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Abstract Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF’s state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.

Abstract With the rapid proliferation of climate policies in both number and scope, there is an increasing demand for a global-level dataset that provides multi-indicator information on policy elements and their implementation contexts. To address this need, we developed the Global Climate Change Mitigation Policy Dataset (GCCMPD) using a semisupervised hybrid machine learning approach, drawing upon policy information from global, regional, and sector-specific sources. Differing from existing climate policy datasets, the GCCMPD covers a large range of policies, amounting to 73,625 policies of 216 entities. Through the integration of expert knowledge-based dictionary mapping, probability statistics methods, and advanced natural language processing technology, the GCCMPD offers detailed classification of multiple indicators and consistent information on sectoral policy instruments. This includes insights into objectives, target sectors, instruments, legal compulsion, administrative entities, etc. By aligning with the sector classification of the Intergovernmental Panel on Climate Change (IPCC) emission datasets, the GCCMPD serves to help policy-makers, researchers, and social organizations gain a deeper understanding of the similarities and distinctions among climate activities across countries, sectors, and entities.

Financial incentives play a key role in promoting renewable energy investments that can help China achieve the ‘dual carbon’ goal. The national emissions trading scheme (ETS) and the renewable energy portfolio standard (RPS) are two existing market-based policy instruments that can generate stable expected returns for low-carbon projects. This paper studies the interactive distribution effects of these two market-based instruments. We use the micro-level thermal power plant data to investigate the abatement effects of the national ETS, in which the details show that the existing rate-based ETS will result in higher negative impacts on power units, whose installed capacities are smaller than 400 MW. The interactive distribution effects between the two markets will occur when the permit allocation standards of the national ETS become stricter than the existing ones. Provinces in Eastern China and Northern China will face high pressure on costs in both ETS and RPS markets. When the levels of the permit allocation standards are set as 70% of the existing ones and the carbon price is assumed to be 200 yuan/ton in 2030, the annual market size of the national ETS will be nearly 100 billion yuan, and the annual market size is predicted to be 250 billion yuan. In the existing rate-based national ETS, the China Certified Emission Reduction (CCER) mechanism will have an offsetting effect, which should be taken into serious consideration during the policy-making processes in the future.

Emotions and task complexity have gained scant attention in EFL writing. This study, based on control-value theory and a positive psychology perspective, explores the relationship between task complexity, writing anxiety, writing boredom, writing enjoyment, and task complexity and writing emotions interaction on EFL writing performance among English major students in China’s educational context (N = 84). It has been found that difficult writing tasks can result in higher writing anxiety and lower writing boredom. By 6 × 2 between-subject ANOVA, there is a significant interaction of task complexity and different levels of writing anxiety, writing boredom, and writing enjoyment for the dimensions of language fluency, complexity, and accuracy among English major students in China’s educational context. This study is an initial step in extending the writing emotions and task complexity in the production of EFL writing, which promotes the mutual integration of emotion and cognition in EFL writing research and thus supplies suggestions for task-based EFL writing instruction.

Abstract Background Small airways disease plays a key role in the pathogenesis of chronic obstructive pulmonary disease (COPD) and is a major cause of obstruction; therefore, it is a critical pharmacotherapy target. This study evaluated lung deposition of two inhaled corticosteroid (ICS)/long-acting β2-agonist/long-acting muscarinic antagonist single-inhaler triple therapies using in silico functional respiratory imaging (FRI). Deposition was assessed using real-world inhalation profiles simulating everyday use where optimal inhalation may be compromised. Methods Three-dimensional airway models were produced from 20 patients with moderate-to-very severe COPD. Total, central, and regional small airways deposition as a percentage of delivered dose of budesonide/glycopyrronium/formoterol fumarate dihydrate (BGF) 160/7.2/5 µg per actuation and fluticasone furoate/umeclidinium/vilanterol (FF/UM/VI) 100/62.5/25 µg were evaluated using in silico FRI based on in vitro aerodynamic particle size distributions of each device. Simulations were performed using multiple inhalation profiles of varying durations and flow rates representing patterns suited for a pressurized metered-dose inhaler or dry-powder inhaler (four for BGF, two for FF/UM/VI, with one common profile). For the common profile, deposition for BGF versus FF/UM/VI was compared post-hoc using paired t-tests. Results Across inhalation profiles, mean total lung deposition was consistently higher with BGF (47.0–54.1%) versus FF/UM/VI (20.8–22.7%) and for each treatment component, with greater deposition for BGF also seen in the central large airways. Mean regional small airways deposition was also greater across inhalation profiles with BGF (16.9–23.6%) versus FF/UM/VI (6.8–8.7%) and for each treatment component. For the common profile, total, central, and regional small airways deposition were significantly greater for BGF versus FF/UM/VI (nominal p

