Local Domain Research Articles

Manipulating the Charge Carriers Through Functionally Bridged Components Advances Low‐Cost Organic Solar Cells with Green Solvent Processing

AbstractOrganic solar cell (OSC) development continues to demonstrate impressive device efficiency improvements. However, the materials synthetic simplicity essential to industrialization remains seriously lacking attention, imparting inferior performance records in low‐cost devices. Hence, low bandgap and completely non‐fused electron acceptors (CNFEAs) having simple molecular structures are investigated herein. In contrast to typically explored fused‐ring acceptors with smaller backbone conformational variations, minimizing the interface recombination sites through a greater extent of localized domains is identified as more critical in CNFEAs, leading to remarkable fill factors (FFs) approaching 75%, among the highest currently realized for low‐cost systems. However, this comes with diminishing charge generation efficiency. The general ternary blend optimization strategy modifying the morphology of host components is limited in preserving such remarkably high FFs. To suppress the trade‐off while keeping notable FFs, a new perspective of constructing functionally bridged components based on optical, electronic, and thermodynamic properties is introduced here. Specifically, charge generation is unrestrained from the host acceptor localized domains through the introduction of a “bridge” component while also taking advantage of the configuration to channel polarons toward the efficient transport moieties of the host components. Accordingly, this work incubates understanding‐guided optimizations toward the advancement of more practical devices.

Local domain generalization with low-rank constraint for EEG-based emotion recognition.

As an important branch in the field of affective computing, emotion recognition based on electroencephalography (EEG) faces a long-standing challenge due to individual diversities. To conquer this challenge, domain adaptation (DA) or domain generalization (i.e., DA without target domain in the training stage) techniques have been introduced into EEG-based emotion recognition to eliminate the distribution discrepancy between different subjects. The preceding DA or domain generalization (DG) methods mainly focus on aligning the global distribution shift between source and target domains, yet without considering the correlations between the subdomains within the source domain and the target domain of interest. Since the ignorance of the fine-grained distribution information in the source may still bind the DG expectation on EEG datasets with multimodal structures, multiple patches (or subdomains) should be reconstructed from the source domain, on which multi-classifiers could be learned collaboratively. It is expected that accurately aligning relevant subdomains by excavating multiple distribution patterns within the source domain could further boost the learning performance of DG/DA. Therefore, we propose in this work a novel DG method for EEG-based emotion recognition, i.e., Local Domain Generalization with low-rank constraint (LDG). Specifically, the source domain is firstly partitioned into multiple local domains, each of which contains only one positive sample and its positive neighbors and k2 negative neighbors. Multiple subject-invariant classifiers on different subdomains are then co-learned in a unified framework by minimizing local regression loss with low-rank regularization for considering the shared knowledge among local domains. In the inference stage, the learned local classifiers are discriminatively selected according to their importance of adaptation. Extensive experiments are conducted on two benchmark databases (DEAP and SEED) under two cross-validation evaluation protocols, i.e., cross-subject within-dataset and cross-dataset within-session. The experimental results under the 5-fold cross-validation demonstrate the superiority of the proposed method compared with several state-of-the-art methods.

City-scale continual neural semantic mapping with three-layer sampling and panoptic representation

Neural implicit representations are drawing much attention from the robotics community recently, as they are expressive, continuous and compact. However, city-scale continual implicit dense mapping based on sparse LiDAR input is still an under-explored challenge. To this end, we successfully build a city-scale continual neural mapping system with a panoptic representation that consists of environment-level and instance-level modeling. Given a stream of sparse LiDAR point cloud, it maintains a model that maps 3D coordinates to signed distance field (SDF) values. To address the difficulty of representing geometric information at different levels in city-scale space, we propose a tailored three-layer sampling strategy to sample the global, local and near-surface domains dynamically. Meanwhile, to realize high fidelity mapping of instance under incomplete observation, category-specific prior is introduced to better model the geometric details. We evaluate on the public SemanticKITTI [1] dataset and demonstrate the significance of the newly proposed three-layer sampling strategy and panoptic representation, using both quantitative and qualitative results. Codes and model will be publicly available.

