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The global push for low-carbon growth highlights the urgent need to examine how corporate internationalization shapes environmental performance. However, the environmental implications of corporate two-way foreign direct investment (CTFDI, integrating inward and outward FDI) remain insufficiently explored. This study investigates whether CTFDI mitigates corporate pollution emissions in line with the pollution halo hypothesis. This study employs a multiple fixed-effects OLS model using 43,410 firm-year observations from 2,894 A-share listed Chinese firms over 2008–2022. The results indicate that CTFDI significantly reduces emissions, with a one-unit increase associated with an average 0.15% decline. Mechanism analysis demonstrates that research and development investment and the adoption of digital and intelligent technologies are primary channels through which CTFDI exerts this effect. Heterogeneity analysis further reveals that non-state-owned enterprises and firms in non-polluting industries experience more pronounced benefits. Overall, the findings provide robust empirical evidence supporting the pollution halo hypothesis from the perspective of two-way FDI and highlight the role of economic openness in advancing green corporate development.

With the new challenges and crises facing agriculture, digitalization and green transformation have become important ways to solve the problems. This paper uses an international economics perspective to chart a new path for sustainable agricultural development. Specifically, it analyzes whether two-way international direct investment (FDI) can facilitate agricultural digital-green fusion(DGF)? Using a sample of 31 provinces (autonomous regions) from 2012 to 2021, this study finds: (1) Two-way FDI can significantly contribute to agriculture’s DGF. (2) In the mechanism test, it is proved that two-way FDI can promote agriculture’s DGF level by promoting green technology innovation capacity and overall regional technology innovation capacity. (3) The positive effects of two-way FDI are prominent in the eastern and central regions, coastal regions, and economically developed areas. (4) In the spatial Durbin model, the local two-way FDI growth improves agriculture’s DGF level in the surrounding areas to a certain extent. The government is advised to prioritize openness, foster an environment for technological innovation, leverage spatial radiation for agricultural DGF, and advance digitally empowered agricultural modernization.

Improving the green electricity efficiency (GEE), is an important issue for China's high-quality economic development. This study presents a spatial correlation network of urban GEE in the Yellow River Basin from 2012 to 2021, constructed using an improved gravity model. Social network analysis and the quadratic assignment procedure method are employed to analyze the spatial correlation characteristics and influencing factors. The findings indicate that urban GEE in the Yellow River Basin exhibits complex and stable network characteristics. The spatial network analysis reveals that Jiayuguan City, Dongying City, Dingxi City, Zibo City, and Shizuishan City occupy central positions within the network. The results indicate that spatial adjacency, GDP per capita, industrial structure, and the level of science and technology expenditure are positively related to urban GEE, while environmental regulation and average temperature are negatively related. The findings of the study have led to policy recommendations aimed at enhancing urban GEE in the Yellow River Basin.

Studies of cultured embryos have provided insights into human peri-implantation development. However, detailed knowledge of peri-implantation lineage development as well as underlying mechanisms remains obscure. Using 3D-cultured human embryos, herein we report a complete cell atlas of the early post-implantation lineages and decipher cellular composition and gene signatures of the epiblast and hypoblast derivatives. In addition, we develop an embryo-like assembloid (E-assembloid) by assembling naive hESCs and extraembryonic cells. Using human embryos and E-assembloids, we reveal that WNT, BMP and Nodal signaling pathways synergistically, but functionally differently, orchestrate human peri-implantation lineage development. Specially, we dissect mechanisms underlying extraembryonic mesoderm and extraembryonic endoderm specifications. Finally, an improved E-assembloid is developed to recapitulate the epiblast and hypoblast development and tissue architectures in the pre-gastrulation human embryo. Our findings provide insights into human peri-implantation development, and the E-assembloid offers a useful model to disentangle cellular behaviors and signaling interactions that drive human embryogenesis.

