The rapid development of high-throughput sequencing technologies has catalyzed a paradigm shift in biological research toward integrative multi-omics analysis.In this context,a key challenge lies in extracting meaningful insights from highly complex omics data while constructing models that are both accurate and interpretable.Although artificial intelligence(AI)has demonstrated powerful capabilities in biological data analysis,its inherent"black-box"nature significantly limits its reliability and acceptance in both basic research and clinical applications.Explainable artificial intelligence(XAI)offers a promising solution by enhancing model transparency and interpretability,thereby facilitating trust and knowledge discovery.In this study,we present a novel biologically informed interpretable neural network framework specifically designed for modeling transcriptional regulatory systems.Applied to gene expression prediction tasks,our method achieves outstanding performance while successfully uncovering a two-step cis-regulatory mechanism underlying zygotic genome activation(ZGA).This work provides a methodological reference for the application of XAI in the analysis of complex multi-omics data.
Organic semiconductor nanostructured materials possess the advantage of weak spin-orbit coupling(SOC),which endows them with long spin lifetimes at room temperature and makes them highly promising for room-temperature spintronic applications.The rich photoelectric functionalities and spinterface based on these materials give rise to several novel multifunctionalities in organic spintronic devices.Focusing on the chemical design of efficient spin transport organic semiconductor nanostructured materials and the emerging spin device functionalities,this manuscript reviews the important research progress of this field in recent years and points out the key challenges that need to be overcome urgently,providing a reference for the future development of organic spintronics.
The C-paired spin-momentum locking(CSML)reveals the origin of exotic phenomena in spin-splitting antiferromagnets(AFMs)—stemming from the collective response of quantum degrees of freedom(spin,orbital,topology,etc.)at symmetry-connected momentum in reciprocal space under static or dynamic states.Here,we report the realization of CSML in a layered room-temperature antiferromagnetic compound,Rb intercalated V2Te2O.Spin resolved photoemission measurements directly demonstrate the opposite spin splitting between C-paired valleys.Quasi-particle interference patterns reveal the suppression of inter-valley scattering due to the spin selection rules,as a direct consequence of CSML.All these experiments are well consistent with the results obtained from first-principles calculations.Our observations represent the first realization of layered antiferromagnets with CSML,enabling both the advantages of layered materials and possible control through crystal symmetry manipulation.These results hold significant promise and broad implications for advancements in magnetism,electronics,and information technology.
High-strength low-alloy(HSLA)steels are widely used in critical fields such as offshore engineering,oil and gas pipelines,and nuclear power equipment due to their excellent mechanical properties and industrial cost-effectiveness.However,these steels are susceptible to stress corrosion cracking(SCC)in service environments,making SCC one of the primary causes of component failure.This paper summarizes the factors influencing SCC in HSLA steels,including alloy composition,microstructure,residual stress,and corrosive media.The SCC mechanisms in HSLA steels are discussed.The protective methods against SCC are discussed with a focus on microstructural design/composition optimization,surface treatment,and cathodic protection.Finally,a roadmap for the future development of SCC mitigation in HSLA steels is outlined.
Large non-primate mammals such as pigs,cattle,and sheep exhibit pronounced species-specific characteristics during early embryonic development.Notably,they differ significantly from classical model organisms like mice in key developmental events such as embryonic morphogenesis,the initiation of gastrulation,the establishment of the three germ layers,and the origin and migration of primordial germ cell(PGC).In certain respects,their developmental features are more closely aligned with those of humans.With the advancement of omics technologies such as single-cell transcriptomics and spatial transcriptomics,along with the establishment of in vitro modeling systems based on organoids and stem cells,researchers are now able to systematically resolve the cell lineage trajectories and molecular mechanisms underlying embryonic development in these large animals at high resolution.This review focuses on three representative non-primate mammals—pig,cattle,and sheep—providing a comprehensive overview of their developmental features during the peri-implantation stage,embryo-maternal interactions,germ layer differentiation,body axis formation,and PGC fate specification.It also compares these features with those of humans and mice,highlighting the potential applications of large non-primate mammals in basic reproductive research,precision breeding,and human medicine.
