Layered Structure Research Articles

AIRHF-Net: an adaptive interaction representation hierarchical fusion network for occluded person re-identification

To tackle the high resource consumption in occluded person re-identification, sparse attention mechanisms based on Vision Transformers (ViTs) have become popular. However, they often suffer from performance degradation with long sequences, omission of crucial information, and token representation convergence. To address these issues, we introduce AIRHF-Net: an Adaptive Interaction Representation Hierarchical Fusion Network, named AIRHF-Net, designed to enhance pedestrian identity recognition in occluded scenarios. Our approach begins with the development of an Adaptive Local-Window Interaction Encoder (AL-WIE), which aims to overcome the inherent subjective limitations of traditional sparse attention mechanisms. This innovative encoder merges window attention, adaptive local attention, and interaction attention, facilitating automatic localization and focusing on visible pedestrian regions within images. It effectively extracts contextual information from window-level features while minimizing the impact of occlusion noise. Additionally, recognizing that ViTs may lose spatial information in deeper structural layers, we implement a Local Hierarchical Encoder (LHE). This component segments the input sequence in the spatial dimension, integrating features from various spatial positions to construct hierarchical local representations that substantially enhance feature discriminability. To further augment the quality and breadth of datasets, we adopt an Occlusion Data Augmentation Strategy (ODAS), which bolsters the model’s capacity to extract critical information under occluded conditions. Extensive experiments demonstrate that our method achieves improved performance on the Occluded-DukeMTMC dataset, with a rank-1 accuracy of 69.6% and an mAP of 61.6%.

A Mixed-Pure Planar Heterojunction Structure of Active Layers for Efficient Sequential Blade-Coating Organic Solar Cells.

Precise modulating the vertical structure of active layers to boost charge transfer is an effective way to achieve high power conversion efficiencies (PCEs) in organic solar cells (OSCs). Herein, efficient OSCs with a well-controlled vertical structure are realized by a rapid film-forming method combining low boiling point solvent and the sequential blade-coating (SBC) technology. The results of grazing incident wide-angle X-ray scattering measurement show that the vertical component distribution is varied by changing the processing solvent. Novel characterization technique such as tilt resonant soft X-ray scattering is used to test the vertical structure of the films, demonstrating the dichloromethane (DCM)-processed film is truly planar heterojunction. The devices with chloroform (CF) processed upper layer show an increased mixed phase region compared to these devices with toluene (TL) or -DCM-, which is beneficial for improving charge generation and achieving a superior PCE of 17.36%. Despite significant morphological varies, the DCM-processed devices perform slightly lower PCE of 16.66%, which is the highest value in truly planar heterojunction devices, demonstrating higher morphological tolerance. This work proposes a solvent-regulating method to optimize the vertical structure of active layers through SBC technology, and provides a practical guidance for the optimization of the active-layer microstructure.

H_2-H_2O demixing in Uranus and Neptune: Adiabatic structure models

Demixing properties of major planetary constituents influence the interior structure and evolution of planets. Comparing experimental and computational data on the miscibility of hydrogen and water to adiabatic profiles suggests that phase separation between these two components occurs in the ice giants Uranus and Neptune. We aim to predict the atmospheric water abundance and transition pressure between the water-poor outer envelope and the water-rich deep interior in Uranus and Neptune. We constructed seven H$_ $-H$_ $O phase diagrams from the available experimental and computational data. We computed interior adiabatic structure models and compared these to the phase diagrams to infer whether demixing occurred. We obtain a strong water depletion in the top layer due to the rain-out of water and find upper limits on the atmospheric water-mass fraction Zatm of 0.21 for Uranus and 0.16 for Neptune. The transition from the water-poor to the water-rich layer is sharp and occurs at pressures $P_Z$ between 4 and 11 GPa. Using these constraints on Zatm and $P_Z$, we find that the observed gravitational harmonics $J_2$ and $J_4$ can be reproduced if $P_Z 10$ GPa in Uranus and $ 5$ GPa in Neptune, and if the deep interior has a high primordial water-mass fraction of 0.8, unless rocks are also present. The agreement with $J_4$ is improved if rocks are confined deeper than $P_Z$, for instance, below a rock cloud level at 2000 K (20--30 GPa). These findings confirm classical few-layer models and suggest that a layered structure may result from a combination of primordial mass accretion and subsequent phase separation. Reduced observational uncertainty in $J_4$ and its dynamic contribution, atmospheric water abundance measurements from the Uranus Orbiter and Probe (UOP) or a Neptune mission, and better understanding of the mixing behaviour of constituents are needed to constrain the interiors of ice giants.

