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Abstract Topological entanglement entropy (TEE), the sub-leading term in the entanglement entropy of topological order, is the direct evidence of the long-range entanglement. While effective in characterizing topological orders on closed manifolds, TEE is model-dependent when entanglement cuts intersect with physical gapped boundaries. In this paper, we study the origin of this model-dependence by introducing a model-independent picture of partitioning the topological orders with gapped boundaries. In our picture, the entanglement boundaries (EBs), i.e. the virtual boundaries of each subsystem induced by the entanglement cuts, are assumed to be gapped boundaries with boundary defects. At this model-independent stage, there are two choices one has to make manually in defining the bi-partition: the boundary condition on the EBs, and the coherence between certain boundary states. We show that TEE appears because of a constraint on the defect configurations on the EBs, which is choice-dependent in the cases where the EBs touch gapped boundaries. This choice-dependence is known as the ambiguity in entanglement entropy. Different models intrinsically employ different choices, rendering TEE model-dependent. For D(ℤ2) topological order, the ambiguity can be fully characterized by two parameters that respectively quantifies the EB condition and the coherence. In particular, calculations compatible with the folding trick naturally choose EB conditions that respect electric-magnetic duality and set specific parameter values.

The first pilot test of polyacrylamide microsphere alternate surfactant flood (PMAS) with mixtures of anionic-cationic surfactants (Sa/c) was carried out for a high-temperature, high-salinity, and high-hardness sandstone reservoir to demonstrate the potential of this novel technique to improve oil recovery. A critical micelle concentration (CMC) of 4.82 mg/L, an ultralow interfacial tension (IFT) of 8 × 10−4 mN/m, and a high oil solubilization of 22 were obtained. Static and dynamic adsorptions of Sa/c on natural core containing 15 wt% clay were reduced to about 2.20 and 0.30 mg/g-core, respectively, with the addition of adsorption inhibitor (AI). Since June 2014, the pilot test of PMAS was carried out in a Sinopec reservoir with a temperature of 87 °C, a salinity of 260,393 mg/L, and a hardness of 6,401 mg/L. Twelve cycles of alternative injection of 0.0125 PV Sa/c with a concentration of 0.1% and 0.0125 PV polyacrylamide microsphere with a concentration of 0.2% were conducted at an injection rate of 0.1 PV/yr, for a total of 0.3 PV chemical injection. As a result, the net daily oil production increased from 0 t to 6.5 t, and the water cut decreased from 96.3% to 93.8%, leading to an ultimate improved oil recovery of 6.3% original oil-in-place.

Object detection via remote sensing encounters significant challenges due to factors such as small target sizes, uneven target distribution, and complex backgrounds. This paper introduces the K-CBST YOLO algorithm, which is designed to address these challenges. It features a novel architecture that integrates the Convolutional Block Attention Module (CBAM) and Swin-Transformer to enhance global semantic understanding of feature maps and maximize the utilization of contextual information. Such integration significantly improves the accuracy with which small targets are detected against complex backgrounds. Additionally, we propose an improved detection network that combines the improved K-Means algorithm with a smooth Non-Maximum Suppression (NMS) algorithm. This network employs an adaptive dynamic K-Means clustering algorithm to pinpoint target areas of concentration in remote sensing images that feature varied distributions and uses a smooth NMS algorithm to suppress the confidence of overlapping candidate boxes, thereby minimizing their interference with subsequent detection results. The enhanced algorithm substantially bolsters the model’s robustness in handling multi-scale target distributions, preserves more potentially valid information, and diminishes the likelihood of missed detections. This study involved experiments performed on the publicly available DIOR remote sensing image dataset and the DOTA aerial image dataset. Our experimental results demonstrate that, compared with other advanced detection algorithms, K-CBST YOLO outperforms all its counterparts in handling both datasets. It achieved a 68.3% mean Average Precision (mAP) on the DIOR dataset and a 78.4% mAP on the DOTA dataset.

The accuracy and reliability of unmanned aerial vehicle (UAV) visual positioning systems are dependent on the performance of multi-source image matching algorithms. Despite many advancements, targeted performance evaluation frameworks and datasets for UAV positioning are still lacking. Moreover, existing consistency verification methods such as Random Sample Consensus (RANSAC) often fail to entirely eliminate mismatches, affecting the precision and stability of the matching process. The contributions of this research include the following: (1) the development of a benchmarking framework accompanied by a large evaluation dataset for assessing the efficacy of multi-source image matching algorithms; (2) the results of this benchmarking framework indicate that combinations of multiple algorithms significantly enhance the Match Success Rate (MSR); (3) the introduction of a novel Geographic Geometric Consistency (GGC) method that effectively identifies mismatches within RANSAC results and accommodates rotational and scale variations; and (4) the implementation of a distance threshold iteration (DTI) method that, according to experimental results, achieves an 87.29% MSR with a Root Mean Square Error (RMSE) of 1.11 m (2.22 pixels) while maintaining runtime at only 1.52 times that of a single execution, thus optimizing the trade-off between MSR, accuracy, and efficiency. Furthermore, when compared with existing studies on UAV positioning, the multi-source image matching algorithms demonstrated a sub-meter positioning error, significantly outperforming the comparative method. These advancements are poised to enhance the application of advanced multi-source image matching technologies in UAV visual positioning.