Highlights A hierarchical interconnected NiMoN (HW-NiMoN-2h) was successfully prepared based on a rational combination of hydrothermal and water bath processes. HW-NiMoN-2h exhibited high hydrogen evolution reaction (HER) activity due to its large specific surface area and good stability due to its enhanced mechanical strength. In 1 M KOH seawater, HW-NiMoN-2h delivered current density of 1 A cm−2 for HER at an overpotential of 130 mV and showed excellent stability over 70 h at 1 A cm−2.

Abstract The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users’ trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.

Measurement(s) environmental pollution Technology Type(s) computational modeling technique Sample Characteristic - Location China

Abstract Background Field cancerization is the process in which a population of normal or pre-malignant cells is affected by oncogenic alterations leading to progressive molecular changes that drive malignant transformation. Aberrant DNA methylation has been implicated in early cancer development in non-small cell lung cancer (NSCLC); however, studies on its role in field cancerization (FC) are limited. This study aims to identify FC-specific methylation patterns that could distinguish between pre-malignant lesions and tumor tissues in NSCLC. Methods We enrolled 52 patients with resectable NSCLC and collected resected tumor (TUM), tumor-adjacent (ADJ) and tumor-distant normal (DIS) tissue samples, among whom 36 qualified for subsequent analyses. Methylation levels were profiled by bisulfite sequencing using a custom lung-cancer methylation panel. Results ADJ and DIS samples demonstrated similar methylation profiles, which were distinct from distinct from that of TUM. Comparison of TUM and DIS profiles led to identification of 1740 tumor-specific differential methylated regions (DMRs), including 1675 hypermethylated and 65 hypomethylated (adjusted P

Summary: Behavioral responses to environmental risks create gains and losses. We use high-frequency datasets to elucidate such behavior responses against air pollution and find a “double-peaked” time pattern in reducing outdoor exposure and in increasing electricity consumption. Despite that one standard deviation increase in the Air Quality Index induces 2% less outdoor population and 6% more household electricity consumption at peak, most responses fail to match with the intra-day pollution peaks, implying ineffective exposure avoidance. We find an unbalanced trade-off between health benefits and energy co-damages. The behavior-induced change in annual residential power consumption (+1.01% to +1.20%) is estimated to be 20 times more than that in the population-based exposure (−0.02% to −0.05%), and generates 0.13–0.15 million more metric tons of citywide carbon emissions. Our results imply that by targeting peak pollution periods, policies can shrink the trade-off imbalance and achieve mutual improvements in exposure reduction and energy conservation.

Abstract A microneedle (MN) array is a novel biomedical device adopted in medical applications to pierce through the stratum corneum while targeting the viable epidermis and dermis layers of the skin. Owing to their micron-scale dimensions, MNs can minimize stimulations of the sensory nerve fibers in the dermis layer. For medical applications, such as wound healing, biosensing, and drug delivery, the structure of MNs significantly influences their mechanical properties. Among the various microfabrication methods for MNs, fused deposition modeling (FDM), a commercial 3D printing method, shows potential in terms of the biocompatibility of the printed material (polylactic acid (PLA)) and preprogrammable arbitrary shapes. Owing to the current limitations of FDM printer resolution, conventional micron-scale MN structures cannot be fabricated without a post-fabrication process. Hydrolysis in an alkaline solution is a feasible approach for reducing the size of PLA needles printed via FDM. Moreover, weak bonding between PLA layers during additive manufacturing triggers the detachment of PLA needles before etching to the expected sizes. Furthermore, various parameters for the fabrication of PLA MNs with FDM have yet to be sufficiently optimized. In this study, the thermal parameters of the FDM printing process, including the nozzle and printing stage temperatures, were investigated to bolster the interfacial bonding between PLA layers. Reinforced bonding was demonstrated to address the detachment challenges faced by PLA MNs during the chemical etching process. Furthermore, chemical etching parameters, including the etchant concentration, environmental temperature, and stirring speed of the etchant, were studied to determine the optimal etching ratio. To develop a universal methodology for the batch fabrication of biodegradable MNs, this study is expected to optimize the conditions of the FDM-based fabrication process. Additive manufacturing was employed to produce MNs with preprogrammed structures. Inclined MNs were successfully fabricated by FDM printing with chemical etching. This geometrical structure can be adopted to enhance adhesion to the skin layer. Our study provides a useful method for fabricating MN structures for various biomedical applications.

