/L]. This is the first real-world study aimed to assess the effectiveness, safety, and treatment patterns of SZC in Chinese patients with HK. Here we present the results of the first interim analysis.Methods: This multicenter, prospective, cohort study included patients aged ≥18 years with documented HK within 1-year before study enrollment day. These patients were followed up for 6 months from the enrollment day after initiating SZC treatment. The treatment was categorized into correction phase (FAS-P1) and maintenance phase (FAS-P2 new and ongoing users). Subgroup analysis was performed in patients on hemodialysis (FAS-H). The primary objective was evaluation of safety profile of SZC; secondary objectives included assessment of treatment patterns of SZC and its effectiveness.Results: Of 421 screened patients, 193, 354, and 162 patients were enrolled in the FAS-P1, FAS-P2, and FAS-H groups, respectively. sK+ levels were reduced significantly from 5.9 mmol/L to 5.0 mmol/L after the correction phase. For the maintenance phase, the mean sK+ levels were maintained at 5.2 mmol/L and 5.0 mmol/L in the FAS-P2 new and ongoing user, respectively, and 5.3 mmol/L in the FAS-H subgroup. A considerable proportion of patients showed normokalemia after 48 h of SZC treatment (FAS-P1:51.3%) which was maintained up to 6 months in the maintenance phase (FAS-P2:44%). SZC was well-tolerated.Conclusion: SZC was effective and safe for the treatment of HK in real-world clinical practice in China.

Abstract Existing research has focused mainly on the technological effects of rigid constraints such as low-carbon policies, but there has been little exploration of flexible constraints such as the government’s low-carbon concerns. To explore the role of flexible low-carbon constraints and effective paths for achieving low-carbon development, this paper theoretically and empirically investigates the influence of the government’s low-carbon concerns on local firms’ innovation in renewable energy technology and the underlying mechanism. Benchmark analysis shows that the government’s low-carbon concerns can improve renewable energy technology among firms in an area. Mechanism analysis reveals that low-carbon concerns promote firms’ renewable energy technology adoption by increasing the amount of research and development (R&D) investment and government subsidies and energy use costs of firms in the area. Heterogeneity analysis reveals that heterogeneity exists across technologies, firms, industries and locations in terms of the incentive impact of the government’s low-carbon concerns on the level of renewable energy technology innovation. Extensive analysis indicates that the government’s low-carbon concerns have a long-term incentive impact on the renewable energy technology of local firms and a positive demonstration impact on the renewable energy technology of neighboring firms. This study is helpful in that it explores the major driving factors behind the creation of a low-carbon economy and the achievement of climate targets.

Abstract In practical engineering, the working conditions of gearbox are complex and variable. In varying working conditions, the performance of intelligent fault diagnosis model is degraded because of limited valid samples and large data distribution differences of gearbox signals. Based on these issues, this research proposes a gearbox fault diagnosis method integrated with lightweight channel attention mechanism, and further realizes the cross-component transfer learning. First, time–frequency distribution of original signals is obtained by wavelet transform. It could intuitively reflect local characteristics of signals. Secondly, based on a local cross-channel interaction strategy, a lightweight efficient channel attention mechanism (LECA) is designed. The kernel size of 1D convolution is affected by channel number and coefficients. Multi-scale feature input is used to retain more detailed features of different dimensions. A lightweight convolutional neural network is constructed. Finally, a transfer learning method is applied to freeze lower structures of the network and fine-tune higher structures of the model using small samples. Through experimental verification, the proposed model could effectively utilize samples. The application of transfer learning could realize accurate and fast fault classification of small samples, and achieve good gearbox fault diagnosis effect under varying working conditions and cross-component conditions.

Abstract Triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in microglial activation, survival, and apoptosis, as well as in Alzheimer’s disease (AD) pathogenesis. We previously reported the MS4A locus as a key modulator for soluble TREM2 (sTREM2) in cerebrospinal fluid (CSF). To identify additional novel genetic modifiers of sTREM2, we performed the largest genome-wide association study (GWAS) and identified four loci for CSF sTREM2 in 3,350 individuals of European ancestry. Through multi-ethnic fine mapping, we identified two independent missense variants (p.M178V in MS4A4A and p.A112T in MS4A6A) that drive the association in MS4A locus and showed an epistatic effect for sTREM2 levels and AD risk. The novel TREM2 locus on chr 6 contains two rare missense variants (rs75932628 p.R47H, P=7.16×10-19; rs142232675 p.D87N, P=2.71×10-10) associated with sTREM2 and AD risk. The third novel locus in the TGFBR2 and RBMS3 gene region (rs73823326, P=3.86×10-9) included a regulatory variant with a microglia-specific chromatin loop for the promoter of TGFBR2. Using cell-based assays we demonstrate that overexpression and knock-down of TGFBR2, but not RBMS3, leads to significant changes of sTREM2. The last novel locus is located on the APOE region (rs11666329, P=2.52×10-8), but we demonstrated that this signal was independent of APOE genotype. This signal colocalized with cis-eQTL of NECTIN2 in the brain cortex and cis-pQTL of NECTIN2 in CSF. Overexpression of NECTIN2 led to an increase of sTREM2 supporting the genetic findings. To our knowledge, this is the largest study to date aimed at identifying genetic modifiers of CSF sTREM2. This study provided novel insights into the MS4A and TREM2 loci, two well-known AD risk genes, and identified TGFBR2 and NECTIN2 as additional modulators involved in TREM2 biology.