Spatio-temporal human action localization in indoor surveillances

Spatio-temporal action localization is a crucial and challenging task in the field of video understanding. Existing benchmarks for spatio-temporal action detection are limited by factors such as incomplete annotations, high-level non-universal actions, and uncommon scenarios. To address these limitations and facilitate research in real-world security applications, we introduce a novel human-centric dataset for spatio-temporal localization of atomic actions in indoor surveillance settings, termed as HIA (Human-centric Indoor Actions). The HIA dataset is constructed by selecting 30 atomic action classes, compiling 100 surveillance videos, and annotating 219,225 frames with 370,937 bounding boxes. The primary characteristics of HIA include (1) accurate spatio-temporal annotations for atomic actions, (2) human-centric annotations at the frame level, (3) temporal linking of persons across discontinuous tracks, and (4) utilization of indoor surveillance videos. Our HIA, with its realistic settings in indoor surveillance scenes and comprehensive annotations, presents a valuable and novel challenge to the spatio-temporal action localization domain. To establish a benchmark, we evaluate various methods and provide an in-depth analysis of the HIA dataset. The HIA dataset will be made available soon, and we anticipate that it will serve as a standard and practical benchmark for the research community.

GLDAN: Global and Local Domain Adaptation Network for Cross-Wind Turbine Fault Diagnosis

Abstract Operating under harsh conditions and exposed to fluctuating loads for extended periods, wind turbines experience a heightened vulnerability in their key components. Early fault detection is crucial to enhance the reliability of wind turbines, minimize downtime, and optimize power generation efficiency. Although deep learning techniques have been widely applied to fault diagnosis tasks, yielding remarkable performance, practical implementations frequently confront the obstacle of acquiring a substantial quantity of labeled data to train an effective deep learning model. Consequently, this paper introduces an unsupervised global and local domain adaptation network (GLDAN) for fault diagnosis across wind turbines, enabling the model to efficiently transfer acquired knowledge to the target domain in the absence of labeled data. This feature renders it an appropriate solution for situations with limited labeled data availability. Employing adversarial training, GLDAN aligns global domain distributions, diminishing the overall discrepancy between source and target domains, and local domain distributions within a single fault category for both domains, capturing more intricate and specific fault features. The proposed approach is corroborated using actual wind farm data, and comprehensive experimental results demonstrate that GLDAN surpasses deep global domain adaptation methods in cross-wind turbine fault diagnosis, underlining its practical value in the field.

Tetraspanner-based nanodomains modulate BAR domain-induced membrane curvature.

The topography of biological membranes is critical for formation of protein and lipid microdomains. One prominent example in the yeast plasma membrane (PM) are BAR domain-induced PM furrows. Here we report a novel function for the Sur7 family of tetraspanner proteins in the regulation of local PM topography. Combining TIRF imaging, STED nanoscopy, freeze-fracture EM and membrane simulations we find that Sur7 tetraspanners form multimeric strands at the edges of PM furrows, where they modulate forces exerted by BAR domain proteins at the furrow base. Loss of Sur7 tetraspanners or Sur7 displacement due to altered PIP2 homeostasis leads to increased PM invagination and a distinct form of membrane tubulation. Physiological defects associated with PM tubulation are rescued by synthetic anchoring of Sur7 to furrows. Our findings suggest a key role for tetraspanner proteins in sculpting local membrane domains. The maintenance of stable PM furrows depends on a balance between negative curvature at the base which is generated by BAR domains and positive curvature at the furrows' edges which is stabilized by strands of Sur7 tetraspanners.

UniTmp: unified resources for transmembrane proteins.

The UNIfied database of TransMembrane Proteins (UniTmp) is a comprehensive and freely accessible resource of transmembrane protein structural information at different levels, from localization of protein segments, through the topology of the protein to the membrane-embedded 3D structure. We not only annotated tens of thousands of new structures and experiments, but we also developed a new system that can serve these resources in parallel. UniTmp is a unified platform that merges TOPDB (Topology Data Bank of Transmembrane Proteins), TOPDOM (database of conservatively located domains and motifs in proteins), PDBTM (Protein Data Bank of Transmembrane Proteins)and HTP (Human Transmembrane Proteome) databases and provides interoperability between the incorporated resources and an easy way to keep them regularly updated. The current update contains 9235 membrane-embedded structures, 9088 sequences with 536035 topology-annotated segmentsand 8692 conservatively localized protein domains or motifs as well as 5466 annotated human transmembrane proteins. The UniTmp database can be accessed at https://www.unitmp.org.