The stability of a cast-in-place pile foundation in permafrost region is primarily subject to the thermal regime and tangential frost-heave forces (TFF) during the operation period. However, studies focusing on the thermal and mechanical characteristics of pile foundation during runtime are rare. To investigate the effect of pile foundation on the thermal regime and quantify the magnitude of unit tangential frost-heave forces (UTFF), a field experiment was conducted on the Qinghai–Tibet Plateau, China. Results showed that the cast-in-place pile foundation enhanced the heat exchange between the atmosphere and soil, which expanded the annual range of the surrounding ground temperature. Furthermore, the permafrost table depth was increased by 0.4–0.7 m (0.33–0.58 times the pile diameter). The TFF increased significantly when the soil temperature decreased from 0 to −0.5°C. Meanwhile, the thickness of the frost heaving layer was approximately double that of the active layer, in which the maximum UTFF was higher than 52.04 kPa. The adfreezing bond force of permafrost to pile shaft burdened most of the applied load, and the end bearing contributed relatively little. Findings from this study will benefit the stability maintenance and structure design of pile foundation in permafrost regions.

PurposeThis study aimed to explore the circular RNA (circRNA/circ) profile engaged in non-small cell lung cancer (NSCLC) development and metastasis and to investigate potentially key carcinogenic circRNAs related to NSCLC.MethodsCircRNA profiles between 10 NSCLC tissues and 10 adjacent tissues and between five NSCLC tissues with lymph node metastasis (LNM) and five NSCLC tissues without LNM were detected by Arraystar Human circRNA Array followed by bioinformatics. Circ_0008594 knockdown, circ_0004293 overexpression, and circ_0003832 overexpression plasmids were transfected into H23 and H460 cells to sort potential oncogenic circRNA. Then circ_0008594 overexpression and knockdown plasmids were transfected, followed by that circ_0008594 knockdown plus miR-760 knockdown plasmids were transfected into these cells. Cell proliferation, apoptosis, invasion, stemness, and pathways were detected. In addition, xenograft mice models were constructed via injecting H23 cells with circ_0008594 overexpression or knockdown to validate the findings.ResultsA total of 455 dysregulated circRNAs in NSCLC tissues versus adjacent tissues and 353 dysregulated circRNAs in NSCLC tissues with LNM versus those without LNM were discovered. Via cross-analysis, 19 accordant circRNAs were uncovered, among which three candidate circRNAs (circ_0008594, circ_0004293, circ_0003832) were chosen for functional experiments, during which it was observed that circ_0008549 affected H23 and H460 cell proliferation and apoptosis more obviously than circ_0004293 and circ_0003832. Subsequent experiments showed that circ_0008594 promoted H23 and H460 cell proliferation and invasion but affected stemness less and negatively regulated miR-760 via direct binding. Furthermore, miR-760 attenuated the effect of circ_0008549 on regulating H23 and H460 cell functions and the PI3K/AKT and MEK/ERK pathways. In vivo experiments further confirmed that circ_0008549 increased tumor volume, epithelial-mesenchymal transition, and the PI3K/AKT and MEK/ERK pathways while reducing tumor apoptosis and miR-760 NSCLC xenograft models.ConclusionOur study identifies several valuable circRNAs related to NSCLC development and LNM. Furthermore, as a key functional circRNA, circ_0008594 was observed to promote NSCLC progression by regulating the miR-760-mediated PI3K/AKT and MEK/ERK pathways.

Abstract Background Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening disease characterized by an excessive systemic inflammatory response, which can be classified as primary HLH (pHLH) and secondary HLH (sHLH). Viruses are the primary pathogens causing sHLH. Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by hantaviruses. Its main characteristics include fever, circulatory collapse with hypotension, hemorrhage, and acute kidney injury. The case of HFRS presented with sHLH is very rare in clinic. We reported the HFRS inducing by Hantaan virus (HTNV) presented with sHLH as the first case in Shaanxi province of west China. Case presentation A case of HFRS in 69-year-old Chinese woman, which had persistent fever, cytopenia, coagulopathy, ferritin significantly increased, hepatosplenomegaly and superficial lymphadenopathy. The hemophagocytosis was found in bone marrow, which was consistent with the characteristics of the HLH. The patient recovered completely after timely comprehensive treatments. Conclusions HTNV should be considered as one of the underlying viruses resulting in hemophagocytosis, and if occurs, the early diagnosis and rapid therapeutic intervention are very important to the prognosis of sHLH.

