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Empirical analysis of the correlation between China’s Macroeconomic Market and Crude Oil Market based on mixed-frequency group factor model
This paper examines the asymmetric correlation and dynamic interaction between China’s macroeconomic market and the global crude oil market, addressing a critical limitation in existing literature: the frequency mismatch between high-frequency (daily) crude oil data and low-frequency (monthly) macroeconomic data. To resolve this, we employ a mixed-frequency group factor model that decomposes volatility drivers into two mutually exclusive components: (1) common factors, which capture cross-market spillovers between the two markets; and (2) group-specific factors, including low-frequency (LF)-specific factors (for China’s macroeconomic indicators) and high-frequency (HF)-specific factors (for crude oil prices). Our empirical analysis uses a comprehensive dataset spanning January 2005 to March 2024, covering 11 daily crude oil price indicators and 60 monthly Chinese macroeconomic indicators. We validate results using the adjusted coefficient of determination ( R 2 ) and Bayesian Information Criterion (BIC) for model selection, and further test robustness across three samples: a full sample (2005.01–2024.03) and two crisis sub-samples (2007.01–2009.12 Financial Crisis, 2020.01–2023.12 COVID-19). Three core findings emerge: First, the two markets exhibit strong asymmetric influence; Second, the correlation is time-varying and crisis-sensitive; Third, factors show long-term persistence. These results confirm that crude oil acts as a key external constraint on China’s macroeconomic stability, while China’s macroeconomic conditions have limited impact on global oil pricing—consistent with its status as a “price taker” in the global crude oil market. The study provides empirical support for policymakers to design targeted risk-mitigation strategies and for market participants to optimize oil-related investment and risk management.
The quality of transition current densities derived from Gaussian basis sets
Quantum-mechanical descriptions of luminescence, excitation energy transfer, and resonant dipole–dipole interactions are usually formulated in terms of transition dipoles from the 1-particle density matrix. However, transition dipoles cannot adequately capture the retardation and polariton effects for large entities. A previous study [M.-W. Lee and L.-Y. Hsu, Phys. Rev. A, 107, 053709 (2023)] showed that based on macroscopic quantum electrodynamics, the transition-current-density (TCD) approach not only enables the description of retardation effects but also accounts for the polariton effects arising from material structures and vacuum electromagnetic fields. Nevertheless, the quality of transition currents derived from ab initio calculations remains largely unexplored. In this study, we examine the numerical equivalence between transition dipoles derived from transition charge densities and those from TCDs for 1- and 2-electron systems, including H2+, HeH+, and H2. We further examine the continuity equation ∇ · Jnm = −iωnmρnm by comparing the transition charge density (ρnm) and the divergence of TCD (Jnm), for a transition between states n and m. Despite close agreement of transition dipole moments, we find substantial violations of the continuity equation. The deviations manifest as spurious oscillations in ∇ · Jnm due to the artifacts from the second-derivative features of the underlying Gaussian-type orbitals. To overcome this issue, we implement a reciprocal-space filtering technique that suppresses these non-physical oscillations, improving physical consistency for the TCD. Our study provides practical considerations for future calculations that require reliable transition currents.
Nitrile RC≡N Triple Bond Cleavage by a Dicopper Nitrite Complex with N <sub>2</sub> Elimination and Formation of RCO <sub>2</sub> <sup>–</sup> Carboxylate Ligands
A comparative study on the nutrient and organic acid profiles of selected pepper genotypes
Abstract To comprehend the nutritional components and organic acids content of peppers for the purpose of advancing pepper breeding, it was imperative to analyze and compare the levels of nutritional components (capsanthin, capsaicin, dihydrocapsaicin, reducing sugar, total amino acids, crude fat, crude fiber and protein) as well as organic acids content. In this study, 18 varieties of peppers ( Capsicum annuum L.) were assessed for their nutrient compositions and organic acids. variance analysis, principal component analysis (PCA), and hierarchical cluster analysis(HCA) were employed for data analysis. The levels of nutrient compositions, including capsanthin, capsaicin, dihydrocapsaicin, reducing sugar, total amino acid, crude fat, crude fiber and protein, varied within the ranges of 11.63–71.75 ug g − 1 DW, 0.15–6.86 mg g − 1 DW, 0.05–4.74 mg g − 1 DW, 0.73–2.54%, 9.53–34.48%, 7.23–19.50%, 19.77–32.64% and 9.96–5.30%, respectively. Concerning 64 organic acids, 11 organic acids(3-D-hydroxybutyric acid, 3-hydroxyphenyl-hydracrylic acid, maslinic acid, 2-indolecarboxylic acid, 3,4-dihydroxyphenylacetic acid, carnosic acid, ethylmalonic acid, maleic acid, methylmalonic acid, oleanic acid and sebacic acid) were not detected. L-malic acid, cis-lactic acid and succinic acid were the main organic acids in 18 pepper varieties, ranking first, second and third respectively. Their concentrations accounted for 13.7% − 66.8%, 37.3% − 54.1%, and 3.1% − 18.3% of the total organic acid content respectively. PCA and HCA revealed that all pepper varieties formed three category, respectively. These nutritional profiles and organic acid compositions could be instrumental in pepper breeding programs aimed at developing novel cultivars, thereby expanding options for both producers and consumers.
