Structural Features Research Articles

Anomalous Hall Effect due to Magnetic Fluctuations in a Ferromagnetic Weyl Semimetal.

The anomalous Hall effect (AHE) has emerged as a key indicator of time-reversal symmetry breaking (TRSB) and topological features in electronic band structures. Absent of a magnetic field, the AHE requires spontaneous TRSB but has proven hard to probe due to averaging over domains. The anomalous component of the Hall effect is thus frequently derived from extrapolating the magnetic field dependence of the Hall response. We show that discerning whether the AHE is an intrinsic property of the field-free system becomes intricate in the presence of strong magnetic fluctuations. As a study case, we use the Weyl semimetal PrAlGe, where TRSB can be toggled via a ferromagnetic transition, providing a transparent view of the AHE's topological origin. Through a combination of thermodynamic, transport, and muon spin relaxation measurements, we contrast the behavior below the ferromagnetic transition temperature to that of strong magnetic fluctuations above. Our results on PrAlGe provide general insights into the interpretation of anomalous Hall signals in systems where TRSB is debated, such as families of kagome metals or certain transition metal dichalcogenides.

Exploring mathematics teachers’ integration of technology into classroom teaching practice: a focus on geometric similarity

Digital technologies like dynamic mathematical technology (DMT) offer considerable promise for enhancing mathematics education. Despite widespread calls for integrating these tools into classroom practice, their global adoption remains limited. This gap underlines the necessity for a better, holistic understanding of teachers' integration of technology in the practice. Adopting Ruthven's Structuring Features of Classroom Practice (SFCP) framework, this study aims to investigate lower secondary mathematics teachers' integration of DMT into classroom teaching practice with a focus on geometric similarity (GS). Case studies were conducted with two mathematics teachers from England, each possessing different levels of experience in using technology. Data were primarily collected through video-recorded classroom observations and audio-recorded post-lesson teacher interviews. The findings indicated notable differences and some similarities between the teachers, highlighting core characteristics of (domain-specific) practices involving DMT. Additionally, both teachers expressed the efficacy of integrating DMT into their classroom teaching of geometric similarity in enhancing pupil learning. The study offers valuable insights into the use of DMT in teaching mathematics (i.e. geometric similarity) in everyday classroom practice, providing evidence-based characteristics of effective technology integration in practice. The findings could inform teacher training programmes and policy decisions, ultimately supporting the broader integration of technology in mathematics education.

Convergent vocal representations in parrot and human forebrain motor networks.

Cortical networks for the production of spoken language in humans are organized by phonetic features1,2, such as articulatory parameters3,4 and vocal pitch5,6. Previous research has failed to find an equivalent forebrain representation in other species7-11. To investigate whether this functional organization is unique to humans, here we performed population recordings in the vocal production circuitry of the budgerigar (Melopsittacus undulatus), a small parrot that can generate flexible vocal output12-15, including mimicked speech sounds16. Using high-density silicon probes17, we measured the song-related activity of a forebrain region, the central nucleus of the anterior arcopallium (AAC), which directly projects to brainstem phonatory motor neurons18-20. We found that AAC neurons form a functional vocal motor map that reflects the spectral properties of ongoing vocalizations. We did not observe this organizing principle in the corresponding forebrain circuitry of the zebra finch, a songbird capable of more limited vocal learning21. We further demonstrated that the AAC represents the production of distinct vocal features (for example, harmonic structure and broadband energy). Furthermore, we discovered an orderly representation of vocal pitch at the population level, with single neurons systematically selective for different frequency values. Taken together, we have uncovered a functional representation in a vertebrate brain that displays unprecedented commonalities with speech-related motor cortices in humans. This work therefore establishes the parrot as an important animal model for investigating speech motor control and for developing therapeutic solutions for addressing a range of communication disorders22,23.

Graph-Dynamics correspondence in metallic glass-forming liquids

Abstract Theoretical challenges in understanding the nature of glass and the glass transition remain significant open questions in statistical and condensed matter physics. As a prototypical example of complex physical systems, glasses and the vitrification process have been central research topics, consistently attracting broad scientific interest. This focus has driven extensive studies on phenomena such as aging, non-exponential relaxation, dynamic anomalies, glass-forming ability, and the mechanical response of glasses under stress. Recent advances in computational and experimental techniques have enabled rigorous testing of theoretical models, shedding new light on glassy behavior. However, the intrinsic complexity of glass and the glass transition that lies in their physics, which spans multiple length and time scales, makes the system challenging to characterize. In this review, we emphasize the need to move beyond conventional approaches and propose a topological perspective as a promising alternative to address these challenges. Specifically, our findings reveal that the diversity in particle relaxation behavior is statistically linked to a global topological feature of the transient network structures formed by the particles in a given liquid. This direction offers opportunities to uncover novel phenomena that could fundamentally reshape our understanding of glassy materials.