Many bubble test methods do not have the ability to predict multiple bubbles in a high dimensional space now. Therefore, we propose a data-driven, self-adaptive evolutionary bubble prediction algorithm named WSADF. First, according to the invariance principle, we speculate that if there are inherent degrees of freedom for high dimensional time series, then comovement causality analysis (CCA) can be improved to select the decisive high dimensional time series that must reflect the prominent comovement causality. The optimization problem of the high dimensional space can be solved in the low dimensional space and maintain the inherent relationships among the time series by using CCA. Second, the learning parameters of hidden neurons have the ability of self-adaptive differential evolution. The neurons in the network are used to model the individuals' signals from the perspective of evolution. Third, a self-adaptive evolutionary neural network can be used to simulate the operation of the entire market's signals. The generalized sup augmented Dickey-Fuller test is improved to suit changing market environmental conditions. Thus, the WSADF algorithm has the ability to predict multiple bubbles in high dimensional space. An empirical application of the methodology is conducted on different types of markets (e.g., the USDCNH and CSI300 closing prices), which has successfully identified and forecasted multiple bubbles from 2015 to 2017.

Electrochemical water splitting driven by clean and sustainable energy sources to produce hydrogen is an efficient and environmentally friendly energy conversion technology. Water splitting involves hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), in which OER is the limiting factor and has attracted extensive research interest in the past few years. Conventional noble-metal-based OER electrocatalysts like IrO2 and RuO2 suffer from the limitations of high cost and scarce availability. Developing innovative alternative nonnoble metal electrocatalysts with high catalytic activity and long-term durability to boost the OER process remains a significant challenge. Among all of the candidates for OER catalysis, self-supported layered double hydroxides (LDHs) have emerged as one of the most promising types of electrocatalysts due to their unique layered structures and high electrocatalytic activity. In this review, we summarize the recent progress on self-supported LDHs and highlight their electrochemical catalytic performance. Specifically, synthesis methods, structural and compositional parameters, and influential factors for optimizing OER performance are discussed in detail. Finally, the remaining challenges facing the development of self-supported LDHs are discussed and perspectives on their potential for use in industrial hydrogen production through water splitting are provided to suggest future research directions.

This study addresses the task of supervised cross-domain image generation, which aims to translate an image from the source domain to the target domain, guided by a reference image from the latter. The key difference between the authors setting and the recently proposed domain guided photogeneration is that their image generation is bidirectional, i.e. images are generated in both domains. Thus, they call it bidirectional cross-domain image generation. This novel task poses new challenges as it requires the model learning to decouple personalised and shared semantics of the two domains. For this purpose, they propose a framework to learn a feature space, which breaks into three parts, namely, two personalised semantic subspaces, which encode patterns that are unique for each domain, and a shared semantic subspace that captures the common patterns. The three subspaces are automatically decoupled through end-to-end training. Extensive experiments on the modified shoe and handbag datasets show that their framework can generate high-quality images in both domains.

The damage of equipment manufactured with ferromagnetic materials in service can be effectively detected by Metal Magnetic Memory Testing (MMMT) technology, which has received extensive attention in various industry fields. The effect of stress or strain on Magnetic Flux Leakage (MFL) signals of ferromagnetic materials has been researched by many scholars for assessing stress concentration and fatigue damage. However, there is still a lack of research on the detection of stress corrosion damage of ferromagnetic materials by MMMT technology. In this paper, the electrochemical corrosion system was designed for corrosion experiments, and three different experiments were performed to study the effect of corrosion on MFL signals. The distribution of MFL signals on the surface of the specimen was investigated. The results indicated that both the normal component Hn and tangential component Ht of MFL signals presented different signal characteristics when the specimen was subjected to different working conditions. Finally, two characterization parameters, Sn and St, were defined to evaluate the corrosion degree of the specimen, and St is better. The direct dependence of corrosion depth on the parameter was developed and the average error rates between the predicted and measured values are 8.94% under the same working condition. Therefore, the expression can be used to evaluate the corrosion degree of the specimen quantitatively. The results are significant for detecting and assessing the corrosion defect of ferromagnetic materials.