There is an interaction between subway station and the catchment area around the station, the catchment area of subway station on surrounding land is closely related to the connection modes. At present, subway passengers mainly reach subway stations by walking, cycling and bus, and the catchment area of the three connection modes is different. In order to clarify the catchment area of subway stations, it is necessary to study the catchment area of the three connection modes of subway stations. This study starts with a generalized connection cost for each connection mode, constructing the generalized connection cost models, especially considering the psychological changes of passengers with the increase of connecting distance. On this basis, we use Logit-SUE model to construct the probability density curve of the superior connection distance of different connection modes in different types and different area subway stations. Taking Beijing Metro Line 4 as the research case, through analyzing the probability density curve of each station of Line 4 we calculate the advantageous connection range of each subway station with different connection modes, and take the advantageous connection range as the catchment area for each type of connection mode.

Abstract Water resources protection is related to the development of the social economy, and the monitoring and prediction of water environmental indicators have important practical significance. In view of the seasonality, periodicity, uncertainty, and nonlinear characteristics of water quality indicators data, traditional prediction models have poor performance. To address this issue, this paper introduces a hybrid water quality index prediction model based on Ensemble Empirical Mode Decomposition (EEMD), combined with Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). We have conducted out experiments to predict dissolved oxygen based on the water quality monitoring indicators of the Liaohe National Control Sanhongcun Village station in Yichun City. The results show that the model proposed in this paper improves the $$R^2$$ R 2 index by 5%, 7% and 5% compared to the suboptimal model in the 4-h, 1-day and 2-day index predictions, respectively.

The foreign trade of China has rapidly increased since it acceded to the World Trade Organization. Simultaneously, the environmental pollution problem of China has become increasingly prominent, thereby seriously restricting the sustainable development of its economy. Based on enterprise-level multivariate heterogeneous data in China, this study explores the influence and mechanism of enterprise export behavior on pollution emissions from a micro perspective. Findings show that pollution emissions increase with enterprise export intensity. The robustness and endogeneity analyses support the cited conclusions. Influence mechanism analysis shows that exports of enterprises may increase pollution emissions by raising energy consumption and export product categories and decrease pollution emissions by promoting green productivity. In addition, expansion analysis found that the influence of export behavior on pollution emissions is heterogeneous owing to trade characteristics. This study deconstructs the influence of China’s foreign trade on domestic environmental pollution from a micro perspective. Moreover, this study provides an objective basis that the relevant Chinese government departments can optimize the structure and improve the quality of foreign trade and achieve coordinated development between foreign trade and environmental protection.

Gantries and beams, as the main load-bearing structures of heavy equipment, usually belong to the box structure consisting of outer walls and inner stiffened plates. The structure of the stiffener layout is bulky due to empirical design, leading to higher material consumption and impacting mechanical performance. There are challenges in effectively identifying load-transferred paths within 3D box structures through direct topological optimization. A method for optimizing the layout of internal stiffened plates of large box structures based on load paths is proposed in this paper. Initially, based on the load conditions acting on the structure, the 3D box structure is decomposed into 2D functional sections. Subsequently, the load paths on the functional cross-section are visualized according to the load path method. Finally, the stiffener layout of the ultimate optimized structure is designed according to the effective load path distribution. Taking the gantry of a heavy-duty aluminum ingot composite processing unit as an example, the optimization results indicate that the maximum stress of the structure decreased by 14.9%, the maximum deformation reduced by 32.95%, and the overall weight decreased by 14.4%. This demonstrates that the approach proposed in this paper is practical and effective for optimizing stiffener layouts in large-box structures.

Three-dimensional indoor models have wide applications in fields such as indoor navigation, civil engineering, virtual reality, and so on. With the development of LiDAR technology, automatic reconstruction of indoor models from point clouds has gained significant attention. We propose a new framework for generating indoor 3D digital models from point clouds. The proposed method first generates a room instance map of an indoor scene. Walls are detected and projected onto a horizontal plane to form line segments. These segments are extended, intersected, and, by solving an integer programming problem, line segments are selected to create room polygons. The polygons are converted into a raster image, and image connectivity detection is used to generate a room instance map. Then the roofs of the point cloud are extracted and used to perform an overlap analysis with the generated room instance map to segment the entire roof point cloud, obtaining the roof for each room. Room boundaries are defined by extracting and regularizing the roof point cloud boundaries. Finally, by detecting doors and windows in the scene in two steps, we generate the floor plans and 3D models separately. Experiments with the Giblayout dataset show that our method is robust to clutter and furniture point clouds, achieving high-accuracy models that match real scenes. The mean precision and recall for the floorplans are both 0.93, and the Point–Surface Distance (PSD) and standard deviation of the PSD for the 3D models are 0.044 m and 0.066 m, respectively.