Modern Monads: Leibniz, Continuity, and the Stream of Consciousness

What is a perceiving ‘me’ or ‘soul’ and does it endure, perhaps for eternity, or is it merely one of a sequence of evanescent events? Conscious perception poses several puzzles in relation to subject identity and continuity that Leibniz paid close attention to. Re-applying Leibniz’s principles in a modern context suggests two conclusions. Firstly, his search for fundamental indivisible (monadic) perceiving dynamic units looks as motivated as ever. Secondly, modern physics may suggest ways to mitigate the temporal paradox raised for Leibniz by Russell, in which events of experience may be both distinct and part of a continuum. Recent condensed matter physics provides a hierarchy in indivisible dynamic units that invites a more subtle application of Leibniz’s ideas on continuity and divisibility. Our experience as a ‘string of mental pearls’ may be peculiar to certain types of fundamental dynamic unit with a ‘constant internal principle of change’ but variable energy content, typified by collective modes of excitation in condensed matter. In simple terms, a local domain of the electromagnetic field may ‘inform’ a collective mode (such as an acoustic mode) both by bringing it into existence and, in addition, through ongoing variation, updating the mode about changing events to which the mode can respond with shifts in energy content. These shifts will divide the mode into individual indivisible excitations - perhaps the best candidates for individual events of experience. Leibniz’s monadic analysis may not escape intact, but his guiding principles may.

Implication of cytoplasmic p53 expression in pulmonary neuroendocrine carcinoma using next-generation sequencinganalysis.

Cytoplasmic p53 expression indicates a high frequency of TP53 abnormalities in gynaecological carcinoma. However, the implication of this expression in pulmonary neuroendocrine carcinoma (NEC) remains unclear. Thus, our study aimed to fill this research gap. Immunohistochemistry (IHC) of p53 was performed on 146 cases of resected small-cell lung carcinoma and large-cell NEC, and next-generation sequencing was conducted on cases showing cytoplasmic and wild-type p53 expression. IHC revealed overexpression in 57% of the cases (n = 83), complete absence in 31% (n = 45), cytoplasmic expression in 8% (n = 12) and wild-type expression in 4% (n = 6) of the cases. TP53 mutations were identified in nine of the 13 cases with available genetic analysis. The TP53 mutation rates in cases with cytoplasmic and wild-type p53 expression were 88% (seven of eight) and 40% (two of five), respectively. All seven cases showing cytoplasmic expression with TP53 mutations harboured loss-of-function type mutations: four had mutations in the DNA-binding domain, two in the nuclear localisation domain and one in the tetramerisation domain. Clinically, cases with cytoplasmic p53 expression had a poor prognosis similar to that in cases with p53 overexpression or complete absence. Cytoplasmic p53 expression in patients with pulmonary NEC suggests a high TP53 mutation rate, which is associated with a poor prognosis similar to that in patients with p53 overexpression or complete absence. This cytoplasmic expression should not be misidentified as a wild-type expression. This is the first report, to our knowledge, that demonstrates the implication of cytoplasmic p53 expression in pulmonary NEC.

Multiple domain dynamic feature adaption transfer learning method for stranded wires health monitoring under variable vibration working conditions using laser-generated ultrasonic guided wave

Laser-generated ultrasonic guided wave is proved as a promising way to the structural health monitoring (SHM) of Stranded wires. However, variable vibrations would greatly affect the damage identification precision of stranded wires for the consequent distribution changes to collected signals. Aiming at this problem, a robust domain adversarial adaptation capsule network (DAACN) is proposed to reduce the influence of vibration and achieve reliable health state identification. Firstly, the UGW signals are preprocessed by normalization, phase alignment and bandpass filtering. Afterward, these preprocessed signals are divided into source and target signals and mapped to a high-dimensional feature domain space by deep feature extractor. Furthermore, an adversarial learning model, which contains a global domain discriminator and a local domain discriminator, is designed to help align the marginal distribution and conditional distribution of the two domain features. Then, a damage decoupled capsule network is built to obtain the relationship between the source features and damage labels. Finally, by joint adversarial training among the damage decoupled capsule network, global and local discriminator, the final model is obtained to identify the real structural state of test signals. Experimental results show that the proposed DAACN method can effectively align the damage feature under different vibration working conditions. Identification precision of more than 99% in all conditions also proves the superiority of the proposed method.