This article analyzes the impact of the level of urban polycentricity (UP) on particulate matter emissions from vehicles (PMV) across 102 prefecture-level cities in China between 2011 and 2015. We adopt a spatial panel modeling approach to our measures of UP and PMV, controlling for (possible) intervening effects such as population density and economic output. We observe an inverted U-shaped relationship between both measures: When UP is low, an increase in polycentricity is associated with higher levels of PMV; however, when UP reaches a certain threshold, the increase in polycentricity is associated with a reduction in PMV. We find a similar relationship between economic output and PMV and demonstrate how the effects of population density on PMV consist of two opposite processes that likely offset each other. Nonetheless, jointly, population density and UP have a significant effect on PMV. We use our results to discuss policy implications and identify avenues for further research.

Among existing approaches to holonomic quantum computing, the adiabatic holonomic quantum gates (HQGs) suffer errors due to decoherence, while the non-adiabatic HQGs either require additional Hilbert spaces or are difficult to scale. Here, we report a systematic, scalable approach based on dynamical invariants to realize HQGs without using additional Hilbert spaces. While presenting the theoretical framework of our approach, we design and experimentally evaluate single-qubit and two-qubits HQGs for the nuclear magnetic resonance system. The single-qubit gates acquire average fidelity 0.9972 by randomized benchmarking, and the controlled-NOT gate acquires fidelity 0.9782 by quantum process tomography. Our approach is also platform-independent, and thus may open a way to large-scale holonomic quantum computation.

Topological quantum computation (TQC) is one of the most striking architectures that can realize fault-tolerant quantum computers. In TQC, the logical space and the quantum gates are topologically protected, i.e., robust against local disturbances. The topological protection, however, requires complicated lattice models and hard-to-manipulate dynamics; even the simplest system that can realize universal TQC—the Fibonacci anyon system—lacks a physical realization, let alone braiding the non-Abelian anyons. Here, we propose a disk model that can simulate the Fibonacci anyon system and construct the topologically protected logical spaces with the Fibonacci anyons. Via braiding the Fibonacci anyons, we can implement universal quantum gates on the logical space. Our disk model merely requires two physical qubits to realize three Fibonacci anyons at the boundary. By 15 sequential braiding operations, we construct a topologically protected Hadamard gate, which is to date the least-resource requirement for TQC. To showcase, we implement a topological Hadamard gate with two nuclear spin qubits, which reaches 97.18% fidelity by randomized benchmarking. We further prove by experiment that the logical space and Hadamard gate are topologically protected: local disturbances due to thermal fluctuations result in a global phase only. As a platform-independent proposal, our work is a proof of principle of TQC and paves the way toward fault-tolerant quantum computation.

Abstract We generalize the electric-magnetic (EM) duality in the quantum double (QD) models to the extended QD models of topological orders with gapped boundaries. We also map the extended QD models to the extended Levin-Wen (LW) models with gapped boundaries. To this end, we Fourier-transform and rewrite the extended QD model on a trivalent lattice with a boundary, where the bulk gauge group is a finite group G. Gapped boundary conditions of the model before the transformation are known to be characterized by the subgroups K ⊆ G supplying the boundary degrees of freedom. We find that after the transformation, the boundary conditions are then characterized by the Frobenius algebras A G,K in Rep G . An A G,K is the dual space of the quotient of the group algebra of G over that of K , and Rep G is the category of the representations of G. The EM duality on the boundary is revealed by mapping the K ’s to A G,K ’s. We also show that our transformed extended QD model can be mapped to an extended LW model on the same lattice via enlarging the Hilbert space of the latter. Moreover, our transformed extended QD model elucidates the phenomenon of anyon splitting in anyon condensation.