Social progress and the improvement of living standards are often accompanied by the intensification of ecological crises. The long-term abuse of natural resources has led to the accumulation of ecological liabilities, which in turn seriously hinders economic development. This has prompted all sectors of society to recognize the importance of ecological carrying capacity (ECC) and ecological security (ES). Remarkable progress has been made in karst desertification control (KDC), which has helped reshape the ECC and ES pattern of forests. Currently, the research field of ECC and ES is experiencing rapid development. Further studies in these areas have immeasurable value in promoting regional sustainable development strategies and strengthening ecological civilization construction. The objective of this paper is to provide an overview of the current research status and potential challenges in the field of ECC and ES, with a view to optimizing the program of forest restoration and protection in KDC. This study systematically analyzed 350 relevant studies and found that (1) research on forest ECC and ES has shown a strong growth trend overall, especially after 2017, with a growth rate exceeding 75%; (2) the literature predominantly focuses on the assessment of forest ECC (40.58%) and the enhancement of forest ES (23.42%); and (3) geographically, research findings are heavily concentrated in Asia, representing 95.40% of the total. Notably, China emerges as the primary contributor to research in this field, accounting for a substantial 94.12%. Based on the above analysis, this review summarizes the significant advancements in forest ecosystems, ECC, and ES, while also delving into the key scientific issues that need to be addressed. Furthermore, it offers valuable insights from forest ecosystems in tackling KDC, with the goal of offering guidance and strategic recommendations for future research and practices in managing delicate ecological environments.

The ascorbate-glutathione (AsA-GSH) cycle plays a critical role in scavenging hydrogen peroxide in plants and contributes significantly to plant stress tolerance. This study examines the cycle’s response in Zostera marina seedlings to warming, specifically under conditions of abnormal sea temperature increase. Three temperature gradients were established: 18°C (control group), 23°C (high-temperature group), and 28°C (abnormally high-temperature group). Results after 7 days of exposure to mild high temperature (23°C) showed decreased MDA content in the HT group, increased AsA/DHA ratio, and enhanced activity of enzymes related to the AsA-GSH cycle. However, exposure to extreme high temperatures resulted in increased oxidative damage and redox imbalance in the AHT group. Initially, enzymes associated with the AsA-GSH cycle, such as APX, MDHAR, GPX, and γ-ECS, increased but significantly decreased later under stress. In contrast, DHAR and GaILDH levels significantly rose on the seventh day. Transcriptome analysis revealed upregulation of APX, MDHAR, DHAR, GR, and γ-ECS genes in the HT group, with a decline in other enzyme gene expressions by the seventh day, except for APX. Under extreme high temperatures, APX expression was downregulated early in the stress period, while DHAR was upregulated, indicating Z. marina seedlings can mitigate oxidative damage under short-term high temperatures by activating the AsA-GSH cycle. Conversely, extreme high temperatures may inhibit this cycle, disrupt redox balance, and adversely affect Z. marina seedling establishment, potentially leading to their demise.

Abstract The incentive-based emergency demand response measure serves as an important regulatory tool during energy system operations. However, whether people will sacrifice comfort to respond to it during heatwave and what the effect on heat vulnerable populations will be are still unclear. A large-scale emergency demand response pilot involving 205,129 households was conducted in southwestern China during continuous extreme high temperatures in summer. We found that the incentive-based emergency demand response causes a statistically significant decline in electricity use with no additional financial burden on vulnerable groups. The electricity conservation potential of urban households was higher than that of rural households. Households with children did not respond to the emergency demand response, while the response of households with elderly individuals proved to be more positive. The repeated and frequent implementation of this policy did not result in an attenuation of the regulatory effect. This research can serve as a reference for countries with similar regulated power markets.

Abstract Risk-related information regarding air pollution can help people understand the risk involved and take preventive measures to reduce health loss. However, the health benefits through these protective behaviors and the health threat of information inequality have not been systematically measured. This article reports the health gains and losses caused by the interaction of “air pollution—air pollution information—human”, and studies the heterogeneity and impact of this interaction. Based on field investigations and transfer learning algorism, this study compiled the first nationwide city-level risk-related information (ERI) response parameter set in China. Then, we developed a Information-Behavioral Equivalent PM2.5 Exposure Model (I-BEPEM) model to project the health benefits caused by the impact of environmental risk-related information on residents’ protective behaviors under different scenarios. The protective behavior led by air pollution risk information reduces 5.7% PM2.5-related premature deaths per year. With a 1% increase in regional ERI reception, PM2.5-related premature mortality decreases by 0.1% on average; If the level of information perception and behavioral protection in all cities is the same as that in Beijing, PM2.5-related premature deaths will decrease by 6.9% annually in China. Further, changing the air quality standard issued by China to the American standard can reduce the overall PM2.5-related premature deaths by 9.9%. Meanwhile, compared with men, other age groups and rural residents, women, older persons, and urban residents are more likely to conceive risk information and adopt protective behaviors to reduce the risk of premature death from air pollution. Air pollution risk information can significantly reduce people's health loss. Changing the real-time air quality monitoring information indicator standard to a more stringent level can quickly and effectively enhance this effect. However, the uneven distribution of this information in regions and populations has resulted in the inequality of health gains and losses.