Conventional vision systems suffer from high energy consumption and latency due to the physical separation of sensing,memory,and computation.Emulating the human visual system,integrated brain-like architectures offer a promising route to real-time and efficient information processing.Two-dimensional semiconductors,with their van der Waals layered structure,dangling-bond-free surfaces,flexible stacking,and strong field tunability,provide an excellent platform for such systems.Coupled with ferroelectric materials that generate reversible and non-volatile strong interfacial electric fields,they enable efficient state regulation and novel brain-like device functionalities.This synergy,combining two-dimensional semiconductors with ferroelectric materials,is expected to overcome the limits of traditional architectures,leading to highly sensitive,low-power in-sensor memory computing devices and advancing next-generation brain-inspired intelligence technologies.
With the explosive growth of network terminals and data carried by services,the existing network is no longer capable of providing service carrying capacity with higher standards,and is confronted with problems such as rigid network structure,simple bearing structure based on IP,and ineffective resource supplying.The development of technologies such as artificial intelligence,edge computing,and computing power networks drives the deep integration of network and computing,promoting the formation of a new type of information infrastructure characterized by cloud and network convergence.In response to the evolution of information infrastructure in the era of intelligent computing,this paper proposes the target architecture of polymorphic intelligent connection computing network,sorts out the key technologies and operation mechanisms of polymorphic intelligent connection computing network,and elaborates on the technical solutions for the coordination of storage,transmission,and computing in polymorphic intelligent connection computing network through current typical application scenarios,providing a new technical evolution idea for cloud and network convergence.
According to symmetry analysis,the electric field is a polar vector with time-reversal even symmetry,and the magnetic field is an axial vector with time-reversal odd symmetry.This fundamental difference in symmetry prevents the electric field from directly and reversibly altering a material's magnetization direction.Over the past decade,magnetization switching driven by current-induced spin-orbit torque has emerged as a significant research direction in spintronics.However,due to symmetry constraints,conventional spin-orbit torques cannot independently achieve deterministic magnetization switching.This paper examines the fundamental principles of current-induced magnetization switching from a symmetry perspective.It clarifies the crystal symmetry constraints on unconventional spin-orbit torques and summarizes recent approaches that exploit crystal symmetry breaking to enable field-free magnetization switching.Finally,the paper discusses the challenges and future prospects of designing spin-orbit torques with symmetry to inspire fundamental research and device applications.
Space-based gravitational wave detection serves as a highly effective approach for detecting low-frequency gravitational wave signals.Compared to ground-based counterparts,it can circumvent the interference of terrestrial environment noise and achieve higher sensitivity and a broader detection frequency band.However,it is confronted with great engineering and technical difficulties.Space-based gravitational wave detection architectures are broadly classified into heliocentric and geocentric orbital schemes.This paper focuses specifically on the latter.It reviews the current research status of constellation construction and control technologies for geocentric orbits and summarizes the challenges inherent in the control technologies of this scheme.Based on this analysis,this paper proposes recommendations for future research on the key technologies of constellation construction and control within the geocentric orbit framework.These insights aim to provide a theoretical reference for the engineering implementation of future space-based gravitational wave detection missions,thereby facilitating the transition of the geocentric orbit scheme from conceptual design to practical application.
Anion exchange membrane fuel cell(AEMFC),benefiting from the intrinsically low corrosivity of its alkaline environment,offers significant potential to reduce or even eliminate reliance on platinum(Pt)-based precious metals,thereby lowering fuel cell costs and accelerating commercialization.However,the sluggish kinetics of the hydrogen oxidation reaction(HOR)at the anode and the difficulty of developing non-platinum catalysts remain major challenges for AEMFC development.This study addresses the key scientific issues of low activity and poor oxidation resistance in AEMFC anode HOR catalysts by introducing two strategies based on charge-transfer modulation to enhance catalytic performance.First,interfacial bonding with TiO2 enables directional electron transfer,selectively tuning the d-orbital occupancy of Ru-based catalysts,thereby improving both HOR activity and oxidation resistance.Second,leveraging quantum confinement effects,a Ni@C-MoOx quantum-well catalytic architecture is constructed,where chemisorption triggers gated charge transfer,effectively suppressing Ni electro-oxidation and enabling non-precious metal anode AEMFC to resist hydrogen starvation.