Abstract An anyonic exclusion statistics, which generalizes the Bose-Einstein and Fermi-Dirac statistics of bosons and fermions, was proposed by Haldane [1]. When fusion of anyons is involved, certain ‘pseudo-species’ anyons appear in the exotic statistical weights of non-Abelian anyon systems; however, the meaning and significance of pseudo-species remains an open problem. The relevant past studies had considered only anyon systems without any physical boundary but boundaries often appear in real-life materials. In this paper, we propose an extended anyonic exclusion statistics on surfaces with gapped boundaries, introducing mutual exclusion statistics between anyons as well as the boundary components. Motivated by refs. [2, 3], we present a formula for the statistical weight of many-anyon states obeying the proposed statistics. Taking the (doubled) Fibonacci topological order as an example, we develop a systematic basis construction for non-Abelian anyons on any Riemann surfaces with gapped boundaries. The basis construction offers a standard way to read off a canonical set of statistics parameters and hence write down the extended statistical weight of the anyon system being studied. The basis construction reveals the meaning of pseudo-species. A pseudo-species has different ‘excitation’ modes, each corresponding to an anyon species. The ‘excitation’ modes of pseudo-species corresponds to good quantum numbers of subsystems of a non-Abelian anyon system. This is important because often (e.g., in topological quantum computing) we may be concerned about only the entanglement between such subsystems.

Curbing carbon emissions by restricting economic growth could decrease human well-being across the world and especially in developing countries, suggesting that we need to find alternative approaches to reducing carbon emissions. Against this background, this paper investigates the relationship between urban spatial structure and carbon emissions in the Chinese context from 2002 to 2019. Specifically, urban spatial structure of 286 Chinese cities, represented by the two dimensions of polycentricity and compactness, are calculated based on the gridded (1 km × 1 km) LandScan dataset on population, while carbon emissions of these cities are aggregated from the gridded (1 km × 1 km) Open-source Data Inventory for Anthropogenic CO2 (ODIAC) dataset on carbon emissions. The empirical results based on different regression models find that overall (1) more dispersed and less monocentric (i.e., less compact and more polycentric) cities are often associated with lower levels of carbon emissions, ceteris paribus; (2) the impact of polycentricity on carbon emissions could be moderated by the economic development levels of Chinese cities. For cities with gross domestic product of more than 173 billion yuan, a more polycentric spatial structure is usually associated with a higher level of carbon emissions; (3) a city’s urban spatial structure could have positive spatial spillovers on carbon emissions of its neighboring cities.

Abstract We extend the twisted gauge theory model of topological orders in three spatial dimensions to the case where the three spaces have two dimensional boundaries. We achieve this by systematically constructing the boundary Hamiltonians that are compatible with the bulk Hamiltonian. Given the bulk Hamiltonian defined by a gauge group G and a four-cocycle ω in the fourth cohomology group of G over U(1), we construct a gapped boundary Hamiltonian using {K, α}, with a subgroup K ⊆ G and a 3-cochain α of K over U(1), which satisfies the generalized Frobenius condition. The Hamiltonian is invariant under the topological renormalization group flow (via Pachner moves). Each solution {K, α} to the generalized Frobenius condition specifies a gapped boundary condition. We derive a closed-form formula of the ground state degeneracy of the model on a three-cylinder, which can be naturally generalized to three-spaces with more boundaries. We also derive the explicit ground-state wavefunction of the model on a three-ball. The ground state degeneracy and ground-state wavefunction are both presented solely in terms of the input data of the model, namely, {G, ω, K, α}.

Abstract In this paper we explore how non trivial boundary conditions could influence the entanglement entropy in a topological order in 2+1 dimensions. Specifically we consider the special class of topological orders describable by the quantum double. We will find very interesting dependence of the entanglement entropy on the boundary conditions particularly when the system is non-Abelian. Along the way, we demonstrate a streamlined procedure to compute the entanglement entropy, which is particularly efficient when dealing with systems with boundaries. We also show how this method efficiently reproduces all the known results in the presence of anyonic excitations.

Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR) was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC) were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh.

BackgroundEGFR kinase domain duplication (EGFR-KDD) is an infrequent oncogenic driver mutation in lung adenocarcinoma. It may be a potential target benefit from EGFR-tyrosine kinase inhibitors (TKIs) treatment.Case presentationA 66-year-old Chinese male was diagnosed with lung adenocarcinoma in stage IVb with brain metastases. Next-generation sequencing revealed EGFR-KDD mutation. The patient received furmonertinib 160mg daily for anti-cancer treatment and obtained therapeutic efficacy with partial response (PR). Progression-free survival (PFS) duration from monotherapy was 16 months. With slow progressions, combined radiotherapy and anti-vascular targeted therapy also brought a continuous decrease in the tumors. The patient has an overall survival (OS) duration of more than 22 months and still benefits from double-dose furmonertinib.ConclusionsThis report provided direct evidence for the treatment of EGFR-KDD to use furmonertinib. A Large-scale study is needed to confirm this preliminary finding.