General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs)1,2 and chain-of-thought (CoT) prompting3, have achieved considerable success on foundational reasoning tasks. However, this success is heavily contingent on extensive human-annotated demonstrations and the capabilities of models are still insufficient for more complex problems. Here we show that the reasoning abilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labelled reasoning trajectories. The proposed RL framework facilitates the emergent development of advanced reasoning patterns, such as self-reflection, verification and dynamic strategy adaptation. Consequently, the trained model achieves superior performance on verifiable tasks such as mathematics, coding competitions and STEM fields, surpassing its counterparts trained through conventional supervised learning on human demonstrations. Moreover, the emergent reasoning patterns exhibited by these large-scale models can be systematically used to guide and enhance the reasoning capabilities of smaller models. A new artificial intelligence model, DeepSeek-R1, is introduced, demonstrating that the reasoning abilities of large language models can be incentivized through pure reinforcement learning, removing the need for human-annotated demonstrations.
There has recently been a dramatic renewal of interest in hadron spectroscopy and charm physics. This renaissance has been driven in part by the discovery of a plethora of charmonium-like XYZ states at BESIII and B factories, and the observation of an intriguing proton-antiproton threshold enhancement and the possibly related X(1835) meson state at BESIII, as well as the threshold measurements of charm mesons and charm baryons. We present a detailed survey of the important topics in tau-charm physics and hadron physics that can be further explored at BESIII during the remaining operation period of BEPCII. This survey will help in the optimization of the data-taking plan over the coming years, and provides physics motivation for the possible upgrade of BEPCII to higher luminosity.
High-energy cosmic-ray electrons and positrons (CREs), which lose energy quickly during their propagation, provide a probe of Galactic high-energy processes and may enable the observation of phenomena such as dark-matter particle annihilation or decay. The CRE spectrum has been measured directly up to approximately 2 teraelectronvolts in previous balloon- or space-borne experiments, and indirectly up to approximately 5 teraelectronvolts using ground-based Cherenkov γ-ray telescope arrays. Evidence for a spectral break in the teraelectronvolt energy range has been provided by indirect measurements, although the results were qualified by sizeable systematic uncertainties. Here we report a direct measurement of CREs in the energy range 25 gigaelectronvolts to 4.6 teraelectronvolts by the Dark Matter Particle Explorer (DAMPE) with unprecedentedly high energy resolution and low background. The largest part of the spectrum can be well fitted by a ‘smoothly broken power-law’ model rather than a single power-law model. The direct detection of a spectral break at about 0.9 teraelectronvolts confirms the evidence found by previous indirect measurements, clarifies the behaviour of the CRE spectrum at energies above 1 teraelectronvolt and sheds light on the physical origin of the sub-teraelectronvolt CREs.
High quality factor resonances are extremely promising for designing ultra-sensitive refractive index label-free sensors, since it allows intense interaction between electromagnetic waves and the analyte material. Metamaterial and plasmonic sensing have recently attracted a lot of attention due to subwavelength confinement of electromagnetic fields in the resonant structures. However, the excitation of high quality factor resonances in these systems has been a challenge. We excite an order of magnitude higher quality factor resonances in planar terahertz metamaterials that we exploit for ultrasensitive sensing. The low-loss quadrupole and Fano resonances with extremely narrow linewidths enable us to measure the minute spectral shift caused due to the smallest change in the refractive index of the surrounding media. We achieve sensitivity levels of 7.75 × 103 nm/refractive index unit (RIU) with quadrupole and 5.7 × 104 nm/RIU with the Fano resonances which could be further enhanced by using thinner substrates. These findings would facilitate the design of ultrasensitive real time chemical and biomolecular sensors in the fingerprint region of the terahertz regime.
Reaction networks have become a major modelling framework in the biological sciences from epidemiology and population biology to genetics and cellular biology. In recent years, much progress has been made on stochastic reaction networks (SRNs),modelled as continuous time Markov chains (CTMCs) and their stationary distributions. We are interested in complex-balanced stationary distributions, where the probability flow out of a complex equals the flow into the complex. We characterise the existence and the form of complex-balanced distributions of SRNs with arbitrary transition functions through conditions on the cycles of the reaction graph (a digraph). Furthermore, we give a sufficient condition for the existence of a complex-balanced distribution and give precise conditions for when it is also necessary. The sufficient condition is also necessary for mass-action kinetics (and certain generalisations of that) or if the connected components of the digraph are cycles. Moreover, we state a deficiency theorem, a generalisation of the deficiency theorem for stochastic mass-action kinetics to arbitrary stochastic kinetics. The theorem gives the co-dimension of the parameter space for which a complex-balanced distribution exists. To achieve this, we construct an iterative procedure to decompose a strongly connected reaction graph into disjoint cycles, such that the corresponding SRN has equivalent dynamics and preserves complex-balancedness, provided the original SRN had so. This decomposition might have independent interest and might be applicable to edge-labelled digraphs in general.