With the surging demand for real-time perception-decision-control closed-loop in distributed embodied intelligent scenarios,traditional cloud-native architectures face severe challenges in concurrent processing and deterministic latency.This paper proposes a 50G-PON end-edge-cloud collaborative architecture for ubiquitous access of intelligent agents.By implementing hierarchical computing resource allocation and deterministic transmission mechanisms,it addresses latency jitter and bandwidth bottlenecks in multi-agent concurrent scenarios.Experimental results demonstrate that under 20 concurrent request testing conditions,the cloud-side brain achieves video understanding inference latency of(62.4±1.2)ms and perception-decision command issuance frequency of 16 Hz;the edge-side cerebellum provides motion control inference latency of(39.2±0.8)ms and motion planning command issuance frequency of 25 Hz.Compared with traditional Ethernet architectures(average latency＞150 ms),this architecture improves latency stability by 58.4%and ensures bandwidth isolation for 20 concurrent streams through 50G-PON technology.The experimental data validate that the proposed end-edge-cloud architecture meets current concurrency requirements for intelligent agents.
Due to the increasing energy crisis and environmental pollution,it is urgent to develop new clean energy.However,due to its intermittent and regional characteristics,the efficient utilization of these energy sources through electrocatalytic conversion reactions has emerged as a key research focus.Currently,some commercially utilized alloy electrocatalysts often face challenges such as rapid degradation of catalytic activity,poor selectivity,and environmental unfriendliness.The adoption of a high-entropy strategy can effectively address these issues.As an emerging nanomaterial,high-entropy alloys(HEAs)primarily consist of no fewer than five metallic elements and show obvious advantages,including highly tunable compositions,abundant surface active sites,excellent chemical/electrochemical stability,etc.These properties enable effectively optimization of electronic structures and enhancement of atomic utilization,demonstrating broad application prospects.This paper starts with the synthesis strategies of HEAs,systematically elaborates on several common preparation methods,and introduces the latest advancements in electrocatalysis and secondary batteries.Finally,the current challenges for HEAs are summarized,and their future development is projected.
We investigate the initial-boundary value problem of the three-dimensional compressible viscoelastic fluids with the electrostatic effect, in which the Dirichlet-Neumann mixed boundary condition for the electrostatic potential is imposed. We prove that there exists a unique global-in-time small strong solution in H2 Sobolev space. Moreover, we show that such a solution converges to the constant equilibrium state with an exponential decay rate as time tends to infinity.