Depth-wise multiparametric assessment of articular cartilage layers with single-sided NMR.

Articular cartilage (AC) is a specialized connective tissue that covers the ends of long bones and facilitates the load-bearing of joints. It consists of chondrocytes distributed throughout an extracellular matrix and organized into three zones: superficial, middle, and deep. Nuclear magnetic resonance (NMR) techniques can be used to characterize this layered structure. In this study, devoted to a better understanding of the NMR response of this complex tissue, 20 specimens excised from femoral and tibial cartilage of bovine samples were analyzed by the low-field single-sided NMR-MOUSE-PM10. A multiparametric depth-wise analysis was performed to characterize the laminar structure of AC and investigate the origin of the NMR dependence on depth. The depth dependence of the single parameters T1, T2, and D has been described in literature, but their simultaneous measurement has not been fully exploited yet, as well as the extent of their variability. A novel parameter, α, evaluated by applying a double-quantum-like sequence, has been measured. The significant decrease in T1, T2, and D from the middle to the deep zone is consistent with depth-dependent composition and structure changes of the complex matrix of fibers confining and interacting with water. The α parameter appears to be a robust marker of the layered structure with a well-reproducible monotonic trend across the zones. The discrimination of cartilage zones was reinforced by a multivariate principal component analysis statistical analysis. The large number of samples allowed for the identification of the smallest number of parameters or their combination able to classify samples. The first two components clustered the data according to the different zones, highlighting the sensitivity of the NMR parameters to the structural and compositional variations of AC. Using two parameters, the best result was obtained by considering T1 and α. Single-sided NMR devices, portable and low-cost, provide information on NMR parameters related to tissue composition and structure.

Structure and properties of the boride layer of steel using the plasma alloying method

Boration is one of the promising methods for increasing surface hardness and wear resistance, resistance to oxidation and corrosion of mechanical engineering parts. The diffusion saturation process is characterized by a long duration, as well as a shallow depth of the hardened layer. The use of concentrated heating sources can solve these problems. Among the methods of high-energy exposure, the technology of plasma surface alloying should be highlighted. In this paper, the study of the microstructure and properties of the boride layer obtained on steel by plasma alloying is carried out. It is noted that the boron-doped layer has a heterogeneous structure and high hardness. It is noted that the layer obtained after alloying with a current of 120 A has the highest microhardness value and amounts to 859–1265 HV. With an increase in current to 140 A, the microhardness of the alloyed layer decreases and amounts to 761–1048 HV. Increasing the current to 160 A leads to a significant decrease in the microhardness of the surface layer and it is 452–747 HV. It is known that the volume of iron boride fractions determines the degree of hardening of the steel surface. An increase in the plasma arc current leads to a decrease in the proportion of primary borides in the surface layer after alloying, and therefore leads to a decrease in microhardness. The alloyed layer has characteristic zones: hypereutectic, eutectic and hypoeutectic. An increase in current leads to a significant change in the microstructure of the surface layer and a decrease in the microhardness of the alloyed layer. The surface layer after plasma alloying with a current of 120 A has the highest microhardness (1265 HV). It has been established that it is possible to obtain a boride layer using the technology of plasma surface doping with boron. After processing, the alloyed layer is characterized by a heterogeneous structure and has high hardness.