Purposes In order to meet the requirements of lightweight design of large mining dump truck carriage, the rib layout is optimized on the basis of bionic structure. Methods The basic structure and functional characteristics of a mining dump truck are introduced. The load-bearing characteristics of the carriage under various working conditions are analyzed, and the carriage with full loads and uniform speed or static state is selected as the limit working condition for the bottom plate. The rigidity and strength are studied through simulation analysis. The morphological characteristics of vein branching structure are summarized, and the high similarity between vein branching structure and bottom plate of the carriage is expounded from three aspects of load, structure, and function. A new rib layout and a new carriage are designed according to the morphological characteristics. The mechanical performances of the new carriage are verified through Finite Element Analysis. Conclusion The results show that the load-bearing performance of the new carriage is better, the stress distribution is more uniform, and the load borne by the bottom plate is transferred to the lower rib plates, effectively reducing the bearing capacity of the main beam. Its stiffness and strength are increased by 20.32% and 32.37% respectively, while the weight is reduced by 1.86%.

Abstract Aiming at the nonlinear and non-stationarity of gearbox fault signals and the confusion among different fault categories, a gear fault diagnosis method combining variational mode decomposition, reconstruction and ResNeXt is proposed in this paper. In this paper, parameter K of VMD is determined according to the changing trend of sample entropy (SE), K modal components are obtained after decomposition, and the effective modal components are extracted and reconstructed according to Pearson autocorrelation coefficient, so as to remove redundant information from the original signal. Then the reconstructed signal is transformed by time–frequency and output two-dimensional time–frequency information, which is used as the input of ResNeXt model to extract the characteristics of different faults. Moreover, the model performance is improved by changing the learning rate decline rate, and a fault diagnosis model with high precision and good stability is established.

BackgroundFibromyalgia (FM) is a syndrome characterized by chronic and widespread musculoskeletal pain. A number of studies have implied a potential association between gut microbiota and FM. However, the casual association between gut microbiota and FM remains unknown.MethodMendelian randomization (MR) study was conducted using the summary statistics of genetic variants from the genome-wide association study (GWAS). Inverse variance weighted (IVW), combined with MR-Egger and weighted median were used to investigate the causal association between 119 gut microbiota genera and FM. Sensitivity analyses were performed on the MR results, including heterogeneity test, leave-one-out test and pleiotropy test.ResultsA total of 1,295 single nucleotide polymorphism (SNPs) were selected as instrumental variables (IVs), with no significant heterogeneity and pleiotropy according to the sensitivity analyses. Five gut microbiota genera were found to have significant casual association with FM. Coprococcus2 (OR = 2.317, p-value = 0.005, 95% CI: 1.289–4.167), Eggerthella (OR = 1.897, p-value = 0.001, 95% CI: 1.313–2.741) and Lactobacillus (OR = 1.576, p-value =0.020, 95% CI: 1.073–2.315) can increase the risk of FM. FamillyXIIIUCG001 (OR = 0.528, p-value = 0.038, 95% CI: 0.289–0.964) and Olsenella (OR = 0.747, p-value = 0.050, 95% CI: 0.557–1.000) can decrease the risk of FM.ConclusionThis MR study found that gut microbiota is casually associated with FM. New insights into the mechanisms of FM mediated by gut microbiota are provided.

ObjectiveTo investigate the responses of Zostera marina seedlings to the individual and combined stresses of seasonal temperature increase and ocean acidification (OA) caused by global climate change and anthropogenic factors. This data will help in efforts to protect and restore seagrass beds in temperate coastal zones of China.MethodsA mesoscale experimental system was utilized to analyze stress response mechanisms at multiple levels - phenotype, transcriptome, and metabolome - during the seedling stage of Z. marina, a dominant temperate seagrass species in China. The study monitored the seedlings under varying conditions: increased seasonal temperature, OA, and a combination of both.ResultsFindings revealed that under high-temperature conditions, carotenoid biosynthesis was stimulated through the upregulation of specific metabolites and enzymes. Similarly, the biosynthesis of certain alkaloids was promoted alongside modifications in starch, sucrose, and nitrogen metabolism, which improved the plant’s adaptation to OA. Unique metabolic pathways were activated under OA, including the degradation of certain amino acids and modifications in the citric acid cycle and pyruvate metabolism. When subjected to both temperature and OA stresses, seedlings actively mobilized various biosynthetic pathways to enhance adaptability and resilience, with distinct metabolic pathways enhancing the plant’s response under diversified stress conditions. In terms of growth, all treatment groups exhibited significant leaf length increase (p < 0.05), but the weakest growth index was observed under combined stress, followed by the thermal treatment group. Conversely, growth under OA treatment was better, showing a significant increase in wet weight, leaf length, and leaf width (p < 0.05).ConclusionSeasonal temperature increase was found to inhibit the growth of Z. marina seedlings to some extent, while OA facilitated their growth. However, the positive effects of OA did not mitigate the damage caused by increased seasonal temperature under combined stress due to seedlings’ sensitivity at this stage. Our findings elucidate differing plant coping strategies under varied stress conditions, contingent on the initial environment. This research anticipates providing significant data support for the adaptation of Z. marina seedlings to seasonal temperature fluctuations and global oceanic events like OA, propelling the effective conservation of seagrass beds.