Dyes are hazardous chemicals that are commonly found in textile industries’ effluent water. Adsorption techniques are more efficiently used for the removal of various dyes from wastewater. The present work deals with the synthesis of Mn-Zn (Mn0.3Zn0.7Fe2O4) spinel ferrite through the solution combustion method. The solution combustion method has many advantages over other conventional methods. Single-phase spinel ferrite materials can be synthesized by using this method at lower temperatures and in a shorter time. Synthesized Mn-Zn spinel ferrite material has been characterized by using FT-IR spectroscopy. As synthesized ferrite material has been employed for the adsorption of various dyes with different concentrations from their aqueous solutions. Results related to dyes’ adsorption have been reported using UV-Visible spectroscopy. Mn- Zn spinel ferrite has worked efficiently as an adsorbent and its magnetic nature is useful for its extraction from the aqueous solution.
Luis F Seoane, Henry Secaira-Morocho, Ester Lázaro
et al.
Understanding how viral mutant spectra organize and explore genotype space is essential for unraveling the mechanisms driving evolution at the finest scale. Here we use deep-sequencing data of an amplicon in the A2 protein of the RNA bacteriophage Q$β$ to reconstruct genotype networks with tens of thousands of different haplotypes. The study of populations evolved under different temperature regimes uncovers generic topological features conditioned by fundamental structural motifs of genotype networks -- tetrahedrons, triangles, and squares -- that govern their local architecture. Mutant swarms display a hierarchical structure where sequences cluster around a highly connected and abundant sequence core that sustains population diversity. The immediate neighborhood of this core is comprehensively sampled, with no signs of selection, while a few mutations away sampling becomes dynamical and sparse, showing signs of purifying selection. By aggregating genotype networks from populations adapted to different temperatures, we capture the early stages of evolutionary divergence, with overlapping populations that remain connected through short mutational paths. Even at the time scale of these experiments, evolutionary pathways might be multiple, preventing the backward reconstruction of unique trajectories once mutations have been fixed. This analysis provides a detailed view of the local, fine-scale processes shaping viral quasispecies evolution and underscores the usefulness of genotype networks as an enlightening visualization of the organization of mutant swarms.
Photonic crystal nanobeam cavities are versatile platforms of interest for optical communications, optomechanics, optofluidics, cavity QED, etc. In a previous work [Appl. Phys. Lett. 96, 203102 (2010)], we proposed a deterministic method to achieve ultrahigh Q cavities. This follow-up work provides systematic analysis and verifications of the deterministic design recipe and further extends the discussion to air-mode cavities. We demonstrate designs of dielectric-mode and air-mode cavities with Q > 10⁹, as well as dielectric-mode nanobeam cavities with both ultrahigh-Q (> 10⁷) and ultrahigh on-resonance transmissions (T > 95%).
Cosmography is an ideal tool to investigate the cosmic expansion history of the Universe in a model-independent way. The equations of motion in modified theories of gravity are usually very complicated; cosmography may select practical models without imposing arbitrary choices a priori. We use the model-independent way to derive $f(z)$ and its derivatives up to fourth order in terms of measurable cosmographic parameters. We then fit those functions into the luminosity distance directly. We perform the MCMC analysis by considering three different sets of cosmographic functions. Using the largest supernovae Ia Pantheon sample, we derive the constraints on the Hubble constant $H_0$ and the cosmographic functions, and find that the former two terms in Taylor expansion of luminosity distance work dominantly in $f(Q)$ gravity.