Long‐chain polyunsaturated fatty acid (LCPUFA) supplementation on neurodevelopmental outcomes in preterm or low birth weight (LBW) infants is controversial. This study aims to evaluate the effects of early LCPUFA supplementation on short‐ and long‐term neurodevelopmental outcomes in preterm or LBW infants. This study was previously registered (CRD42024503566). We searched MEDLINE, Embase, PsycInfo, ClinicalTrials.gov and Cochrane Database through January 2024. Randomised clinical trials (RCTs) or follow‐up studies comparing early LCPUFA supplementation to placebo or no supplementation in preterm or LBW infants were included. Outcomes assessed included long‐term (≥ 5 years) and short‐term (< 5 years) measures, such as IQ, neurodevelopmental impairment (NDI), mental development index (MDI) and psychomotor development index (PDI). A random‐effects model was used to pool outcome data. Thirteen RCTs involving 3360 participants were analysed. Due to imprecision, it was unclear whether LCPUFA supplementation had a beneficial or harmful effect on long‐term IQ (SMD, 0.00; 95% CI, −0.32 to 0.33; I2 = 63%; very low certainty) or on the risk of NDI (RR, 0.77; 95% CI, 0.55–1.08; low certainty), as the confidence intervals allow for potentially clinically meaningful effects. LCPUFA supplementation may reduce the risk of intellectual disability (RR, 0.58; 95% CI, 0.36–0.93; moderate certainty). The evidence did not clearly show short‐term neurodevelopmental benefits. Evidence quality varied from moderate to very low. LCPUFA supplementation may not improve most neurodevelopmental outcomes, but could reduce the risk of intellectual disability in preterm or LBW infants. Further studies with long‐term follow‐up are recommended. Early supplementation with long‐chain polyunsaturated fatty acids in preterm or low birth weight infants shows limited impact on short‐term neurodevelopment but may reduce the risk of intellectual disability. These findings highlight potential benefits and underscore the need for further research on long‐term outcomes and optimal supplementation protocols. Summary Current evidence suggests that early long‐chain polyunsaturated fatty acids (LCPUFA) supplementation has little or no impact on short‐term neurodevelopmental outcomes in preterm or low birth weight infants, including measures like IQ and neurodevelopmental impairment. Despite the modest overall effects, LCPUFA supplementation may lower the risk of intellectual disability, highlighting potential benefits for this vulnerable population. Further well‐designed randomised controlled trials with extended follow‐up periods are necessary to confirm long‐term neurodevelopmental benefits and establish optimal LCPUFA supplementation protocols in this population.

The association of enteral micronutrient supplementation and the neurodevelopmental outcomes of preterm or low birth weight (LBW) infants is controversial. This research was prospectively registered (CRD42023454034). We searched MEDLINE, Embase, PsycInfo, ClinicalTrials. gov, and the Cochrane Library for randomised clinical trials (RCTs) or quasi‐RCTs comparing any enteral micronutrients supplementation with placebo or no supplementation in preterm or LBW infants. The primary outcome was neurodevelopmental impairment (NDI), with secondary outcomes involving various neurodevelopmental tests and disabilities. There was no evidence of an association between enteral micronutrients supplementation and the risk of NDI (RR, 1.03; 95% CI, 0.93–1.14; moderate certainty evidence). There was no evidence that the supplemented groups enhanced cognitive (MD, 0.65; 95% CI, −0.37 to 1.67; low certainty evidence), language (SMD, −0.01; 95% CI, −0.11 to 0.09; moderate certainty evidence), or motor scores (SMD, 0.04; 95% CI, −0.06 to 0.15; very low certainty evidence) or IQ (SMD, −0.20; 95% CI, −0.53 to 0.13; very low certainty evidence). Subgroup analysis showed that multiple micronutrients supplementation improved expressive language score (MD, 1.42; 95% CI, 0.39–2.45), and zinc supplementation enhanced fine motor score (SMD, 1.70; 95% CI, 0.98–2.43). The overall heterogeneity was low. This study demonstrates that enteral micronutrient supplementation is associated with little or no benefits in neurodevelopmental outcomes for preterm or LBW infants. Well‐designed RCTs are needed to further ascertain these associations. Enteral micronutrient supplementation shows limited to no impact on the neurodevelopmental outcomes of preterm or low birth weight infants. Subgroup analyses suggest potential benefits in expressive language and motor skills with specific supplements. Key messages There is little or no evidence that enteral micronutrient supplementation during infancy is associated with a decreased risk of neurodevelopmental impairment in preterm or low birth weight infants. Multiple micronutrient supplementation may improve expressive language score, and zinc supplementation may enhance gross motor and fine motor scores. Future well‐designed randomised clinical trials with more participants and longer follow‐up are needed to further ascertain these associations.