Computational repurposing of approved drugs targeting KRAS G12D and EGFR for colorectal cancer therapy
Background/objectives Colorectal cancer is characterized by various oncogenic mutations, with the KRAS G12D mutation being the most prevalent. The development of MRTX1133 has revitalized the KRAS direct targeting. However, colorectal cancer demonstrated intrinsic resistance to MRTX1133, primarily due to the feedback activation of the EGFR pathway. Combining KRAS G12D and EGFR inhibition has demonstrated improved treatment efficacy, highlighting the potential of dual-targeting approaches in colorectal cancer therapy. This study employs CADD tools to identify approved drugs capable of dual targeting KRAS G12D and EGFR. Methods A library of 3,591 approved drugs was screened against KRAS G12D using high-throughput virtual screening (HTVs), standard precision (SP) and extra precision (XP) Glide docking modules. The top-ranking compounds were then docked into the EGFR binding pocket using XP mode, and docking scores were calculated. Further refinement of binding affinities was performed using Molecular Mechanics with Generalized Born and Surface Area Solvation (MM-GBSA) and Molecular Dynamics (MD) simulations. Results A total of 25 drugs showed better affinity to KRAS G12D than MRTX1133 (−9.18 kcal/mol). Among them, six drugs: Reproterol, Macimorelin, Nebivolol, Nadolol, Antrafenine, and Carteolol, displayed docking scores between −7.186 and −8.864 kcal/mol against EGFR, compared to the reference ligand Erlotinib (−9.669 kcal/mol). Subsequent MM-GBSA and MD identified Carteolol, an FDA-approved beta blocker, as the most promising candidate, showing stable and reliable binding with KRAS G12D and relatively stable interactions with EGFR. Conclusions This in silico study predicts Carteolol as a potential dual-targeting therapeutic agent, requiring biochemical and cellular validation before clinical relevance can be established.
Supramolecular cooperativity through the lens of enhanced sampling molecular dynamics
Supramolecular polymers are dynamic aggregates whose properties arise from their constitutive bonds, based on reversible, non-covalent interactions. A central aspect in the design and function of these materials is the cooperativity of polymerization, by which the addition of monomers becomes increasingly favorable as the polymer grows. Cooperativity strongly influences both the structure and collective behavior of supramolecular materials, with significant implications for their properties. Understanding the origins and consequences of cooperativity is crucial for the rational design of new functional supramolecular polymer systems. Herein, we systematically explore the cooperativity of supramolecular polymer systems via Molecular Dynamics simulations, powered by On-the-fly Probability Enhanced Sampling, to accurately characterize the free energy landscape associated with polymerization. We validate our approach via ad hoc, minimalistic coarse-grained models of cooperative and non-cooperative self-assembling monomers. We then apply our analysis to ureidopyrimidinone (UPy) supramolecular polymers, widely used in biohydrogel design. Our work provides detailed insights into the UPy polymerization process and how cooperativity can emerge from the hierarchical character of its supramolecular structure. The results underscore the importance of an extensive molecular simulation approach to obtain a quantitative characterization of the self-assembly thermodynamics, which is crucial to guide the rational development of next-generation supramolecular materials.