Fermiology and Band Structure of Oxygen-Terminated Ti_{3}C_{2}T_{x} MXene.

The class of two-dimensional carbides and nitrides known as MXenes exhibit remarkable electronic properties. Tailoring these properties, however, requires an in-depth understanding of the band structure and Fermi-surface topology. Surface oxidation of MXenes has previously hampered the characterization of their Fermi surface, which is crucial for understanding the topology and anisotropy in the electronic structure and, ultimately, for tailoring electronic properties. Here, we reveal the Fermi surface topology and band structure of purely oxygen-terminated Ti_{3}C_{2}T_{x} MXene achieved through rigorous thin film sample preparation and ultrahigh vacuum annealing. Polarized synchrotron radiation-based angle-resolved photoemission spectroscopy reveals electron pockets, bulk band gaps, and a Dirac-like feature in the anisotropic electronic band structure. This paves the way for a fundamental understanding of band engineering of electronic transport properties, providing insights of importance for energy storage devices, transparent conductors, and catalysis.

Probing moiré electronic structures through quasiparticle interference

Moiré lattices are a general feature of bilayer structures, where an additional periodic superstructure is generated by either lattice mismatch or from a twist angle. They have been shown to stabilize exotic ground states, including unconventional superconductivity and Mott insulating phases, attributed to strong electron correlations. However, controlling these interactions requires a detailed understanding of the low-energy electronic structure, which is lacking so far. Probing the electronic structure is challenging due to sample inhomogeneity, the low characteristic energy scales involved, and small sample sizes. Through quasiparticle interference (QPI) imaging, scanning tunneling microscopy (STM) can overcome many of these challenges but requires detailed modeling to extract the k-space electronic structure. Here, we present realistic calculations of QPI in twisted bilayer structures, which accounts for the effect of the long-range moiré lattice on the electronic structure as well as its interaction at a defect. These calculations reveal that, while the moiré supercell significantly reduces the size of the Brillouin zone, the QPI scattering vectors retain characteristics of the individual monolayers with distinct perturbations from the twisted geometry that can be directly linked back to the electronic structure. The procedure introduced here provides a general framework to use QPI to determine the low-energy electronic structure in moiré lattice systems. Published by the American Physical Society 2025

The fabrication of conveyor rubber belt via bio‐oil obtained by rice husk pyrolysis

AbstractBio‐oil, also called pyrolysis oil, bio‐fuel oil, pyrolytic oil, or liquid wood, is produced through biomass pyrolysis at high‐temperature decomposition without added oxygen. In this study, the use of the liquid product (bio‐oil) obtained as a result of the pyrolysis of rice husk was investigated. The aim of the work was the employment of obtained bio‐oil instead of rubber process oil and binder resin in rubber dough mixtures used in the production of conveyor belts. The bio‐oil obtained from pyrolysis of rice husk was used instead of resin and aromatic oil at rates of 25%, 50%, 75%, and 100% in the rubber fabrication. The optimum curing time was initially determined by rheometer tests before the vulcanization process. Then, the adhesion strengths rubber‐cloth (rubber‐weave) and cloth‐cloth (weave‐weave) of belt samples were tested and compared. Furthermore, the appearance and distribution on the surface of rubber samples were analyzed by scanning electron microscope (SEM). It was found that the bio‐oil derived from rice husk pyrolysis can be effectively utilized in rubber products due to its ability to reduce curing time. Moreover, the torque values also increased as the amount of bio‐oil was increased.Highlights Bio‐oil was obtained by pyrolysis of rice husk. Bio‐oil was employed as rubber process oil and softener oil in rubber dough. The adhesion and rheometer tests of rubber belt samples were investigated. Rice husk‐derived bio‐oil decreased the curing time of rubber fabrication. The cross‐linking feature in the belt structure increased.