Progress and Perspective of High‐Entropy Strategy Applied in Layered Transition Metal Oxide Cathode Materials for High‐Energy and Long Cycle Life Sodium‐Ion Batteries

AbstractLayered transition metal oxide (LTMO) cathode materials of sodium‐ion batteries (SIBs) have shown great potential in large‐scale energy storage applications owing to their distinctive periodic layered structure and 2D ion diffusion channels. However, several challenges have hindered their widespread application, including phase transition complexities, interface instability, and susceptibility to air exposure. Fortunately, an impactful solution has emerged in the form of a high‐entropy doping strategy employed in energy storage research. Through the implementation of high‐entropy doping, LTMOs can overcome the aforementioned limitations, thereby elevating LTMO materials to a highly competitive and attractive option for next‐generation cathodes of SIBs. Thus, a comprehensive overview of the origins, definition, and characteristics of high‐entropy doping is provided. Additionally, the challenges associated with LTMOs in SIBs are explored, and discussed various modification methods to address these challenges. This review places significant emphasis on conducting a thorough analysis of the research advancements about high‐entropy LTMOs utilized in SIBs. Furthermore, a meticulous assessment of the future development trajectory is undertaken, heralding valuable research insights for the design and synthesis of advanced energy storage materials.

Layered high-entropy sulfides: boosting electrocatalytic performance for hydrogen evolution reaction by cocktail effects

Abstract This study explores high-entropy sulfides (HESs) as potential electrocatalysts for the hydrogen evolution reaction (HER). Novel Pa-3 and Pnma structured HESs containing Fe, Mn, Ni, Co and Mo, were synthesized via a facile mechanochemical method. Structural and chemical properties were extensively characterized using x-ray diffraction, transmission electron microscopy, energy-dispersive x-ray spectroscopy, and x-ray photoelectron spectroscopy. The electrocatalytic performance of four as-prepared HESs in alkaline electrolyte for HER reveals the remarkable outperformance compared to medium-entropy and conventional sulfides. Particularly, (Fe0.2Mn0.2Ni0.2Co0.2Mo0.2)S2 demonstrated outstanding activities, with minimal overpotentials (187 mV at 10 mA cm–2) and outstanding durability under harsh alkaline conditions (a mere polarization increase ΔE = 17 mV after 14 h via chronopotentiometry). The remarkable catalytic activities can be attributed to synergistic effects resulting from the cocktail effects within the high-entropy disulfide. The introduction of Mo contributes to the formation of a layered structure, which leads to an increased surface area and thus to a superior HER performance compared to other HES and conventional sulfides. This work demonstrates the promising potential of HES and underscores that further development for catalytic applications paves the way for innovative routes to new and more efficient active materials for HER catalysis.

Experimental and Theoretical Investigation of Anisotropic Proton Conduction in Two-Dimensional Metal-Organic Frameworks.

Two-dimensional (2D) materials are known for their potential to exhibit anisotropic transport properties due to their layered structures. However, the anisotropic ion conduction of 2D metal-organic frameworks (MOFs) has been rarely explored. In this study, we investigated the anisotropic proton conduction along the in-plane and stacking directions of two analogs of undulating 2D MOFs: [Mn(salen)]2[Pt(CN)4]·H2O (MnPt) and [Mn(salen)]2[PtI2(CN)4]·H2O (MnPtI). This investigation was conducted using both experimental methods, involving single crystals, and theoretical calculations. Compared to the relatively isotropic proton conduction of MnPt at 85 °C and 95% relative humidity (RH), with a stacking direction conductivity (σstacking) of 1.8 × 10-5 S/cm, which is approximately 2.9 times the in-plane conductivity (σin-plane), MnPtI exhibited highly anisotropic proton conduction. The σstacking of MnPtI under the same conditions (85 °C, 95% RH) was 1.5 × 10-4 S/cm, which is 83 times higher than its σin-plane. Additionally, the activation energy for proton conduction in MnPtI ranged from 0.65 to 0.73 eV, which is higher than the 0.48 eV observed for MnPt. Theoretical calculations confirmed that slight differences in local structures, including node distortions between MnPt and MnPtI, significantly influenced the activation energies for water migration. This was attributed to the formation of hydrogen bonds between layers and water molecules.

Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing.