A tsunami deposit in the Stonewall Formation (Upper Ordovician), northeastern margin of the Williston Basin, Canada, and its tectonic and stratigraphic implications

The dolomudstone of the Stonewall Formation was deposited on the present-day northeastern side of the low-relief, relatively shallow, tropical Williston Basin of central Laurentia during the latest Ordovician. This unit hosts a geographically widespread carbonate conglomerate, up to 0.5 m thick and consisting of rudstone to floatstone, that thins and eventually disappears westward and southward. The bed contains dolomudstone intraclasts in a variably argillaceous dolomudstone matrix and varies in composition laterally and vertically. Intraclasts range from millimetres to centimetres in size, are spheroidal to tabular to irregular in shape, and are angular to rounded. They record incipient microcrystalline calcite precipitation in localised domains in lime mud that began just below the sediment–water interface, forming lightly cemented nodules in an unconsolidated matrix, and indicate exhumation and deposition by a single, short-duration, high-energy but variable event that fragmented and abraded many of the nodules and washed in clay and lime mud. The composite nature of the bed in terms of varying proportion of intraclasts versus mud, and range of intraclast size with variable rounding and sorting, suggest that the event fluctuated in intensity and current direction. We interpret this bed as having been formed by a succession of tsunami waves and backwash that scoured the sea bottom and reworked the nodules, and brought in suspended illite from shallow water and the adjacent land surface. Despite drainage from tsunami-inundated coastal areas, the absence of allochthonous quartz sand and silt suggests that there was no sediment of this size stored there. Backwash transport may also have played a role in the formation of argillaceous marker beds which have been attributed previously to sea level fall and subaerial exposure. Other anomalous beds that punctuate shallow epeiric sea successions may also be tsunami deposits, especially if the ambient paleoclimate and bathymetry were not conducive to generating or leaving a signal of major storms, and sea level fluctuation can be discounted.

НАУКОВА АКТИВНІСТЬ НАУКОВО-ПЕДАГОГІЧНИХ ПРАЦІВНИКІВ УНІВЕРСИТЕТУ: РЕФЛЕКСІЇ ВПЛИВУ УМОВ СУЧАСНОСТІ

Contemporary global development conditions present a range of challenges and opportunities to academic experts in higher education institutions, who must navigate them with limited resources and varying circumstances. The study outlined in this article aims to investigate the factors that influence the scholarly engagement of academic personnel across different timeframes, encompassing both times of peace and conflict. The findings from the analysis underscore critical limitations and motivations that mold the scholarly conduct of university educators. During periods of tranquility, the principal barriers to scholarly engagement include substantial administrative and methodological workloads, as well as limited financial impetus. In times of military events, emotional fatigue and financial hurdles emerge as primary factors curbing the scholarly involvement of academic staff, particularly when coupled with excessive workloads. Moreover, the research results reveal that adaptable work environments, embracing hybrid learning models, also significantly contribute to scholarly involvement. The analysis brings to light the need for dedicated research time in scholarly activities, necessitating a harmonious balance between teaching, methodological commitments, administrative duties, and opportunities for profound research. An intriguing revelation is that young instructors prove most productive, emphasizing the critical role of their guidance and growth. The study's deductions emphasize the importance and imperative of streamlining university operations, particularly through the equitable allocation of workloads, prioritization of psychological and emotional well-being, and establishment of favorable circumstances for scholarly commitment, especially among emerging academics, across local, regional, and national domains. These findings establish a groundwork for crafting effective strategies aimed at sustaining academic experts and fostering scholarly engagement within universities amidst a backdrop of dynamic transformations. Keywords: scientific activity, academic professionals,university, war, peacetime, Covid-2019.

Minimizing electric currents for ionothermal synthesis of molecular sieve films by control of interfacial microenvironment

Minimizing electric currents in the electrosynthesis of molecular sieve films is critically challenging. Herein we demonstrate the electric currents can be significantly minimized to catalyze AEL molecular sieve films nucleation in ionic liquids by the control of interfacial microenvironment. In the local microenvironment, the nucleation promoter of water and structure directing agents of ionic liquids are believed to be linked via hydrogen bonding in the electrical double layer, thus lowering the currents to 10e-8 A. The corresponding catalytic efficiency reaches up to as high as 105. This study sheds lights on manipulating the local chemical domain in the interfacial structure using extremely low currents for the synthesis chemistry.