Utility-scale solar energy (USSE) and rooftop solar PV generate about 70% and 30% of all solar electricity. While the adoption of rooftop solar in the United States (U.S.) has been explored in depth, how USSE is spatially distributed has yet to be discussed. This study examines the spatial distribution of solar power plants of 1 MW and above across regions of different socio-demographic characteristics in the U.S. and the impact of solar policy on the spatial distribution using the Geographical Detector Model (GDM) and Kernel Density analysis. The results show that: 1) the distribution of USSE has significant spatial disparity across different socio-economic communities and is not entirely following solar irradiance abundancy; 2) contrary to rooftop solar, USSE is more distributed in more ethnically diverse regions and regions with higher renter percent, which can mitigate the energy inequity issue in rooftop solar PV's tilt toward white communities and house owners; 3) similar to rooftop solar, USSE is also more adopted by groups of higher income levels; 4) The solar policy is an effective tool in promoting USSE but is also exacerbating the spatial disparity of USSE. The findings of this study help to understand better how to mitigate potential inequity during the energy transition toward a renewable and sustainable future.

Summary: Pandemics such as COVID-19 threaten income growth by disrupting productive activities for households, especially those that have just escaped from poverty. We provide empirical evidence on how the pandemic disproportionately threatens the rural productive livelihood based on 48 months of household production electricity consumption data. The results indicate that after COVID-19, the productive livelihood activities of 51.11% households that have just overcome poverty have returned to the level before poverty alleviation. Their productive livelihood activities dropped by 21.81% on average during the national COVID-19 epidemic and by 40.57% during the regional epidemic. The households with lower income, lower level of education, and less labor force suffer more. We estimate a 3.74% decline in income owing to the decrease in productive activities, resulting in 5.41% of households potentially falling back into poverty. This study provides an important reference for countries being at risk of returning to poverty after the pandemic.

The growth of Residential rooftop solar (RRS) in some western countries has predominantly been driven by individual or market behaviour and has been extensively studied. However, the development landscape of RRS in China differs, and its driving mechanisms remain unclear. To address this research gap, we investigate the spatial distribution pattern and driving factors of RRS growth using city-level data on RRS installation. Employing the Geographical Detector Model, we calculate indicators to identify the contributions of various socio-economic factors to RRS growth and the strength of their interactions. Our key findings include: 1) significant spatial heterogeneity in RRS growth across regions with different natural and socio-economic characteristics, which impact RRS growth in two patterns: one-way and inverted U-shaped; 2) although solar radiation abundancy is important, air pollution and certain socio-economic factors appear more influential, be it comparing between the eastern and western China, or north and south; 3) the significance of fiscal subsidies has diminished, but benchmark electricity prices (BEP) could serve as a useful alternative; 4) substantial synergistic effects exist between different factors, with environmental and demographic factors displaying particularly strong synergies with others, suggesting that they are essential considerations for future RRS planning. Our findings contribute to a comprehensive understanding of RRS development in China and hold critical implications for future policy design.

Seagrasses are higher flowering plants that live entirely in marine environments, with the greatest habitat variation occurring from land to sea. Genetic structure or population differentiation history is a hot topic in evolutionary biology, which is of great significance for understanding speciation. Genetic information is obtained from geographically distributed subpopulations, different subspecies, or strains of the same species using next-generation sequencing techniques. Genetic variation is identified by comparison with reference genomes. Genetic diversity is explored using population structure, principal component analysis (PCA), and phylogenetic relationships. Patterns of population genetic differentiation are elucidated by combining the isolation by distance (IBD) model, linkage disequilibrium levels, and genetic statistical analysis. Demographic history is simulated using effective population size, divergence time, and site frequency spectrum (SFS). Through various population genetic analyses, the genetic structure and historical population dynamics of seagrass can be clarified, and their evolutionary processes can be further explored at the molecular level to understand how evolutionary processes contributed to the formation of early ecological species and provide data support for seagrass conservation.

Abstract How to control the global temperature rise within 1.5 °C in the post-COVID-19 era has attracted attention. Road transport accounts for nearly a quarter of global CO2 emissions, and the related sulfur dioxide (SO2) emissions also trigger air pollution issues in population-intensive cities and areas. Many cities and states have announced a timetable for phasing out urban-based fossil fuel vehicles. By combining a Markov-chain model with a dynamic stochastic general equilibrium (DSGE) model, the impacts of on-road energy structural change led by phasing out fossil fuel vehicles in the road transportation sector are evaluated. The impact of automobile emissions (both CO2 and SO2) on the environment is evaluated, taking into consideration of variation between cities, regions, and countries. Two other major driving forces in addition to CO2 emissions reduction in promoting fossil fuel vehicles’ transition toward net-zero carbon are identified and analyzed with multiple different indicators. Under the framework of the DSGE model, climate policy instruments’ effects on economic development, energy consumption, and their link to economic and environmental resilience are evaluated under exogenous shocks as well.

Abstract Purpose To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification. Methods A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves. Results Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit. Conclusions A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients.