Calculation of the Number of Roller Passes When Strengthening Roads Using Recycled Asphalt Granulate

Statement of the problem. Work during the repair, overhaul and reconstruction of highways involves the destruction of old asphalt concrete pavement using the cold milling method. The resulting discrete material is used to strengthen roadsides, and if it is regenerated, it is used to construct base layers and lower layers of covering. The compaction of layers of road pavement is carried out with rollers, the efficiency of which depends on the properties of the resulting material, the power parameters of the rollers, and the technology of work, determined by the number of passes of the roller along one track. Analysis of the work showed that there are no substantiated recommendations for determining the number of roller passes taking into account the properties of the material being compacted. Establishing such dependencies makes it possible to clarify the composition of the compaction machine link when installing structural layers of road pavements while ensuring the required quality of work. Results. Analytical dependencies are presented for calculating the deformation modulus and rigidity of a layer of processed asphalt granulate under load, taking into account the thickness of the layer and the size of the fractions. The stresses under the rollers were determined for different types of rollers and their operating modes, taking into account the technical characteristics. The results of a study to determine the number of roller passes when installing a layer of recycled asphalt granulate are presented, taking into account the size of the fractions and the characteristics of the rollers used. Conclusions. It has been established that both the deformation modulus and the rigidity of the compacted layer can be taken as a criterion when choosing the parameters of the rollers and determining the number of passes of the roller along one track. Based on the results obtained, we can conclude that for a given value of the deformation modulus and the stiffness coefficient of the compacted layer of asphalt granulate, it is possible to determine the number of passes, the type of roller and its operating mode to ensure the required characteristics of the layer.

Machine learning-enhanced band gaps prediction for low-symmetry double and layered perovskites.

Density functional theory (DFT) calculations are widely used for material property prediction, but their computational cost can hinder the discovery of novel perovskites. This work explores machine learning (ML) as a faster alternative for predicting band gaps in complex perovskites, focusing on low-symmetry double and layered structures. We employ Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost) to predict both direct and indirect band gaps. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) metrics. Our results reveal SVR as the most effective general model for predicting band gaps in both double and layered perovskites. Interestingly, for double perovskites specifically, XGBoost achieves even higher accuracy when incorporating derivative discontinuity as a feature. Feature importance analysis identifies the standard deviation of valence charges ("Valence (std)") as the most critical factor for band gap prediction across all studied perovskites. This research demonstrates the potential of ML for efficient and accurate band gap prediction in complex perovskites, accelerating material discovery efforts.

Optimized Kretschmann–Raether configuration for maximized electromagnetic near-field enhancement

This work focuses on maximizing the near-field enhancement effect of a Kretschmann–Raether configuration. Through theoretical calculation and numerical simulation, we demonstrate that a global optimization of excitation parameters provides 36 times more near-field intensity enhancement. This maximum near-field enhancement at the transmitted side of a metal surface occurs at the coalescence of the transmission coefficient’s pole and the reflection coefficient’s zero. The additional enhancement is possible by tuning the excitation wavelength, incident angle, and metal layer thickness synergistically. This work paves the way for further enhancing light-matter interaction in a broad application using layered structures.

A Mathematically Optimized Design Solution for Structure of PEMFC Catalyst Layer Based on a Two-Phase Flow Model

Abstract Proton exchange membrane fuel cells (PEMFCs) have emerged as a promising solution as the world is moving toward sustainable energy resources. However, in order to compete economically with existing technologies, further improvements in performance are necessary. Mathematical modeling and optimization are viable tools for designing better PEMFCs. This study aims to provide a framework for topological optimization of the electrode structure, with the ultimate goal of enhancing cell performance. To achieve this, a two-phase flow model of PEMFC is developed to characterize the cell performance. The model is then coupled with a topology optimization technique, which is the main focus of the present work, to seek an optimized constituent distribution in the catalyst layer. Results indicate that an electrode with a heterogeneous structure can enhance the overall cell performance by balancing various transport and rate processes. The optimized designs are investigated for various key factors, including effective diffusivity, effective conductivity, and liquid water management, to demonstrate how an optimized design can be advantageous.