About 75% energy demand and emissions all concentrate in urban areas, especially in the metropolises, placing a heavy burden on both the energy supply system and the environment system. To explore low emission pathways and provide policy recommendations for the Shanghai energy system and the environmental system to reach the carbon dioxide (CO2) peak by 2030 and attain emission reduction targets for local air pollutants (LAPs), a regional energy–environment optimization model was developed in this study, considering system costs, socio-economic development and technology. To verify the reliability of the model simulation and evaluate the model risk, a historical scenario was defined to calculate the emissions for 2004–2014, and the data were compared with the bottom-up emission inventory results. By considering four scenarios, we simulated the energy consumption and emissions in the period of 2020–2030 from the perspective of energy policies, economic measures and technology updates. We found that CO2 emissions might exceed the amount of 250 million tons by the end of 2020 under the current policy, and carbon tax with a price of 40 CNY per ton of carbon dioxide is an imperative measure to lower carbon emissions. Under the constraints, the emissions amount of SO2, NOx, PM10, and PM2.5 will be reduced by 95.3–180.8, 207.8–357.1, 149.4–274.5, and 59.5–119.8 Kt in 2030, respectively.

To carry out theoretical analysis of the constant current source of the high stability semiconductor laser, and to perform in-depth experiments and research on this basis. It used certain technical means, and the stability and safety of the laser's power supply were tested and studied based on theoretical analysis of the constant current source of the high-stability conductor laser. According to the experimental results, it was proved that the development of high stability laser power supply was of great significance to the stability and safety of the laser power supply. In improving the stability and safety of semiconductor laser constant current sources, the application of high stability driving power that meets international standards has significantly improved the stability of output power and extended the use time. Therefore, it has certain value of application and promotion.

As the atomic force microscope (AFM) becomes a more accessible tool in the laboratory, researchers involved in the development of pharmaceutical formulations are taking advantage of its unique capabilities. In this work we review the uses of the AFM in the development of hydrofluoroalkane-based pressurized metered-dose inhaler formulations. Perspectives and limitations of the AFM and related techniques in this area also discussed.

Emergency department discharge instructions are variably understood by patients, and in the setting of emergency department crowding, innovations are needed to counteract shortened interaction times with the physician. We evaluated the effect of viewing an online video of diagnosis-specific discharge instructions on patient comprehension and recall of instructions.In this prospective, single-center, randomized controlled trial conducted between November 2011 and January 2012, we randomized emergency department patients who were discharged with one of 38 diagnoses to either view (after they left the emergency department) a vetted online video of diagnosis-specific discharge instructions, or to usual care. Patients were subsequently contacted by telephone and asked three standardized questions about their discharge instructions; one point was awarded for each correct answer. Using an intention-to-treat analysis, differences between groups were assessed using univariate testing, and with logistic regression that accounted for clustering on managing physician. A secondary outcome measure was patient satisfaction with the videos, on a 10-point scale.Among 133 patients enrolled, mean age was 46.1 (s.d.D. 21.5) and 55% were female. Patients in the video group had 19% higher mean scores (2.5, s.d. 0.7) than patients in the control group (2.1, s.d. 0.8) (p=0.002). After adjustment for patient age, sex, first language, triage acuity score, and clustering, the odds of achieving a fully correct score (3 out of 3) were 3.5 (95% CI, 1.7 to 7.2) times higher in the video group, compared to the control group. Among those who viewed the videos, median rating of the videos was 10 (IQR 8 to 10).In this single-center trial, patients who viewed an online video of their discharge instructions scored higher on their understanding of key concepts around their diagnosis and subsequent care. Those who viewed the videos found them to be a helpful addition to standard care.ClinicalTrials.gov NCT01361932 http://clinicaltrials.gov/ct2/show/NCT01361932?term=nct01361932&rank=1.

Rapid development of big data science and technology put forward great challenges to talent cultivation.Data scientist should have the capability of coax commercial value from tremendous data and require multi-disciplinary training.Based on the progress and demand of cultivating big data talent,the patlerns of cultivation big data talent by combing various disciplines and utilizing multiple resources were discussed.In the patterns of talent cultivation,core knowledge system should be built and the basis of innovation capability should be solid.The talent cultivation should have data resource.Therefore,breaking through the data open barriers of government,private firms and universities and providing real world data is essential for talent education.To strengthen the interdisciplinary features,talent cultivation should remove the barriers of disciplines and build up systematic standards through the teaching,internship,graduate and evaluation.