Experimental Study of Compression Behavior on Monolayer FFF Samples

Additive manufacturing (AM) processes, such as Fused Filament Fabrication (FFF), enable the production of lightweight parts with high stiffness-to-weight ratios, making them highly suitable for a wide range of engineering applications. However, ensuring the mechanical reliability of these components, particularly for load-bearing purposes, requires systematic mechanical testing of well-designed specimens to asses their suitability. While the tensile properties of additively manufactured materials have been extensively studied, the compressive behavior of components produced via AM, particularly those made from thermoplastic materials, remains comparatively underexplored and insufficiently characterized in the existing body of research. Among these materials, polylactic acid (PLA)—a biodegradable thermoplastic derived from renewable resources—has gained prominence in AM applications. Recent studies have investigated the compression properties of PLA in reinforced materials; however, the focus has primarily been on solid, semi-solid, or porous specimens. These investigations largely overlook thin-walled structures, which are integral to weight-saving designs and commonly feature in topology-optimized structures. Understanding the mechanical behavior of monolayers, the fundamental building blocks of most AM components, is essential for accurately predicting the overall performance of multilayer structures. Monolayers represent the smallest, most basic structural elements of AM parts, and their properties directly influence the behavior of the final, more complex assemblies. Establishing a methodology that correlates monolayer properties with those of multilayer components could significantly streamline testing procedures. By performing mechanical tests on monolayers, instead of on more intricate multilayer specimens, manufacturers could reduce testing complexity and cost while accelerating the development process. The current literature reveals a gap in the design and analysis of thin-walled AM specimens, especially monolayers, under compressive loads. Specifically, the design of monolayer or thin-walled AM compression specimens without infill has not been thoroughly explored. This article addresses this gap by investigating the design and testing of AM monolayer compression specimens produced using FFF of PLA. Three distinct specimen geometries are considered—circular, helicoidal, and S-shaped—to evaluate their potential for understanding and predicting the compressive behavior of AM monolayer structures.

SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection.

Plant disease detection remains a significant challenge, necessitating innovative approaches to enhance detection efficiency and accuracy. This study proposes an improved YOLOv8 model, SerpensGate-YOLOv8, specifically designed for plant disease detection tasks. Key enhancements include the incorporation of Dynamic Snake Convolution (DySnakeConv) into the C2F module, which improves the detection of intricate features in complex structures, and the integration of the SPPELAN module, combining Spatial Pyramid Pooling (SPP) and Efficient Local Aggregation Network (ELAN) for superior feature extraction and fusion. Additionally, an innovative Super Token Attention (STA) mechanism was introduced to strengthen global feature modeling during the early stages of the network. The model leverages the PlantDoc dataset, a highly generalizable dataset containing 2,598 images across 13 plant species and 27 classes (17 diseases and 10 healthy categories). With these improvements, the model achieved a Precision of 0.719. Compared to the original YOLOv8, the mean Average Precision (mAP@0.5) improved by 3.3%, demonstrating significant performance gains. The results indicate that SerpensGate-YOLOv8 is a reliable and efficient solution for plant disease detection in real-world agricultural environments.

Synthesis, Structural Studies of Some New Transition Metals Complexes of Semicarbazide hydro chloride Schiff Base Derivatives

A new series of transition metal complexes of Cu(II), Ni(II), Co(II) and Fe(III) have been synthesized from the Schiff base (L1) and (L2) derived from Semicarbazide hydro chloride and 4-chlorobenzaldehyde or 4-bromobenzaldehyde. The structural features have been arrived from their elemental analyses, magnetic susceptibility, molar conductivity, IR, UV-Vis. and 1H NMR spectral studies. The data show that the complexes have composition of [M(L)2](NO3)2 and [Fe(L)2 (NO3)2](NO3) where the M=Co(II),Ni(II) and Cu(II) ;L=L1and L2 type. The magnetic susceptibility and UV-Vis spectral data of the complexes suggest a square planer geometry for Co(II) and Cu(II) but Fe(III) octahedral geometry and Ni(II) tetrahedral geometry around the central metal ion.Hyper Chem-6 program has been used to predict structure geometries of compounds in gas phase .The electrostatic potential of the free ligands were calculated to investigate the reactive sites of the molecules .The heat of formation(?Hf ?) and binding energy(?Eb) at 298K for the free ligands and its metal complexes were calculated by using PM3 method.

Observation of uncommon elastic wave group velocity in common honeycomb structure and its potential for debonding detection

We report wave propagation with an uncommon negative group velocity (NGV) feature in the common honeycomb structure. Theoretical analysis indicates that the NGV feature arises from the repulsion between skin's localized resonance modes in the same family, influenced by the relative parameters between the core and skin, such as skin thickness. Experimental results show that the NGV feature in honeycomb structure is unlike in isotropic plates, and it only appears in the front half of the hexagon hollow unit, which is surrounded by the honeycomb core. More notably, the NGV feature disappears in the skin–core debonding area and reappears in the intact area, suggesting potential applicability for detecting debonding defects.