Enhancing the potassium-ion storage performance of potassium manganese oxide cathode by single-element doping strategies

Layered manganese-based oxides are considered as ideal cathode materials for potassium-ion batteries (PIBs) due to their ease of synthesis and relatively large interlayer spacing. Nevertheless, the Jahn-Teller effect of Mn3+ and the irreversible phase transitions in high-voltage regions of layered manganese-based oxide leads to slow diffusion kinetics of potassium ions and severe capacity fading. Herein, a series of single-element doped manganese-based oxide cathodes K0.5Mn0.98X0.02O2 (X = Fe, Mg, Cu, Ti and Zn) are designed and investigated. The results demonstrate that trace doping with these elements stabilizes the layered structure of KMnO2 cathode materials, improves the diffusion coefficient of K+, and effectively suppresses the P3-O3-P3 phase transition in high-voltage regions. Among the doping-samples, the K0.5Mn0.98Fe0.02O2 exhibits outstanding reversible specific capacity of 127 mAh g−1 at a current density of 20 mA g−1. Meanwhile, K0.5Mn0.98Ti0.02O2 demonstrates exceptional cycling stability and rate capability, retaining 83.8 % of its initial capacity after 300 cycles at 200 mA g−1, and maintaining a high specific capacity close to 40 mAh g−1 even at 1000 mA g−1. This study validates the feasibility of trace element doping in enhancing the electrochemical performance of layered Mn-based cathode materials for PIBs.

Self-Assembly Synthesis of Oxygen and Sulfur Co-Doped Porous Graphitic Carbon Nitride Nanosheets for Boosting CO2 Photoreduction.

Graphitic carbon nitride (CN) has garnered considerable attention in the field of visible-light CO2 photoreduction, but its efficiency remains limited by the intrinsic π-conjugated skeleton. Here, O and S were co-doped CN (O, S/CN) by a facile "hydrolysis + calcination" approach to modulate the physicochemical and electronic structure. Distinctive from S doped CN (SCN), O, S/CN owned porous layer structure with several nanosheets and less SO4 2- groups on the surface. The amount of heteroatom-doping was achieved by changing the hydrothermal temperature. The optimum O, S/CN-80 achieved moderate CO production rate of 1.29 μmol g-1 h-1, which was 3.79 times as much as SCN (0.34 μmol g-1 h-1). The O and most S atoms were substitutionally doped and the effect of S doped state on the improved efficiency of CO generation in O, S/CN was also explored based on the theoretical calculations. This work provides an inspiration to develop efficient dual-doped CN photocatalysts for photocatalytic CO2 reduction.

Misfit Layered Compounds: Insights into Chemical, Kinetic, and Thermodynamic Stability of Nanophases.

ConspectusCompounds with layered structures (2D-materials), like transition metal-dichalcogenides (e.g., MoS2), attracted a great deal of interest in the scientific community in recent years. This interest can be attributed to their unique lamellar structure, which induces large anisotropy in their physicochemical properties. Furthermore, owing to the weak van der Waals interaction between the layers, they can be cleaved along the a-b plane, which allows fabricating single layers with physical properties entirely different from the bulk material. Moreover, stacking layers of different 2D-materials on top of each other has led to a wealth of new observations, for instance, by twisting two layers with respect to each other and producing Moiré lattice. Another outstanding property of inorganic layer compounds is their tendency to form nanotubes, reported first (for WS2) many years ago and subsequently from many other layered compounds.Among the 2D-materials, misfit layer compounds make a special class with an incommensurate and nonstoichiometric lattice made of an alternating layer with rocksalt structure, like LaS (O) and a layer with hexagonal structure, like TaS2 (T). The lack of lattice commensuration between the two slabs leads to a built-in strain, which can be relaxed via bending. Consequently, nanotubes have been produced from numerous MLC compounds over the past decade and their structure was elucidated.Owing to their large surface area, nanostructures are generally metastable and tend to recrystallize into microscopic crystallites via different mechanisms, like Ostwald ripening, or chemically decompose and then recrystallize. The stability of nanostructures at elevated temperatures has been investigated quite scarcely so far. In this perspective, electron microscopy as well as synchrotron-based X-ray absorption and reflection techniques were used to elucidate the chemical selectivity and decomposition routes of rare-earth based MLC nanotubes prepared at elevated temperatures (800-1200 °C).As for the chemical selectivity, entropic effects are expected to dictate the random distribution of the chalcogen atoms on the anion sites of the MLC nanotubes at elevated temperatures. Nonetheless, the sulfur atoms were found to bind exclusively to the rare-earth atom (Ln = La, Sm) of the rocksalt slab and the selenium to the tantalum of the hexagonal TX2 slab. This uncommon selectivity was not found in other kinds of nanotubes like WSe2xS2(1-x). In other series of experiments, the lack of utter symmetry in the multiwall nanotubes leads to exclusions of certain X-ray (0kl) reflections, which was used to distinguish them from the bulk crystallites. The transformation of Ln-based MLC nanotubes into microscopic flakes was followed as a function of the synthesis temperature (800-1200 °C) and the synthesis time (1-96 h). Furthermore, sequential high-temperature transformations of the (O-T) lattice into (O-T-T) and finally (O-T-T-T) phases via deintercalation of the LnS slab was observed. This autocatalytic process is reminiscent of the deintercalation of alkali atoms from different layered structure materials. Annealing at higher temperatures and for longer periods of time eventually leads to the decomposition of the ternary MLC into binary metal-sulfide phases, as well as partial oxidation of the product. This study sheds light on the complex mechanism of high-temperature chemical stability of the nanostructures.