Contact Electrification–Based Enantioselective Recognition of Chiral Amino Acids through Stereospecific Interfacial Electron Transfer
CLM-former for enhancing multi-horizon time series forecasting and load prediction in smart microgrids using a robust transformer-based model
Research on multi-channel access strategy based on congestion control with burst traffic in CRNs
This paper investigates a multi-channel access strategy for cognitive radio networks (CRNs) under bursty traffic conditions, with a focus on congestion control. The proposed approach integrates cross-layer factors including channel fading, user activity, and finite cache capacity and models heterogeneous burst service arrivals using a two-state Markov-modulated Bernoulli process (MMBP-2). A dual-threshold mechanism is implemented in the node buffer to effectively manage congestion. System states are mapped onto a two-dimensional discrete Markov chain, where state transitions are characterized by a high-dimensional transition matrix. Through steady-state analysis, key performance metrics such as average queue length, throughput, delay, and packet loss rate are derived. Simulation results confirm that the model achieves stable operational performance. Building upon this framework, this paper proposes a multi-channel access strategy that maximizes average throughput while minimizing packet loss rate by employing a genetic algorithm. The results show that, in comparison with traditional strategies, the burst flow control model developed in this study effectively meets data access requirements in highly bursty environments. Furthermore, simulation experiments explore how system performance varies with changes in the number of channels and cognitive users, and the key operational threshold is determined. These findings offer valuable guidance for channel access design and capacity planning in burst communication scenarios.
2D self-assembly of phosphorus allotropes on Bi(111)
Phosphorus allotropes have attracted extensive interest in both fundamental research and potential applications. As the basic units of white P, tetrahedral P4 molecules have been the subject of extensive investigation. Here, we report the formation of self-assembled monolayers and the second layers of P4 molecules on a Bi(111) surface. At the initial adsorption stage, P4 molecules form a self-assembled monolayer that is commensurate with the Bi(111) substrate, adopting a √13 × √13 superstructure. The building blocks of this monolayer are P4 nonamers arranged in a clover-like motif. Further deposition leads to the formation of the second layer of P4 with a 2√3 × 2√3 superstructure. Scanning tunneling spectroscopy measurements reveal that the P4 monolayer has a bandgap of 1.55 eV, smaller than the gap (1.69 eV) of the second layer.
Catellani-Inspired BN-Aromatic Expansion: A Versatile Tool toward π-Extended 1,2-Azaborines with Tunable Photosensitizing Properties
The association of Alzheimer’s disease-related SNPs with mild cognitive impairment susceptibility in the Chinese population
Effects of stimulus emotional content on gaze pattern: An eye-tracking study
The attentional system tends to prioritize negative stimuli in the early stages of processing, favoring threat detection. However, it is unclear whether this bias is maintained or reversed toward positive stimuli at later stages. In this study, we used a free-viewing paradigm with eye tracking to examine early and late attentional biases toward negative, positive, and neutral stimuli (humans in emotionally unloaded activities) versus control stimuli (inanimate objects) in 122 participants without affective disorders (64 men, 58 women). We fitted generalized linear mixed models with random intercepts for stimuli and random intercepts and slopes for participants, and used non-parametric bootstrap resampling to obtain robust estimates and confidence intervals. Additionally, the number of first fixations was analyzed with a COM-Poisson. Results showed that participants fixated faster ( χ 2 (3) = 97.55, p < .001) and for longer durations (χ 2 (3) = 337.45, p < .001) on negative stimuli compared to the other categories, confirming a negativity bias in early attention In late attention, we found longer total fixation durations ( χ 2 (3) = 200.24, p < .001) and a greater number of fixations ( χ 2 (3) = 207.02, p < .001) for negative stimuli, contradicting the hypothesis of a positivity shift during emotional regulation. This sustained negativity bias may reflect an adaptive regulatory process in which individuals allocate attentional resources to threat-related information to enhance learning and emotional preparedness. Future studies should examine these effects across diverse sociocultural settings and in clinical populations.