Insight into Robust Anion Coordination Behavior of Organic Cathode with Dual Elongated π-Conjugated Motifs.

Organic electrode materials offer multi-electron reactivity, flexible structures, and redox reversibility, but encounter poor conductivity and durability in electrolytes. To overcome above barriers, we propose a dual elongation strategy of π-conjugated motifs with active sites, involving the extended carbazole and electropolymerized polymer, which enhances electronic conductivity by the electronic delocalization of electron-withdrawing conjugated groups, boosts theoretical capacity by increasing redox-active site density, and endows robust electrochemical stability attributed to the nanonetwork feature of polymer structures. As a proof-of-concept, 5,11-dihydridoindolo[3,2-b]carbazole (DHIC) is selected as the model cathode material for a dual-ion battery, with elongated carbazole groups functioning both as redox-active centers and polymerization anchors. Electrochemical comparisons and theoretical simulations validate the excellent specific capacity, accelerated reaction kinetics, and enhanced anion storage stability imparted by the dual elongated π-conjugated system containing both carbazole motif and electropolymerized DHIC (pDHIC). Simultaneously, the coordination interaction between pDHIC and anions is innovatively evidenced through operando electron paramagnetic resonance spectra. As anticipated, pDHIC cathode delivers an unprecedentedly high specific capacity of 197 mAh/g at 50 mA/g, far outperforming graphite cathodes, and maintains excellent cycling stability with a capacity retention of 86.1 % over 500 cycles. This synergetic strategy sheds light on the performance revolution of organic electrode materials.

Testing the robustness of the BAO determination in the presence of massive neutrinos

We study the robustness of the Baryon Acoustic Oscillation (BAO) feature in the large-scale structure in the presence of massive neutrinos. In the standard BAO analysis pipeline a reference cosmological model is assumed to boost the BAO peak through the so-called reconstruction technique and in the modelling of the BAO feature to extract the cosmological information. State-of-the art spectroscopic BAO measurements, such as the Dark Energy Spectroscopic Instrument claim an aggregate precision of 0.52% on the BAO scale, with a systematic error of 0.1% associated to the assumption of a reference cosmology when measuring and analyzing the BAO feature. While the systematic effect induced by this arbitrary choice of fiducial cosmology has been studied for a wide range of ΛCDM-like models, it has not yet been tested for reference cosmologies with massive neutrinos with the precision afforded by next generation surveys. In this context, we employ the Quijote high-resolution dark-matter simulations with haloes above a mass of M ∼ 2×1013 h -1 M ⊙, with different values for the total sum of neutrinos masses, ∑mν [eV] = 0, 0.1, 0,2, 0.4 to study and quantify the impact of the pipeline's built-in assumption of massless neutrinos on the measurement of the BAO signal, with a special focus on the BAO reconstruction technique. We determine that any additional systematic bias introduced by the assumption of massless neutrinos is no greater than 0.1% (0.2%) for the isotropic (anisotropic) measurement. We expect these conclusions also hold for galaxies provided that neutrino properties do not alter the galaxy-halo connection.

Vegetation segmentation using oblique photogrammetry point clouds based on RSPT network

ABSTRACT Vegetation segmentation via point cloud data can provide important information for urban planning and environmental protection. The point cloud dataset is obtained using light detection and ranging (LiDAR) or RGB-D images. Oblique photogrammetry has received little attention as another important source of point cloud data. We present a pointwise annotated oblique photogrammetry point-cloud dataset that contains rich RGB information, texture, and structural features. This dataset contains five regions of Bengbu, China, with more than twenty thousand samples in this paper. Obviously, previous indoor point cloud semantic segmentation models are no longer applicable to oblique photogrammetry point clouds. A random sampling point transformer (RSPT) network is proposed to enhance vegetation segmentation accuracy. The RSPT model offers both efficient and lightweight architecture. In RSPT, random point sampling is utilized to downsample point clouds, and a local feature aggregation module based on self-attention is designed to extract additional representation features. The network also incorporated residual and dense connections (ResiDense) to capture both local and comprehensive features. Compared to state-of-the-art models, RSPT achieves notable improvements. The intersection over union (IoU) metric increased from 96.0% to 96.5%, the F1-score increased from 90.8% to 97.0%, and the overall accuracy (OA) increased from 91.9% to 96.9%.

