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There is an ever-growing demand for artificial intelligence. Optical processors, which compute with photons instead of electrons, can fundamentally accelerate the development of artificial intelligence by offering substantially improved computing performance. There has been long-term interest in optically constructing the most widely used artificial-intelligence architecture, that is, artificial neural networks, to achieve brain-inspired information processing at the speed of light. However, owing to restrictions in design flexibility and the accumulation of system errors, existing processor architectures are not reconfigurable and have limited model complexity and experimental performance. Here, we propose the reconfigurable diffractive processing unit, an optoelectronic fused computing architecture based on the diffraction of light, which can support different neural networks and achieve a high model complexity with millions of neurons. Along with the developed adaptive training approach to circumvent system errors, we achieved excellent experimental accuracies for high-speed image and video recognition over benchmark datasets and a computing performance superior to that of cutting-edge electronic computing platforms.Linear diffractive structures are by themselves passive systems but researchers here exploit the non-linearity of a photodetector to realize a reconfigurable diffractive ‘processing’ unit. High-speed image and video recognition is demonstrated.

Photonic computing enables faster and more energy-efficient processing of vision data 1 – 5 . However, experimental superiority of deployable systems remains a challenge because of complicated optical nonlinearities, considerable power consumption of analog-to-digital converters (ADCs) for downstream digital processing and vulnerability to noises and system errors 1 , 6 – 8 . Here we propose an all-analog chip combining electronic and light computing (ACCEL). It has a systemic energy efficiency of 74.8 peta-operations per second per watt and a computing speed of 4.6 peta-operations per second (more than 99% implemented by optics), corresponding to more than three and one order of magnitude higher than state-of-the-art computing processors, respectively. After applying diffractive optical computing as an optical encoder for feature extraction, the light-induced photocurrents are directly used for further calculation in an integrated analog computing chip without the requirement of analog-to-digital converters, leading to a low computing latency of 72 ns for each frame. With joint optimizations of optoelectronic computing and adaptive training, ACCEL achieves competitive classification accuracies of 85.5%, 82.0% and 92.6%, respectively, for Fashion-MNIST, 3-class ImageNet classification and time-lapse video recognition task experimentally, while showing superior system robustness in low-light conditions (0.14 fJ μm −2 each frame). ACCEL can be used across a broad range of applications such as wearable devices, autonomous driving and industrial inspections. An all-analog chip combining electronic and light computing achieves systemic energy efficiency of more than three orders of magnitude and a computing speed of more than one order of magnitude compared with state-of-the-art computing processors.

The global warming caused by greenhouse gas emissions has received widespread attention from all around the world. In this regard, how to calculate the carbon footprint (CF) scientifically and accurately produced by human activities to achieve emission reduction goals has been widely discussed by scholars. In recent years, related research on this issue has increased, leading to a significant expand in the number of publications. It is necessary to excavate and summarize the current development status and possible future trends of this field based on quantitative methods. To achieve this goal, this paper develops a main path analysis (MPA) of the entire field and three research sub-topics (agriculture, energy fuels, and business economic) based on the 4973 papers extracted from Web of Science (WoS) database. The results show that the CF domain mainly focuses on optimizing the CF calculation methods from a theoretical perspective to improve the accuracy of estimation. Furthermore, scholars engaged in the agricultural research mainly focus on adjusting the life cycle assessment (LCA) model, which has advantage on microlevel CF accounting, according to actual needs to achieve more accurate predictions, while researchers who pay attention to the topic of business economic are committed to improving the input–output model, which is suitable for meso and macro analysis, to enhance accounting accuracy. In general, this article is beneficial for presenting the intellectual structure and knowledge diffusion trajectories of the CF domain from horizontal and vertical perspectives.

Bilingual education is becoming more and more popular in China. The reasons behind this phenomenon are objective environmental factors, that is, the market demand for English talents is growing day by day. There should also be subjective individual factors, that is, bilingual learning has something to do with children’s own ability. However, the exploration of the impact of bilingual learning experiences on children’s cognitive development is not in-depth enough. This paper aims to discuss the findings of previous studies on bilingual learning experiences and children’s cognitive development. The core discovery demonstrates that bilingual learning experiences have beneficial influence on children’s cognitive ability development, including executive function and inhibitory control. The relation between bilingual learning experiences and children’s cognitive ability is positive. This paper has a reference value for future research on bilingual education experience and children’s cognitive development, and also has reference significance for parents who are considering their children’s education. Future researches should focus more on the relation between bilingual learning experiences and children’s cognitive development.