In this research, we integrate a three-dimensional micro-/nano-lattice structure metamaterial with conventional helical gears using a composite method to reduce the vibration. A comprehensive six-degree-of-freedom nonlinear dynamics model is constructed to analyze the vibration characteristics of the resulting mechanical metamaterial gear pair. The model incorporates crucial factors such as meshing damping, error excitation, bearing support stiffness, and time-varying meshing stiffness using the dynamic–static method. The results demonstrate the good damping effect of the designed mechanical metamaterial on gear pair vibrations. This research provides theoretical insights for reducing vibrations in gear transmission systems by using mechanical metamaterial.

Bone has a robust regenerative potential, but its capacity to repair critical-sized bone defects is limited. In recent years, stem cells have attracted significant interest for their potential in tissue engineering. Applying mesenchymal stem cells (MSCs) for enhancing bone regeneration is a promising therapeutic strategy. However, maintaining optimal cell efficacy or viability of MSCs is limited by several factors. Epigenetic modification can cause changes in gene expression levels without changing its sequence, mainly including nucleic acids methylation, histone modification, and non-coding RNAs. This modification is believed to be one of the determinants of MSCs fate and differentiation. Understanding the epigenetic modification of MSCs can improve the activity and function of stem cells. This review summarizes recent advances in the epigenetic mechanisms of MSCs differentiation into osteoblast lineages. We expound that epigenetic modification of MSCs can be harnessed to treat bone defects and promote bone regeneration, providing potential therapeutic targets for bone-related diseases.

The expansion and intensification of coastal aquaculture around the Bohai Sea in China has reduced the tidal flats and damaged the coastal vegetation environment. However, there are few studies on the relationship between the evolution of coastal aquaculture and the variability of coastal vegetation, which limits our understanding of the impact of human activities on the coastal ecosystem. In this study, based on remote sensing technology, we firstly used a combination of a neural network classifier and manual correction to monitor the long-term dynamic changes in aquaculture in the Bohai Sea from 1984 to 2022. We then analyzed its evolution, as well as the relationship between the evolution of coastal aquaculture and the variability of coastal vegetation, in detail. Our study had three main conclusions. Firstly, the aquaculture along the coast of the Bohai Sea showed an expanding trend from 1984 to 2022, with an increase of 538%. Secondly, the spatiotemporal changes in the aquaculture centroids in different provinces and cities varied. The centroid of aquaculture in Liaoning Province was mainly distributed in the Liaodong Peninsula, and moved northwest; that in Hebei Province was distributed in the northeast and moved with no apparent pattern; the centroid of aquaculture in Tianjin was mainly distributed in the southeast and moved westward; and the centroid of aquaculture in Shandong Province was mainly distributed in the northwest and moved in a northwesterly direction. Finally, the expansion of aquaculture of the Bohai Sea has increased the regional NDVI and length of the corresponding coastline, and has made coastlines move toward the sea. Our results provide reliable data support and reference for ecologically managing aquaculture and coastal environmental protection in the Bohai Sea.

The coastal wetlands in the Yellow River Delta play a vital role in the ecological function of the area. However, the impact of primary restoration on microbial communities is not yet fully understood. Hence, this study aimed to analyze the bacterial and archaeal communities in the soil. The results indicated that Marinobacter and Halomonas were predominant in the bacterial community during spring and winter. On the other hand, Muribaculaceae and Helicobacter were prevalent during the core remediation of soil, while Inhella and Halanaerobium were predominant in non-vegetation-covered high-salinity soil. The bacterial Shannon index showed significant differences in vegetation-covered areas. For archaea, Salinigranum, Halorubrum, and Halogranum were dominant in vegetation areas, while Halolamina, Halogranum, and Halorubrum were prevalent in non-vegetation areas. The colonization of Suaeda salsa led to differences in the composition of bacteria (22.6%) and archaea (29.5%), and salt was one of the significant reasons for this difference. The microflora was more diverse, and the elements circulated after vegetation grounding, while the microbial composition in non-vegetation areas was similar, but there was potential competition. Therefore, vegetation restoration can effectively restore soil ecological function, while the microorganisms in the soil before restoration provide germplasm resources for pollutant degradation and antimicrobial development.

Efficient storage of crude hydrogen, through toluene hydrogenation to methylcyclohexane, is often inhibited by CO impurities. Here, the authors develop a RuNi/TiO2 catalyst which avoids deactivation through promoting simultaneous CO methanation.

Rotary mechanism is the core part of the multi-functional sling for turning over and erecting the castings and forgings. The mechanical performance of a nail plate inside the structure determines the working safety. In this paper, the dangerous working condition of the rotary mechanism is first introduced when the castings and forgings with different diameters are clamped by the sling. Then, the temperature field and the thermal–mechanical coupling model for the nail plate are established, and the failure mode is analyzed. Next, the layout and shape of the nails on the nail plate are studied to improve the load-bearing performance. A mathematical model, taking the height and draft angle of the nail as the design variables, is established, and a new nail plate model is given. The comparison is carried out through simulation and experiment. The results show that the mechanical performances of the nail plate are significantly improved, and the failure problem of the rotary mechanism is solved.