ABSTRACT The widespread use of ChatGPT has normalized the dialogue Turing test. To meet this challenge, China's major national development strategy suggests that for a new generation of artificial intelligence, it is first necessary to answer the big questions raised by Turing in 1950 from the perspective of cognitive physics: Can machines think? How do machines think? How do machines cognize? Whether it is carbon-based human cognition or silicon-based machine cognition, it is an interaction between complex constructs composed of the four most basic elements: matter, energy, structure, and time. Both humans and machines depend on negative entropy for living, and time is the cornerstone of cognition. Structure and time are parasitic on matter and energy in physical space, forming hard-structured ware. The soft-structured ware in cognitive space is mind, which is parasitic on the hard-structured ware or other existing soft-structured ware, and constitutes a rich hierarchy of multi-scale feelings, concepts, information, and knowledge. Extending “abstraction” from the symbolic school of artificial intelligence, “association” from the connectionist school, and “interaction” from the behaviorist school, the core of cognition is established on the shoulders of such scientific giants such as Schrödinger, Turing and Wiener. Soft and hard-structured ware interact. Cognitive machine can comprise heterogeneous hard-structured ware, such as field programmable gate arrays (FPGAs), data processing units (DPUs), central processing units (CPUs), graphics processing units (GPUs), tensor processing units (TPUs), and memory. It can also be implanted with the “Baby Cognitive Nucleus” which is hard-structured ware genetically inherited and naturally evolved to form the embodied machine. Then the hard-structured ware is parasitized by rich, multi-scale soft-structured ware. By regulating matter and energy through soft-structured ware, machines produce orderly events, form coordinated and orderly thinking activities. The heterogeneous sensors configured by the machine and the speed of thinking will no longer be trapped by the extreme values of biochemical parameters of carbon-based organisms but will be able to perceive through multi-channel cross-modal means, carry out intense thinking, and maintain cognitive continuity with memory. To generate computational and memory intelligence in cognitive space which can bootstrap, self-reuse and self-replicate, imagination and creativity are improved through memory-constrained computing. The new generation of artificial intelligence will leap beyond mechanized mathematics to automation in thinking and self-driven growth of cognition, and the thinking in the cognitive space and the behavior in the physical space verify each other, from the dialogue Turing test to the embodied Turing test. Humans have entered the intelligent era of human-machine co-creation with cognitive machines iteratively inventing, discovering, and creating alongside scientists, engineers, and skilled craftsmen, each wise in its way, improving thinking ability, and amplifying human energy.
空间堆作为一种可以改写航天器用途的关键电源技术备受航天大国关注.阐述一种热电一体化空间堆构想,通过合并堆芯系统和热电转化系统,提高系统的比功率,实现非重力自动循环能力.提出斯特林热电一体化空间堆,并对其在掉落事故时的安全特性进行研究,分析堆芯高径比、谱移材料添加方式、反射层、掉落环境和燃料密实效应对中子有效增殖因子的影响,提出在UN燃料元件中增加 3.8 at%的谱移材料 151Eu2O3的保证措施.研究结果表明,该方案符合在12种事故工况及密实效应发生时有效增殖因子均小于0.98的掉落安全准则.
增强现实、虚拟现实等新兴领域的发展,对显示技术的分辨率提出了更高的要求.对量子点发光二极管(QLED)而言,目前量子点薄膜的像素化主要通过喷墨打印、纳米压印和光刻实现.其中,纳米压印具有超高分辨率、高产量、低成本的优势,适合亚微米级图案化.通过对目前的电场驱动纳米像元QLED器件图案化技术进行对比和总结,分析纳米压印进行QLED器件图案化的问题与可行性,并讨论超高分辨率QLED阵列的制备方案.
赤铁矿是一种具有潜力的光电催化材料,已被广泛用于水氧化反应研究.在该反应中,赤铁矿受光激发后会在表面形成捕获态空穴,即高价铁氧物种(FeIV=O).其受水分子的亲核进攻形成氧—氧键,且遵循质子耦合电荷转移机制,这一观点已得到动力学同位素效应、电化学阻抗谱以及原位光电化学红外光谱等实验证据的支持.在此基础上,受高价金属—氧物种诱导的氧原子转移(oxygen atom transfer,OAT)反应的启发,赤铁矿催化的有机相OAT反应取得了突破性进展,开辟了赤铁矿光电催化应用的新方向.这种高效的OAT机制被进一步拓展到一些典型的无机物(氨、亚硝酸盐等)氧化反应,为环境污染物的去除提供了新的思路.
宏模块布局是超大规模集成电路(very large scale integration,VLSI)物理设计的核心环节之一,对集成电路的性能有重大影响.随着越来越多的知识产权核和其他预先设计的宏模块被广泛采用,VLSI通常集成数百个甚至上千个宏模块,给其布局带来了巨大挑战.基于此,聚焦VLSI宏模块布局研究,首先介绍VLSI布局的研究背景;其次阐述 2D宏模块布局算法的主要类型和发展,包括基于解析和基于打包的 2D宏模块布局算法;最后探讨宏模块布局未来的研究趋势,主要包括 3D宏模块布局和基于机器学习的宏模块布局方法.
