Shanxi University

Brucellosis, a typical zoonotic disease, has long been a public health concern in China due to its high incidence and wide range of infections. Quantitative studies of epidemiological data are essential for understanding the dynamics of disease transmission, developing intervention programs, and eradicating infectious diseases. This paper proposes an improved data-driven method called sparsity-promoting Hankel dynamic mode decomposition (HDMDsp). This method is applied to characterize the epidemiological features of human brucellosis in China. By performing dynamic mode decomposition (DMD), this method extracts the spectral characteristics of the underlying system, allowing for the analysis of dynamic changes in infectious diseases. To improve prediction accuracy and simplify computation, this paper introduces a sparsity-promoting technique–elastic network regularization–to select the dominant model for reconstructing the evolutionary system of disease transmission. Additionally, a Hankel matrix is constructed via time-delay embedding to project the disease data into high dimensions, thereby enhancing the applicability of DMD. A study on human brucellosis in China demonstrates that the HDMDsp algorithm is highly effective at describing the spatiotemporal distribution of the disease and accurately predicting its transmission dynamics.

The direct photodetachment and two-photon photodissociation–photodetachment processes of a series of PtIn⁻ (n = 2–5) anions were systematically studied using cryogenic anion photoelectron spectroscopy and first-principles electronic structure calculations. The adiabatic/vertical detachment energies (ADEs/VDEs) of these anions were determined from their 193 nm photoelectron (PE) spectra, i.e., 3.54/3.63, 4.04/4.09, 4.33/4.36, and 4.37/4.41 eV for n = 2–5, respectively, and well reproduced by B3LYP-D3(BJ)/aug-cc-pVTZ-pp calculations. As the coordination number increases, the electron affinity (EA) of PtIn• (n = 2–5) neutrals (equivalent to the corresponding anion’s ADE) gradually increases, exceeding the EA of Cl at n = 3 and exhibiting superhalogen characteristics for n ≥ 3. Meanwhile, the ground state transition contributed from detaching electrons in the highest occupied molecular orbital gradually evolves from the central metal Pt to the iodine ligands. For the PtI3⁻ anion, besides one-photon direct detachment, four distinct two-photon photodissociation–photodetachment channels were identified, and the competition between them was discussed.

The spin transverse relaxation time (T2) of atoms is an important indicator for magnetic field precision measurement. Especially in optically-pumped atomic magnetometer, the linewidth of the magnetic resonance signal is one of the most important parameters of sensitivity, which is inversely correlated with T2 of atoms. In this paper, we propose four methods, namely spin noise spectroscopy signal fitting, radio-frequency free induction decay (RF-FID) signal fitting, ω m (modulation frequency)-broadening fitting, and magnetic resonance broadening fitting, for in-situ measurement T2 of atomic vapor cells based on light-atom interactions. Meanwhile, T2 of three Rubidium (Rb) atomic vapor cells with different parameters are measured and discussed by using these four methods. A comparative analysis visualizes the characteristics of the different methods and the effects of buffer gas on T2 of Rb atoms. Through theoretical and experimental analysis, we assess the applicability of each method and concluded that the RF-FID signal fitting method provides the most accurate measurements due to the timing sequence control system, which results in a cleaner measurement environment. Furthermore, we demonstrate and qualitatively analyze the relationship between temperature and T2 of Rb atoms. This work may offer valuable insights into the selection of atomic vapor cells and it is also applicable for the spin-exchange relaxation-free region.

Optogenetics, as an emerging interdisciplinary technology, has enabled the precise manipulation of neuronal activity by combining optical and genetic approaches. This paper summarizes the application of optogenetics in neuroscience and behavioral research and introduces its research background and topics. The application of optogenetics in neuronal activity regulation, neural loop resolution, exploration of learning and memory mechanisms, and treating neurological diseases were focused on. By combining a literature review and case analysis, we analyze the core principles of optogenetics and the technical points of optogenetics and demonstrate their potential applications in different research scenarios. The types and properties of photosensitive proteins and their application strategies in experiments are detailed, demonstrating the unique advantages of optogenetics in revealing the mysteries of neurobiological processes. The results show that optogenetic technology provides a powerful tool to reveal the mysteries of neurobiological processes, but it still faces challenges such as light transmission efficiency and gene delivery safety in practical applications. This paper summarizes the advantages and limitations of optogenetics and prospects for its future direction, providing an important reference for the continuous development of neuroscience and behavioral research.