On the engineering of higher-order Van Hove singularities in two dimensions

The properties of correlated electron materials are often intricately linked to Van Hove singularities (VHS) in the vicinity of the Fermi energy. The class of these VHS is of great importance, with higher-order ones—with power-law divergence in the density of states—leaving frequently distinct signatures in physical properties. We use a new theoretical method to detect and analyse higher-order VHS (HOVHS) in two-dimensional materials and apply it to the electronic structure of the surface layer of Sr2RuO4. We then constrain a low energy model of the VHS of the surface layer of Sr2RuO4 against angle-resolved photoemission spectroscopy and quasiparticle interference data to analyse the VHS near the Fermi level. We show how these VHS can be engineered into HOVHS.

A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER.

Evaluation of a novel forward-looking optical coherence tomography probe for endoscopic applications: an ex vivo feasibility study.

As a result of recent advances in the development of small microelectromechanical system mirrors, a novel forward-looking optical coherence tomography (OCT) probe with a uniquely large field of view is being commercially developed. The aim of this study is to prospectively assess the feasibility of this advanced OCT probe in interpreting ex vivo images of colorectal polyp tissue and to identify necessary steps for further development. A total of 13 colorectal lesions from 9 patients, removed during endoscopic resection, were imaged ex vivo with the OCT device and compared with histopathological images that served as the gold standard for diagnostics. Normal tissue from one patient, removed during the endoscopic procedure, was imaged as a negative control. We assessed the presence of features indicative for polyp type and degree of dysplasia, by comparing OCT images to histopathological images and by evaluating the presence of OCT-specific features identified by previous studies, such as effacement (loss of layered tissue structure), a hyperreflective epithelial layer, and irregularity of the surface. As verified by corresponding histological images, tissue structures such as blood vessels and tissue layers could be distinguished in OCT images of the normal tissue sample. Detailed structures on histological images such as crypts and cell nuclei could not be identified in the OCT images. However, we did identify OCT features specific for colorectal lesions, such as effacement and a hyperreflective epithelial layer. In general, the imaging depth was about 1mm. Some relevant tissue structures could be observed in OCT images of the novel device. However, some adaptations, such as increasing imaging depth using a laser with a longer central wavelength, are required to improve its clinical value for the imaging of colorectal lesions.

Pronouns beyond phi-features: the speaker–addressee relation in Japanese pronouns and its implications for formal pronouns

Abstract Greenberg’s Universal 42 states that all languages have pronominal categories involving at least three persons and two numbers. However, this characterization fails to capture the properties of pronouns in Japanese, which are not bundles of person, number and gender features (so-called phi-features); rather, they contain sociolinguistic information about the interlocutors. We propose that these properties are structurally determined. Following Ritter and Wiltschko, we assume that the highest layer of structure in nominals is interactional structure. As for phi-features, we adopt the standard assumption that they are represented internal to the determiner phrase (DP). We propose that the distinctive properties of Japanese pronouns follow from the hypothesis that they spell out elements of the interactional structure and not the DP. We show that the lack of phi-features in Japanese pronouns correlates with other properties of this language’s grammar. Support for this analysis comes from languages where pronouns with phi-features can optionally be used to encode formality (e.g. German and French). We propose that in these languages, formal pronouns originate within the DP but are interpreted in the interactional structure. Finally, we suggest that this analysis may extend to imposters and vocatives in that they may also be interpreted in the interactional structure.