One of the coal mining corporations is in charge of environmental management, which includes managing water resources One of the coal mining corporations is in charge of environmental management, which includes managing water resources. The management of coal mine water that has the potential to become polluted water can be processed into fresh water in a sustainable manner. This indicates that acidic water from coal mines is still handled as waste that must be addressed once more. According Ministry of Health Regulation No. 416 1990 on Water Quality Requirements and Monitoring (Indonesian Government Regulation Ministry of Health related to water quality requirements and water quality monitoring), the purpose of this study is to evaluate the viability of coal mine acidic water quality in comparison to Fe (iron), Mn (manganese) ions, and pH parameters as fresh water. According to the findings of this study into acid mine drainage, the pH of the water in nine study areas in Pucok Reudeup Village, a former coal mining region, is low. The findings of this study on acid mine drainage demonstrate that the pH of the water at nine research sites in Pucok Reudeup a village near a disused coal mine. In nine research locations in Pucok Reudeup Village, a former coal mining area, the pH of the water did not exceed the established freshwater quality requirements (pH water below 6.5 - 9), according to the findings of this acid mine drainage study. Fe parameters that fulfilled the quality requirements were only present at five research sites. High iron concentrations result from Fe 2+ or Fe 3+ ions that cannot obtain oxygen from the environment as well as from the stripping of soil and rocks that are predominately composed of iron minerals, necessitating further management such as aeration. All study sites' Mn (manganese) parameters continue to fall short of the quality requirements outlined in No. Minister of Health Regulation 416 of 1990 Requirements on water quality No. Minister of Health Regulation 416 of 1990 Requirements on water quality and monitoring, that is, always exceeding the quality standard by 0.5 mg/L, except for stations 4, 5 and 6 There are 3 locations of Mn (manganese). In general, it can be concluded that the Fe and pH parameters of the study area still do not meet the freshwater quality standards. While Mn partially meets freshwater quality standards atstations (4, 5 and 6). Stations 1, 2, 3, 7, 9 are always higher than fresh water quality standards. The problem of acid mine drainage requires environmentally friendly water treatment.
Gene regulatory relationships can be abstracted as a gene regulatory network (GRN), which plays a key role in characterizing complex cellular processes and pathways. Recently, graph neural networks (GNNs), as a class of deep learning models, have emerged as a useful tool to infer gene regulatory relationships from gene expression data. However, deep learning models have been found to be vulnerable to noise, which greatly hinders the adoption of deep learning in constructing GRNs, because high noise is often unavoidable in the process of gene expression measurement. Can we preferably prototype a robust GNN for constructing GRNs? In this paper, we give a positive answer by proposing a Quadratic Graph Attention Network (Q-GAT) with a dual attention mechanism. We study the changes in the predictive accuracy of Q-GAT and 9 state-of-the-art baselines by introducing different levels of adversarial perturbations. Experiments in the E. coli and S. cerevisiae datasets suggest that Q-GAT outperforms the state-of-the-art models in robustness. Lastly, we dissect why Q-GAT is robust through the signal-to-noise ratio (SNR) and interpretability analyses. The former informs that nonlinear aggregation of quadratic neurons can amplify useful signals and suppress unwanted noise, thereby facilitating robustness, while the latter reveals that Q-GAT can leverage more features in prediction thanks to the dual attention mechanism, which endows Q-GAT with the ability to confront adversarial perturbation. We have shared our code in https://github.com/Minorway/Q-GAT_for_Robust_Construction_of_GRN for readers' evaluation.
Optimizing the properties of molecules (materials or drugs) for stronger toughness, lower toxicity, or better bioavailability has been a long-standing challenge. In this context, we propose a molecular optimization framework called Q-Drug (Quantum-inspired optimization algorithm for Drugs) that leverages quantum-inspired algorithms to optimize molecules on discrete binary domain variables. The framework begins by encoding the molecules into binary embeddings using a discrete VAE. The binary embeddings are then used to construct an Ising energy-like objective function, over which the state-of-the-art quantum-inspired optimization algorithm is adopted to find the optima. The binary embeddings corresponding to the optima are decoded to obtain the optimized molecules. We have tested the framework for optimizing drug molecule properties and have found that it outperforms other molecular optimization methods, finding molecules with better properties in 1/20th to 1/10th of the time previously required. The framework can also be deployed directly on various quantum computing equipment, such as laser pulses CIMs, FPGA Ising Machines, and quantum computers based on quantum annealing, among others. Our work demonstrates a new paradigm that leverages the advantages of quantum computing and AI to solve practically useful problems.
Agarwood is the resinous heartwood obtained from the injured parts of trees under Thymelaeaceae family particularly from Aquilaria species. Agarwood is considered as the most prized non-timber forest product (NTFP) used in attars as well as medicines. Quality of agarwood is the determiner for defining its commercial value. Different countries use different grading system to explain the quality of agarwood. In Bangladesh, more than 45 compounds have been identified so far, mostly sesquiterpenoids, alkanes, fatty acid and other volatile aromatic compounds that are responsible for its fragrance and high prices. Ether extract, total phenolic contents, flavonoid, antioxidant and microbiological tests have shown tremendous positive results. Chemical profiling of agar products is the best identified using GC-MS technique by the various authors. Present review discusses the full scenario of Aquilaria production, inoculation, extraction methods, determination of agarwood quality and chemical constituents of agar oils and possibilities and barriers of this industry in Bangladesh. J. Bio-Sci. 29(2): 151-173, 2021 (December)