Isogeometric analysis (IGA) has been widely used as a spatial discretization method for phase field models since the seminal work of Gómez et al. (Comput. Methods Appl. Mech. Engrg. 197(49), pp. 4333–4352, 2008), and the first numerical convergence study of IGA for the Cahn-Hilliard equation was presented by Kästner et al. (J. Comput. Phys. 305(15), pp. 360–371, 2016). However, to the best of our knowledge, the theoretical convergence analysis of IGA for the Cahn-Hilliard equation is still missing in the literature. In this paper, we provide the convergence analysis of IGA for the multi-dimensional Cahn-Hilliard equation for the first time. The two important steps to carry out the convergence analysis are (1) we rigorously prove that the $L^{\infty }$ norm of IGA solution is uniformly bounded for all mesh sizes, and (2) we construct an appropriate Ritz projection operator for the bi-Laplacian term in the Cahn-Hilliard equation. The first- and second-order stabilized semi-implicit schemes are used to obtain the fully discrete schemes. The energy stability analyses are rigorously proved for the resulting fully discrete schemes. Finally, several two- and three-dimensional numerical examples are presented to verify the theoretical results.

This study investigates the transverse momentum ($p_T$) spectra of identified light charged hadrons produced in gold–gold (Au+Au) collisions across various centrality classes at center-of-mass energies per nucleon pair, $\sqrt{s_{NN}}$, ranging from 7.7 to 200 GeV, as measured by the STAR Collaboration at the Relativistic Heavy Ion Collider (RHIC). The analysis employs standard (Bose-Einstein/Fermi-Dirac), Tsallis, and q-dual statistics to fit the same $p_T$ spectra and derive distinct effective temperatures: $T_{\text {Standard}}$, $T_{\text {Tsallis}}$, and $T_{\text {q-dual}}$. In most instances, there exists an approximately linear relationship or positive correlation between $T_{\text {Tsallis}}$ and $T_{\text {Standard}}$, as well as between $T_{\text {q-dual}}$ and $T_{\text {Standard}}$, when considering $T_{\text {Standard}}$ as a baseline. However, while both $T_{\text {Tsallis}}$ and $T_{\text {q-dual}}$ increase from semi-central to central Au+Au collisions at 62.4 GeV and 200 GeV, where QGP is expected, changes in $T_{\text {Standard}}$ occur more gradually. This work suggests that $T_{\text {Standard}}$ is better suited for characterizing phase transitions between hadronic matter and QGP compared to $T_{\text {Tsallis}}$ or $T_{\text {q-dual}}$, primarily due to the considerations related to entropy index in the Tsallis and q-dual statistics.

Erbium-doped thin-film lithium niobate (TFLN) lasers have attracted great interest in recent years due to their compatibility with high-speed electro-optic (EO) modulation on the same platform. In this work, high-efficiency single-mode erbium-doped microring lasers with milliwatt output powers were demonstrated. Monolithic lithium niobate microring resonators using pulley-waveguide-coupling were fabricated by the photolithography assisted chemo-mechanical etching (PLACE) technique. The maximum single-mode laser power of 1.26 mW with the side-mode suppression ratio (SMSR) of 50 dB was achieved around the wavelength of 1562 nm, as well as the maximum laser slope efficiency of 2.51% and the minimum laser linewidth of 30 kHz. Besides, the lasing band was easily switched by the pulley-coupler with variable waveguide widths. The demonstrated milliwatt-level on-chip microlasers hold great promise as bright light sources for various integrated devices on the TFLN platform such as EO modulators and combs.

MOFs‐modified nanofiltration (NF) membranes have been gained a lot of attention due to their favorable permeability and ion separation performance. Nevertheless, the prevailing preparation techniques are afflicted by the incompatibility of MOFs with polymers and the facile loss of MOFs. In this work, polyethyleneimine (PEI)‐templated ZIF‐8 (PEI‐ZIF‐8) was synthesized and incorporated into the PEI aqueous solution, then interfacial polymerized with trimesoyl chloride (TMC) to obtain the PEI‐ZIF‐8 modified polyamide NF membrane. This PEI modified strategy could endow the ZIF‐8 nanoparticles with positively charged properties to avoid the aggregation and increase the interfacial compatibility with the polyamide. Meanwhile, the appropriate pore size of ZIF‐8 (3.4 Å), which is between the hydration sheath surrounding of Li⁺ (2 Å) and Mg²⁺ (4.2 Å) impart the membrane with precise Mg²⁺/Li⁺ separation ability. The optimal PEI‐ZIF‐8‐TMC membrane exhibits a permeance of 9 L/h m²bar and a Mg²⁺/Li⁺ separation factor (SF) of 19, both of which surpass the performance of the pure PEI‐TMC membrane, which has a permeance of 4 L/h m²bar and a Mg²⁺/Li⁺ separation factor of 11. Meanwhile, the membrane exhibited excellent long‐term stability of 85 h. This novel approach to preparing MOFs‐modified NF membrane represents a promising avenue for the separation of lithium and magnesium.