Enhancing NMR shielding predictions of atoms-in-molecules machine learning models with neighborhood-informed representations
Accurate prediction of nuclear magnetic resonance (NMR) shielding with machine learning (ML) models remains a central challenge for data-driven spectroscopy. We present atomic variants of the Coulomb matrix (aCM) and bag-of-bonds (aBoB) descriptors and extend them using radial basis functions (RBFs) to yield smooth, per-atom representations (aCM-RBF and aBoB-RBF). Local structural information is incorporated by augmenting each atomic descriptor with contributions from the n nearest neighbors, resulting in the family of descriptors, aCM-RBF(n) and aBoB-RBF(n). For 13C shielding prediction on the QM9NMR dataset (831 925 shielding values across 130 831 molecules), aBoB-RBF(4) achieves an out-of-sample mean error of 1.69 ppm, outperforming models reported in previous studies. While explicit three-body descriptors further reduce errors at a higher cost, aBoB-RBF(4) offers the best balance of accuracy and efficiency. Benchmarking on external datasets comprising larger molecules (GDBm, Drug12/Drug40, and pyrimidinone derivatives) confirms the robustness and transferability of aBoB-RBF(4), establishing it as a practical tool for ML-based NMR shielding prediction.
Direct Atomic Observation of Discrete Bond Lengths and Fractional Quantized Conductance in Gold Atomic Chains
Modeling and experimental study of cutting forces of a variable pitch ball-end cutter in five-axis milling
Engagement in research capability-building: Impact on healthcare workforce attraction and retention in rural and remote Australia – A scoping review protocol
Background Geographic maldistribution of the health workforce remains a major challenge in Australia, with rural and remote communities experiencing persistent shortages that undermine access to and quality of care. Engagement in research is recognised as a potential mechanism for professional growth, continued learning, and improved workplace environments. Providing opportunities for health workers to participate in research or research capability-building (RCB) may therefore support workforce outcomes such as attraction and retention. This scoping review will identify and map existing evidence on the relationship between research engagement and health workforce outcomes in rural and remote Australia, and to summarise the factors that contribute to successful implementation of such initiatives. Methods and analysis We will conduct a scoping review of published and grey literature from Australia (2000 to present). Searches will be undertaken in CINAHL (EBSCO), Embase (Ovid), Global Health (Ovid), MEDLINE (Ovid), and PubMed, following the PRISMA-ScR 2020 guidelines and the Joanna Briggs Institute methodology for scoping reviews. Additional searches will be conducted through Google, Google Scholar, organisational websites, and snowballing of reference lists from included studies. Search terms will address four core concepts: (i) health professionals; (ii) research and RCB; (iii) workforce outcomes; and (iv) rural and remote Australian settings. Both qualitative and quantitative evidence will be included. Data will be synthesised using descriptive and thematic analysis, combining deductive approaches informed by the socioecological model and inductive approaches. Subgroup analyses will be undertaken where appropriate to provide deeper insights into the findings. Ethics and dissemination This scoping review will not involve human participants or primary data collection and does not require ethical approval. The results of this review will be published in a peer-reviewed journal.
Ultrafast dual-bond photodissociation of trifluorothioanisole: Mechanistic insights into the competing deactivation channels and environmental implications
Fluorinated compounds play indispensable roles across pharmaceutical, agrochemical, and materials science due to fluorine’s unique electronegativity, small atomic radius, and high metabolic stability. Among them, trifluorothioanisole (Ph–S–CF3) serves as a representative functionally model system for environmental photochemistry, given the widespread use of the –SCF3 group in agrochemicals and its concerning persistence. However, the ultrafast photodynamics and non-radiative decay mechanisms of Ph–S–CF3 remain poorly understood, limiting predictive insight into its environmental fate. Here, we combine high-level static electronic structure calculations with excited-state non-adiabatic dynamics simulations to unravel the mechanism of Ph–S–CF3. Our results reveal that excitation to the S1 state initiates ultrafast internal conversion via two competitive bond-cleavage pathways mediated by distinct conical intersections: dissociation at either the S1–C3 or S1–CF3 bond, yielding ·CF3 (46%) and ·SCF3 (54%) radicals, with an overall S1 lifetime of ∼612 fs. These findings not only elucidate the photodegradation mechanism of a prominent fluorinated environmental contaminant but also provide a general theoretical framework for predicting the photostability and formation dynamics of persistent radical species (·CF3/·SCF3) derived from –SCF3-functionalized compounds, underscoring the regulatory role of fluorine substitution in excited-state dynamics. Thereby, this study provides a crucial basis for assessing the environmental persistence and ecotoxicity of fluorinated organics and offers strategic guidance for the rational design of low-persistence fluorinated functional molecules.