Introduction Hyperkalaemia (HK) is a potentially life-threatening electrolyte imbalance associated with several adverse clinical outcomes. The efficacy and negative effects of currently existing treatment options have made HK management questionable. Sodium zirconium cyclosilicate (SZC), a novel highly selective potassium binder, is approved for the treatment of HK. The present study will be aimed to assess the safety, effectiveness and treatment patterns of SZC in Chinese patients with HK in a real-world clinical setting as it is required by China’s drug review and approval process.Methods and analysis This is a multicentre, prospective cohort study which plans to enrol 1000 patients taking SZC or willing to take SZC from approximately 40 sites in China. Patients ≥18 years of age at the time of signing the written informed consent and with documented serum potassium levels ≥5.0 mmol/L within 1 year before study enrolment day will be included. Eligible patients will receive SZC treatment and will be followed up for 6 months from enrolment day. The primary objective will be to evaluate the safety of SZC for the management of HK in Chinese patients in terms of adverse events (AEs), serious AEs as well as discontinuation of SZC. The secondary objectives will include understanding the SZC dosage information in terms of its effectiveness and treatment patterns under real-world clinical practice and assessing effectiveness of SZC during the observational period.Ethics and dissemination This study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University (approval number: YJ-JG-YW-2020). All the participating sites have received the ethics approval. Results will be disseminated through national and international presentations and peer-reviewed publications.Trial registration number NCT05271266.

Summary: Energy demand-side management is essential for deep decarbonization. However, while we target people as discrete and isolated individuals, we ignore the fact that energy consumption occurs in intricate webs of pre-existing social relations. This study examines an emergency demand response (EDR) program in China involving more than 180,000 households based on relations with family members, communities, and identity. The results indicate that the existence of social relations can promote energy conservation behavior and there is a large degree of heterogeneity. Targeted relations, such as single-living residents, small-scale households, city dweller etc., show greater electricity savings with 12.35% increase in overall effect, which benefit when demographics change in the next few decades. Such heterogeneous changes put forward the pressing need for policymakers to focus on social relations as a unit in future intervention designs to decarbonize the energy system.

Energy efficiency improvement is key to achieving energy conservation. Based on China's microdata from 2000 to 2010, this study systematically analyzes the impact of carbon reduction policies on energy efficiency through propensity score matching and the difference-in-differences method. The benchmark estimation shows that carbon reduction policy can increase energy efficiency of firms by approximately 50%. A heterogeneity analysis shows that the policy has made energy efficiency of firms reach and exceed the expected target increase by 23% and 53%, respectively. Carbon reduction policies have an increasingly prominent effect in the middle and late stages. Reducing coal input and increasing R&D innovation are identified as transmission paths through which carbon reduction policies promote energy efficiency. In addition, carbon reduction policies can increase profitability of firms by approximately 1.8% and decrease probability of firms' withdrawal by approximately 6.1%. This study opens the black box of energy conservation from a micro perspective and provides a scientific basis for energy transformation.

Energy-environment efficiency can be used as a means of control to change the mode of economic development by considering the pollution reduction factor. Taking China as an example, this study uses a nonradial superefficiency data envelopment analysis method to measure microlevel energy-environment efficiency and investigates the relationship between environmental regulation and efficiency. The findings show that for every 1% increase in environmental regulation, firms’ energy-environment efficiency increases by approximately 0.04% and that environmental regulations have a greater impact on pollution-intensive firms and private firms compared to other types of firms. A median effect model shows that end treatment and source control are important channels through which environmental regulation can improve firms’ energy-environment efficiency. In addition, the influence of different environmental regulation policy tools on firm energy-environment efficiency is heterogeneous. With every 1% increase in a command-and-control and market-based incentive policy, energy-environment efficiency increases by 0.01–0.02%, while the effect of public participation policy tools is not significant. This study provides not only new micro empirical evidence for evaluating the effect of environmental regulation but also a reference for applying and coordinating different types of environmental policy tools.

Because of its long running time, complex working environment, and for other reasons, a gear is prone to failure, and early failure is difficult to detect by direct observation; therefore, fault diagnosis of gears is very necessary. Neural network algorithms have been widely used to realize gear fault diagnosis, but the structure of the neural network model is complicated, the training time is long and the model is not easy to converge. To solve the above problems and combine the advantages of the ResNeXt50 model in the extraction of image features, this paper proposes a gearbox fault detection method that integrates the convolutional block attention module (CBAM). Firstly, the CBAM is embedded in the ResNeXt50 network to enhance the extraction of image channels and spatial features. Secondly, the different time–frequency analysis method was compared and analyzed, and the method with the better effect was selected to convert the one-dimensional vibration signal in the open data set of the gearbox into a two-dimensional image, eliminating the influence of the redundant background noise, and took it as the input of the model for training. Finally, the accuracy and the average training time of the model were obtained by entering the test set into the model, and the results were compared with four other classical convolutional neural network models. The results show that the proposed method performs well both in fault identification accuracy and average training time under two working conditions, and it also provides some references for existing gear failure diagnosis research.