Metal halide perovskites are promising for next-generation flexible photodetectors owing to their low-temperature solution processability, mechanical flexibility, and excellent photoelectric properties. However, the defects and notorious ion migration in polycrystalline metal halide perovskites often lead to high and unstable dark current, thus deteriorating their detection limit and long-term operations. Here, we propose an electrical field modulation strategy to significantly reduce the dark current of metal halide perovskites-based flexible photodetector more than 1000 times (from ~5 nA to ~5 pA). Meanwhile, ion migration in metal halide perovskites is effectively suppressed, and the metal halide perovskites-based flexible photodetector shows a long-term continuous operational stability (~8000 s) with low signal drift (~4.2 × 10 −4 pA per second) and ultralow dark current drift (~1.3 × 10 −5 pA per second). Benefitting from the electrical modulation strategy, a high signal-to-noise ratio wearable photoplethysmography sensor and an active-matrix photodetector array for weak light imaging are successfully demonstrated. This work offers a universal strategy to improve the performance of metal halide perovskites for wearable flexible photodetector and image sensor applications. Defects and ion migration in perovskites hinder their potential as active material for flexible photodetectors. Here, the authors provide an electrical field modulation strategy to enhance the operational stability and the signal-to-noise ratio of flexible perovskite photodetectors.

Periodontitis drives irreversible destruction of periodontal tissue and is prone to exacerbating inflammatory disorders. Systemic immunomodulatory management continues to be an attractive approach in periodontal care, particularly within the context of ‘predictive, preventive, and personalized’ periodontics. The present study incorporated genetic proxies identified through genome-wide association studies for circulating immune cells and periodontitis into a comprehensive Mendelian randomization (MR) framework. Univariable MR, multivariable MR, subgroup analysis, reverse MR, and Bayesian model averaging (MR-BMA) were utilized to investigate the causal relationships. Furthermore, transcriptome-wide association study and colocalization analysis were deployed to pinpoint the underlying genes. Consequently, the MR study indicated a causal association between circulating neutrophils, natural killer T cells, plasmacytoid dendritic cells, and an elevated risk of periodontitis. MR-BMA analysis revealed that neutrophils were the primary contributors to periodontitis. The high-confidence genes S100A9 and S100A12 , located on 1q21.3, could potentially serve as immunomodulatory targets for neutrophil-mediated periodontitis. These findings hold promise for early diagnosis, risk assessment, targeted prevention, and personalized treatment of periodontitis. Considering the marginal association observed in our study, further research is required to comprehend the biological underpinnings and ascertain the clinical relevance thoroughly.

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware. Multiplexed spiking data coding schemes could enable artificial visual neurons to emulate the human visual system in a more biologically plausible way. Here, Li et al. present an artificial neuron device capable of encoding visual analog signals into spike trains using multiplexed rate and temporal fusion coding. Reviewer recognition:

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks.

Arctic shipping is a significant source of greenhouse gas (GHG) emissions, including carbon dioxide and black carbon, which intensify climate risks in the region. While the International Maritime Organization (IMO) has established the International Code for Ships Operating in Polar Waters (Polar Code) to address environmental and safety concerns of polar navigation, it falls short in promoting the decarbonization of Arctic shipping. The collaboration between the IMO and the Arctic Council, along with the contributions of the Arctic Council’s task forces, is essential but requires further strengthening. In response to the climate crisis, the IMO has raised environmental standards, leading efforts to promote low-carbon growth in Arctic shipping through measures such as sulfur limits, heavy fuel oil bans, and reductions in black carbon emissions. Despite these initiatives, the governance of Arctic shipping decarbonization remains fragmented. To achieve meaningful decarbonization, the Polar Code must be strengthened and expanded into a unified regulatory framework. Additionally, enhanced collaboration between the IMO and the Arctic Council is crucial to maximize their collective impact. As a key player in Arctic shipping, China must strengthen compliance with international regulations through updated domestic legislation and Arctic policies. By actively engaging in multilateral mechanisms and developing a port state control inspection network, China can play a pivotal role in advancing Arctic shipping governance and IMO energy efficiency standards, contributing to a more coordinated and sustainable approach to the region’s environmental challenges and global maritime governance.

Sestrins (Sesns), highly conserved stress-inducible metabolic proteins, are known to protect organisms against various noxious stimuli including DNA damage, oxidative stress, starvation, endoplasmic reticulum (ER) stress, and hypoxia. Sesns regulate metabolism mainly through activation of the key energy sensor AMP-dependent protein kinase (AMPK) and inhibition of mammalian target of rapamycin complex 1 (mTORC1). Sesns also play pivotal roles in autophagy activation and apoptosis inhibition in normal cells, while conversely promoting apoptosis in cancer cells. The functions of Sesns in diseases such as metabolic disorders, neurodegenerative diseases, cardiovascular diseases, and cancer have been broadly investigated in the past decades. However, there is a limited number of reviews that have summarized the functions of Sesns in the pathophysiological processes of human diseases, especially musculoskeletal system diseases. One aim of this review is to discuss the biological functions of Sesns in the pathophysiological process and phenotype of diseases. More significantly, we include some new evidence about the musculoskeletal system. Another purpose is to explore whether Sesns could be potential biomarkers or targets in the future diagnostic and therapeutic process.