Planar hypercoordination has intrigued researchers for decades, yet hydrogen remained absent from this category until recently. Despite some advancements, further exploration of the emerging field of planar hypercoordinate hydrogen chemistry is still necessary. Herein, we report a D4h symmetric H©Li4Au4⁻ cluster containing a planar tetracoordinate hydrogen (ptH) center. Both density functional theory (DFT) and ab initio calculations revealed that designed H©Li4Au4⁻ is a real global minimum. Meanwhile, it also possesses excellent dynamic stability. This stability is dominated by electrostatic interactions, and reinforced by multicenter covalent bonds rather than any aromaticity. Interestingly, it is the first superhalogen anion in the ptH cluster and is expected to experimental synthesis and characterization.

An effective nonmetal F‐doped Co3O4 (F‐Co3O4) catalyst was prepared using co‐precipitation method, and its catalytic performance was investigated for N2O decomposition comparing with pure Co3O4 catalyst. The catalytic activity test indicates that F‐Co3O4 catalyst exhibits better activity with 100% N2O conversion at a reaction temperature of 380 °C, which is 80 °C lower than that of pure Co3O4. The characterization results show that F is successfully doped into the lattice of Co3O4 and replaces part of O sites, which enlarges the surface area, enhances the surface basicity, and leads to higher basic sites density on the surface of Co3O4. Moreover, F doping promotes the electron donation capacity of Co²⁺, weakening of Co─O bond and generation of more oxygen vacancies. The synergy of the above factors results in reduction of activation energy over F‐Co3O4 catalyst, thus F‐Co3O4 catalyst exhibits better catalytic performance than pure Co3O4 for N2O decomposition, even in the existence of O2 or H2O as impurity gas. Meanwhile, F‐Co3O4 catalyst also exhibits better stability than pure Co3O4. This work will provide practical reference for constructing efficient nonmetal‐doped Co3O4 catalysts for N2O decomposition.

Functional groups (FGs) represent a classification scheme designed to study the ecological adaptations of phytoplankton. However, FG dynamics studies in phytoplankton are often conducted independent of taxonomic studies, so the factors influencing community dynamics have not been sufficiently investigated or compared between the two classification systems. In this study, we compared the intricate relationship between taxonomic and FG compositions in North China lakes and delve into the key environmental drivers shaping phytoplankton community dynamics. This investigation revealed that taxonomic and FG classifications exhibit high qualitative and quantitative similarities in the community structure. Environmental drivers had a stronger influence on the FG structure than taxonomic composition, indicating that the FG classification does not result in the loss of ecological information regarding the community structure, even with the reduced number of grouping units. Indeed, it was evident that FGs contained a larger quantity of ecological information. These conclusions were further verified using lakes in eastern China. Additionally, we found that climatic–geographical factors usually exerted indirect influences, by altering water chemistry, while water chemical factors had more direct and stronger influences. The combined effects of both types of environmental factors had a greater impact on the phytoplankton FG structure than on taxonomic composition. In conclusion, we believe that an in‐depth study of FGs will better focus on the ecological characteristics of phytoplankton, while also avoiding the need for extensive species identification.

Novel fluorescent superparamagnetic nanocomposites have been fabricated by introduction of the coumarin group on the surface of amine-functionalized magnetite-silica nanocomposites, and characterized by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy, fluorescence spectra, dynamic light scattering and vibrating sample magnetometer techniques. The nanocomposites were employed as delivery vehicles of a photoactive platinum diimine complex. The cellular uptake and photocytotoxicity of the photosensitizer-loaded nanocomposites in HeLa cells (human cervical cancer line) or HL-7702 cells (human liver cell line) have been studied by fluorescence spectra and cell viability assay, respectively. The results suggest that the nanocomposites can be used to monitor the cellular uptake of the photosensitizer, and can significantly enhance the photocytotoxicity of the photosensitizer towards cancer cells when employed as carriers of the photosensitizer. Also, the photosensitizer-loaded nanocomposites are almost nontoxic to human normal cells either in the dark or after irradiation.