East Barents Basin and Admiralty High Composite Tectono-Sedimentary Elements, Barents Sea

The East Barents Sea includes two major tectonic elements, the East Barents Basin (EBB) and the Admiralty High, which are, in the context of the volume, characterized as composite tectono-sedimentary elements. Whereas they share some stratigraphy, their early history may differ. The EBB is a dominant structural feature of the Barents Shelf, and one of the largest and deepest sedimentary basins in the Arctic. The Admiralty High rims the EBB in the east. The advance in understanding the East Barents Sea geology was achieved during the 1980s and 1990s mainly due to a significant amount of multichannel reflection seismic data and a number of drilled deep exploration wells. These wells tested many large anticlinal structures and made several large gas discoveries, including the giant Shtokman gas and gas condensate accumulation. In the following decades, many important geological results were published in the international and Russian literature. In this chapter we provide a summary of the geology and petroleum geology of the East Barents Sea based on published results and public-domain geological reports.

DeepFusion: A Deep Bimodal Information Fusion Network for Unraveling Protein-RNA Interactions Using In Vivo RNA Structures

DeepFusion: A Deep Bimodal Information Fusion Network for Unraveling Protein-RNA Interactions Using In Vivo RNA Structures

3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity

Background: Nitric oxide (NO), an important second messenger molecule, regulates numerous physiological responses, while excessive NO generates negative effects on the circulatory, nervous and immune systems. Recently, some natural flavonoids were reported to possess the capability of inhibiting LPS-induced NO production. To fully understand the nature of their own NO inhibitory activity, it is necessary to address the structural requirements of flavonoids as NO inhibitors. Objective: The objective of this work was to develop efficient QSAR models for predicting the NOinhibitory activity of new flavonoids and improving insights into the critical properties of the chemical structures that were required for the ideal NO production inhibitory activities. Methods: To provide insights into the structural basis of flavonoids as NO inhibitors, 3D quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR models were developed on a dataset of 55 flavonoids using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) approaches. Results: The statistically significant models for CoMFA, CoMSIA and HQSAR resulted in crossvalidated coefficient (q2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (r2) values of 0.793, 0.828 and 0.852, respectively. The robustness of these models was further affirmed using a test set of 18 compounds, which resulted in predictive correlation coefficients (r2 pred) of 0.968, 0.954 and 0.906. Furthermore, the models-derived contour maps were appraised for activity trends for the molecules analyzed. Conclusion: The 3D and 2D-QSAR models constructed in this paper were efficient in estimating the NO inhibitory activities of flavonoids and facilitating the design of flavonoid-derived NO production inhibitors.

Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.

Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors. The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues. The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy. ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity. The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.

Recent Advances in Pyrazole-based Protein Kinase Inhibitors as Emerging Therapeutic Targets.

Pyrazole-scaffold protein kinase inhibitors (PKIs) have emerged as promising therapeutic agents for the treatment of various diseases, such as cancer, inflammatory disorders, and neurological diseases. This review article provides an overview of the pharmacological properties of pyrazole-scaffold PKIs, including their mechanism of action, selectivity, potency, and toxicity. The article also summarizes the recent developments in the design and synthesis of pyrazole-scaffold PKIs, highlighting the structural features and modifications that contribute to their pharmacological activity. In addition, the article discusses the preclinical and clinical studies of pyrazole-scaffold PKIs, including their efficacy, safety, and pharmacokinetic properties. A comprehensive search has been conducted on several online patent databases, including the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO), and the World Intellectual Property Organization (WIPO). The search was conducted using pyrazole as the keyword. The search was limited to patents filed between 2015 and 2022. Patents were included if they involved articles in the fields of protein kinase inhibitors, and included literature on some pyrazoles and their pharmacological activities. Data were extracted from each included patent on the following variables: patent title, patent number, inventors, assignee, filing date, publication date, patent type, and field of invention. Data were extracted from each patent using a standardized form to ensure consistency and accuracy. The design and pharmacological evaluation of organic compounds containing pyrazole structure as biologically active substances have been done, and the key structures from the pharmacological data obtained as protein kinase inhibitors have been addressed in detail. The review concludes with a discussion on the current challenges and future directions for the development of pyrazole-scaffold PKIs as therapeutic agents. Overall, this review article provides a comprehensive summary of the pharmacological properties of pyrazole-scaffold PKIs, which will be of interest to researchers and clinicians in the field of drug discovery and development.

Dilated dendritic learning of global–local feature representation for medical image segmentation

Dilated dendritic learning of global–local feature representation for medical image segmentation