The linear motor feeding system is a typical electromechanical coupling system. Conventional characteristic analyses of electromechanical coupling often overlook the influence of flexible deformation in critical components of the linear motor feeding system. Moreover, when employing genetic algorithms to optimize servo system PID control parameters, slow convergence, nonconvergence, or premature convergence problems may arise. To address these issues, this paper proposes a new performance optimization method for a linear motor feeding system. The method uses a combination of “multi-body theory + finite element” to accurately account for the flexible deformation of critical components of the feeding system, establishes a rigid–flexible electromechanical coupling model of the linear motor feeding system, and optimizes the PID parameters of the established model with an improved adaptive genetic algorithm. Simulation results demonstrate that, when utilizing an adaptive genetic algorithm to optimize the rigid–flexible electromechanical coupling model and a control system model that disregards flexible body deformation, the system achieves stability in 0.02 s and 0.027 s with overshoots of 13% and 27%, respectively. These outcomes confirm the accuracy and importance of considering flexible body deformation in the optimization performance of a linear motor feeding system. At the same time, the time required to reach the steady state of the rigid–flexible electromechanical coupling model optimized by the adaptive genetic algorithm is shortened from 0.035 s to 0.02 s. The sinusoidal signal response curve of the optimized system does not exhibit any peak overshoot compared with that of the nonoptimized system, and the response speed is also faster. These results demonstrate the effectiveness of the rigid–flexible electromechanical coupling model optimized by the nonlinear adaptive genetic algorithm. The displacement response curves of the linear motor feeding system under different workbench loads are obtained through experiments and compared with those obtained from simulations to verify the established model and the correctness of the proposed method.

Abstract With the maturity of big data technology, analyzing residents’ travel habits and tracks has become an important research direction in the field of intelligent transportation study. In this paper, based on the subway and bus ride data, a subway-bus double-layer network model was established using complex network theory, taking the optimal traffic efficiency as the goal, the structure of intelligent bus network optimization method is proposed, and an empirical study is conducted on the Beijing bus network. In the empirical study, by adding or deleting bus station in the network, obtain an efficient network structure, the goal of optimal operation efficiency of the bus network was realized, and the theoretical and practical research on solving the problem of transportation line network planning with big data of traffic travel was enriched.

A sling is a kind of auxiliary equipment in the manufacturing process of castings and forgings. There are some problems, such as a single function, complex operation, and lack of a safety evaluation index, in the design and application of slings. In this paper, a novel multi-functional sling is designed, and it can carry out the transportation, turning over, erection, and lifting of 550 tons of castings and forgings. A clamping coefficient index is defined, and the mechanical model of the sling is established to evaluate whether it can safely hold castings and forgings. Then, an optimization model with a clamping coefficient is established to reduce the weight and maintain the load-bearing performance. The genetic algorithm is adopted, and the total weight is reduced by 3.4%. Finally, the mechanical performances are verified by finite element analysis and experiment, and the results indicate that the sling can meet the design requirements.

Abstract One unmet challenge in lung cancer diagnosis is to accurately differentiate lung cancer from other lung diseases with similar clinical symptoms and radiological features, such as pulmonary tuberculosis (TB). To identify reliable biomarkers for lung cancer screening, we leverage the recently discovered non-canonical small non-coding RNAs (i.e., tRNA-derived small RNAs [tsRNAs], rRNA-derived small RNAs [rsRNAs], and YRNA-derived small RNAs [ysRNAs]) in human peripheral blood mononuclear cells and develop a molecular signature composed of distinct ts/rs/ysRNAs (TRY-RNA). Our TRY-RNA signature precisely discriminates between control, lung cancer, and pulmonary TB subjects in both the discovery and validation cohorts and outperforms microRNA-based biomarkers, which bears the diagnostic potential for lung cancer screening.

The global warming phenomenon emerges from the issue of climate change, which attracts the attention of intellectuals towards clean energy sources from dirty energy sources. Among clean sources, nuclear energy is getting immense attention among policymakers. However, the role of nuclear energy in pollution emissions reduction has remained inconclusive and demand for further investigation. Therefore, the current study contributes to extend knowledge by investigating the nexus between nuclear energy, economic growth, and CO2 emissions in a developing country context such as Pakistan for the period between 1973 and 2017. The auto-regressive distributive lag model summarizes the nuclear energy has negative effect on environmental pollution as it releases carbon emission in the environment. Moreover, vector error correction Granger causality provides evidence for bidirectional causality between nuclear energy and carbon emissions. These interesting findings provide new insight, and policy guidelines provided based on these results.

There lacks a spatially explicit mapping of global carbon footprint in China that considers both international and interprovincial trade. Here the authors map the carbon footprints of global regions in China and show the hotspots concentrated in key manufacturing hubs, including the Yangtze River Delta, Pearl River Delta, and North China Plain.

Bone marrow adipocytes accumulate with age and in diverse disease states. However, their origins and adaptations in these conditions remain unclear, impairing our understanding of their context-specific endocrine functions and relationship with surrounding tissues. In this study, by analyzing bone and adipose tissues in the lipodystrophic ‘fat-free’ mouse, we define a novel, secondary adipogenesis pathway that relies on the recruitment of adiponectin-negative stromal progenitors. This pathway is unique to the bone marrow and is activated with age and in states of metabolic stress in the fat-free mouse model, resulting in the expansion of bone marrow adipocytes specialized for lipid storage with compromised lipid mobilization and cytokine expression within regions traditionally devoted to hematopoiesis. This finding further distinguishes bone marrow from peripheral adipocytes and contributes to our understanding of bone marrow adipocyte origins, adaptations, and relationships with surrounding tissues with age and disease.