Reinforcement Learning (RL) knowledge graph reasoning aims to predict complete triplets by learning existing relationship paths. This greatly improves the efficiency of prediction because the RL-based methods do not traverse all entities and relations like representation reasoning. Meanwhile, this kind of method increases the interpretability of reasoning. However, due to the necessity of normalizing the entity outdegree matrices for neural network computations in each step of the retrieval process in reinforcement learning, entities with an excessively high number of outdegrees compel the RL-based model to restrict the retrieval space of each path. Consequently, this limitation leads to the omission of some correct answers. Moreover, for some isolated tail entities with sparse connections, this path-based reasoning will lose these island nodes. To solve both problems, we propose an analogy-based reinforcement learning model named Analogical Reinforcement Learning network (ARL). This model features a novel analogy reinforcement learning architecture, dynamic graph attention networks, and our proprietary AODS algorithm. It injects entity analogy information into the model’s reasoning process and employs virtual link generation, which not only enhances the probability of paths getting rewards, but also increases the breadth of path connection and brings more possibilities for island nodes. In the meantime, we analyze and compare various analogy methods in detail. Experimental results show that ARL outperforms existing multi-hop methods on several datasets.

The taxonomic concepts and phylogenetic relations among genera of the family Mniaceae have given rise to much controversy in recent years, including Mnium, Plagiomnium, and Pohlia. Chloroplast genome study of these genera will be helpful to reflect the fact of this relationship. In this study, we sequenced three species in the Plagiomnium genus using an Illumina HiSeq 4000 platform. The complete chloroplast genomes of P. rostratum, P. succulentum and P. vesicatum were 125,196 bp, 124,689 bp, and 124,663 bp in length, which all contained a quadripartite structure including two copies of the invert repeats (IR, 10,120 bp, 9,818 bp, and 9,665 bp), one large single copy region (LSC, 86,395 bp, 86,299 bp, and 86,532 bp), and one single copy region (SSC, 18,561 bp, 18,754 bp, and 18,801 bp). The overall GC contents were 29.8%, 30.5%, and 30.5% respectively. The simple sequence repeats (SSRs) were detected in conjunction with Plagiomnium acutum, with variable sites genes observed: rpoC2, ycf1, and ycf2. Combined with the other three sequences published in Mniaceae, analyses of codon usage, repeats sequences, GC contents, and gene features revealed similarities among the seven species in Mniaceae. The trend of nucleotide diversity (Pi) in the seven complete chloroplast genomes showed Pi > 0.056: trnI-rpl23, petG-petL-psbE, trnK-chlB, trnG-trnR-atpA, rpoB-trnC-ycf66, ndhB, trnN-ndhF, and rps15-ycf1. We confirmed the phylogenetic relationships that Plagiomnium genus is a sister group with Mnium, while the Pohlia genus is not a monophyletic group. Phylogenetic analyses corroborated the monophyly of Mniaceae and supported the transfer of the Pohlia genus into Mniaceae.

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important; however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. All algorithms can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/OPF-Miner .

Inferring causal structures from experimentation is a challenging task in many fields. Most causal structure learning algorithms with unknown interventions are proposed to discover causal relationships over an identical variable set. However, often due to privacy, ethical, financial, and practical concerns, the variable sets observed by multiple sources or domains are not entirely identical. While a few algorithms are proposed to handle the partially overlapping variable sets, they focus on the case of known intervention targets. Therefore, to be close to the real-world environment, we consider discovering causal relationships over overlapping variable sets under the unknown intervention setting and exploring a scenario where a problem is studied across multiple domains. Here, we propose an algorithm for discovering the causal relationships over the integrated set of variables from unknown interventions, mainly handling the entangled inconsistencies caused by the incomplete observation of variables and unknown intervention targets. Specifically, we first distinguish two types of inconsistencies and then deal with respectively them by presenting some lemmas. Finally, we construct a fusion rule to combine learned structures of multiple domains, obtaining the final structures over the integrated set of variables. Theoretical analysis and experimental results on synthetic, benchmark, and real-world datasets have verified the effectiveness of the proposed algorithm.