The issue of overcapacity has been a widespread concern in the international community since the global financial crisis. For developing countries, adopting effective measures to alleviate overcapacity is crucial to overcome development bottlenecks and achieve the “dual-carbon” target on schedule. The integration of environmental, social, and governance (ESG) principles, which advocates for clean production and sustainable operation, reshapes the business philosophy of enterprises during their development process and profoundly influences enterprise behavior. It is worthwhile to explore how the ESG performance of Chinese enterprises affects capacity utilization (CU). Using data from 4,100 A-share listed companies over the period 2009 to 2022, the study employs a two-way fixed effects model for empirical analysis. The results of this study are as follows: (a) good ESG performance can enhance enterprises' CU; (b) ESG performance enhances CU by alleviating information asymmetry, improving green innovation capability, and strengthening internal control levels; and (c) the impact is more significant in non-state-owned, small, capital-intensive, and low-carbon industries. This study supports the global adoption of ESG practices and provides insights for addressing overcapacity issues in the context of global decarbonization. Plain Language Summary Good ESG performance can resolve enterprises overcapacity This study focuses on the relationship between ESG performance and capacity utilization (CU) from a green development perspective. This study can enrich the frontier of green finance as well as provide information for companies to realize environmental sustainability. The problem of overcapacity caused by the financial crisis has brought the issue of corporate sustainability to the forefront. The purpose of this paper is to analyze how companies can solve overcapacity through good ESG performance based on sustainable development orientation, and then provide new ideas for developing countries to solve the overcapacity problem in the context of global decarbonization. This paper aims to analyze how enterprises can solve overcapacity through good ESG performance based on sustainable development orientation, and then provide new ideas for developing countries to solve the overcapacity problem in the context of global decarbonization. The main innovation of this paper is to analyze the economic consequences of enterprises’ ESG performance on capacity utilization, which has certain policy implications for promoting enterprises’ sustainable development, overcoming resource and environmental constraints, and promoting society’s green development.

In the multi-axis motion control field, the point-to-point multi-objective learning algorithm, taking target point tracking accuracy as the major control objective, limits the optimization of other control objectives such as motion distance and energy consumption of the system. Therefore, this paper initially proposes multi-objective region iterative learning control algorithm, extending point-to-point tracking to region-to-region tracking and further optimizing other performance indexes via flexibly setting the region size on the grounds of the tracking requirements. In view that the fixed weight coefficient of each object in the multi-objective iterative learning algorithm is not suitable for occasions with dynamic control requirements, this paper further presents time-varying multi-objective region iterative learning control algorithm and unifies point-to-point tracking and region-to-region tracking under one algorithm framework. In addition, by means of designing time-varying weight matrix, strengthening major control objectives, weakening minor control objectives and satisfying the ever-changing control requirements, experimental results show that time-varying multi-objective region iterative learning algorithm is capable of flexibly adjusting the weight size of each objective in line with various control needs so that the control system is endowed with higher tracking accuracy in trajectory tracking segment as well as lower energy consumption and shorter motion distance in region tracking segment, thus having good flexibility.

Mindful learning is widely known to improve learning outcomes, yet its association with students’ well-being remains unexplored. This study aimed to investigate the impact of mindful learning on subjective well-being (SWB) and psychological well-being (PWB) in postgraduate students, using survey questionnaires and a randomized experimental design. In Study 1, correlation and regression analyses based on 236 postgraduate students revealed significant positive associations among mindful learning, SWB, and PWB. In Study 2, 54 students were randomly assigned to three groups: the experimental (which received Mindful Learning Coaching), active-, and blank control groups. The results from repeated-measures ANOVA showed that coaching significantly improved students’ mindful learning. The participants’ SWB and PWB significantly decreased in both the active- and blank control groups, whilst their SWB and PWB tended to increase in the experimental group. In conclusion, mindful learning, SWB, and PWB are significantly correlated, while the enhancement of mindful learning may be a protective factor in students’ well-being.

Background: Brain–computer interface (BCI) technology opens up new avenues for human–machine interaction and rehabilitation by connecting the brain to machines. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Despite the great potential of BCI technology, the challenges of extracting and decoding brain signals limit its wide application. Methods: To address this challenge, this study proposes a novel hybrid deep learning model, CLTNet, which focuses on solving the feature extraction problem to improve the classification of MI-EEG signals. In the preliminary feature extraction stage, CLTNet uses a convolutional neural network (CNN) to extract time series, channel, and spatial features of EEG signals to obtain important local information. In the deep feature extraction stage, the model combines the long short-term memory (LSTM) network and the Transformer module to capture time-series data and global dependencies in the EEG. The LSTM explains the dynamics of the brain activity, while the Transformer’s self-attention mechanism reveals the global features of the time series. Ultimately, the CLTNet model classifies motor imagery EEG signals through a fully connected layer. Results: The model achieved an average accuracy of 83.02% and a Kappa value of 0.77 on the BCI IV 2a dataset, and 87.11% and a Kappa value of 0.74 on the BCI IV 2b dataset, both of which outperformed the traditional methods. Conclusions: The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area.