A holistic approach for using global climate model (GCM) outputs in decision making

All human endeavours are affected by climate change and local weather, and 21st-century climate-change estimates will provide greater challenges. Therefore, numerical model-based climate projections (i.e., General Circulation Models – GCMs) are essential for making decisions about adaptation, mitigation, and resilience building to combat adverse climate impacts. However, these coarser resolution model projections have uncertainties resulting in significant doubts in decision-making, particularly in the local or regional domain. Thus, we suggest five principles for GCM use at local and regional scales to overcome uncertainties in decision-making. This study examines historical and projected precipitation from 44 GCMs, calculated under the Representative Concentration Pathways (RCP) 8.5 scenario considering regional, geographical, and temporal variability for establishing a decision-making support system based on the degree of confidence for nine diverse main river basins in tropical Sri Lanka as a case study. Each GCM confirmed the current climatic pattern in terms of wind vector and meridional wind patterns out of eight climate variables considered. GCM sensitivity varies geographically and temporally. An annual precipitation study is not enough to make climate change judgments because climate change signals change seasonally. Each basin's average annual precipitation was projected to likely increase over the near, middle, and far future (2025–2050, 2050–2075, 2075–2100, respectively); however, in the Mahaweli basin (MB), the largest basin neighboring most of the other basins, the annual precipitation was projected to extremely likely increase. Most of MB's surrounding basins show a more-likely-than-not decreasing precipitation tendency for inter-monsoon-1 in the future (2025–2100); however, MB shows a more-likely-than-not increasing trend. Our analysis shows that the southwest monsoon and inter-monsoon-2 are more likely than not or extremely likely to strengthen, whereas the northeast monsoon (NEM) and inter-monsoon-1 are more likely than not to weaken, excluding NEM in the near future. Here, we introduced a simple color-coded climate change (C4) matrix for decision-making that provides spatial, temporal, and seasonal climate change projections likelihood trends with current observed seasonal trends. Future projections indicate a swapping in seasonal precipitation trends between IM-1 and SWM for almost basins compared to the past. NEM appears to have a weakening influence on the majority of northeast-facing basins during the middle and far future. A simple detailed climate analysis chart may be more effective in communicating scientific messages to the scientific community and the key public actors who make decisions.

ALGEBRAIC SEMANTICS FOR RELATIVE TRUTH, AWARENESS, AND POSSIBILITY

AbstractThis paper puts forth a class of algebraic structures, relativized Boolean algebras (RBAs), that provide semantics for propositional logic in which truth/validity is only defined relative to a local domain. In particular, the join of an event and its complement need not be the top element. Nonetheless, behavior is locally governed by the laws of propositional logic. By further endowing these structures with operators—akin to the theory of modal Algebras—RBAs serve as models of modal logics in which truth is relative. In particular, modal RBAs provide semantics for various well-known awareness logics and an alternative view of possibility semantics.

Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature

Point cloud deep learning networks have been widely applied in point cloud classification, part segmentation and semantic segmentation. However, current point cloud deep learning networks are insufficient in the local feature extraction of the point cloud, which affects the accuracy of point cloud classification and segmentation. To address this issue, this paper proposes a local domain multi-level feature fusion point cloud deep learning network. First, dynamic graph convolutional operation is utilized to obtain the local neighborhood feature of the point cloud. Then, relation-shape convolution is used to extract a deeper-level edge feature of the point cloud, and max pooling is adopted to aggregate the edge features. Finally, point cloud classification and segmentation are realized based on global features and local features. We use the ModelNet40 and ShapeNet datasets to conduct the comparison experiment, which is a large-scale 3D CAD model dataset and a richly annotated, large-scale dataset of 3D shapes. For ModelNet40, the overall accuracy (OA) of the proposed method is similar to DGCNN, RS-CNN, PointConv and GAPNet, all exceeding 92%. Compared to PointNet, PointNet++, SO-Net and MSHANet, the OA of the proposed method is improved by 5%, 2%, 3% and 2.6%, respectively. For the ShapeNet dataset, the mean Intersection over Union (mIoU) of the part segmentation achieved by the proposed method is 86.3%, which is 2.9%, 1.4%, 1.7%, 1.7%, 1.2%, 0.1% and 1.0% higher than PointNet, RS-Net, SCN, SPLATNet, DGCNN, RS-CNN and LRC-NET, respectively.

Effect of Underlayer Quality on Microstructure, Stoichiometry, and Magnetic Properties of Hexaferrite BaFe12O19 Grown on YSZ(111) by Pulsed Laser Deposition.

We have studied the effect of platinum underlayer for two deposited thicknesses on the microstructure, crystalline quality, morphology, chemical composition, and magnetic properties as well as magnetic domain formation of BaFe12O19 (BaM) grown on YSZ(111) by pulsed laser deposition (PLD). We found that PLD platinum deposited with a thickness of 25 nm cannot withstand the dewetting phenomenon occurring during the subsequent BaM layer growth. A smooth and continuous Pt underlayer that possesses a sharp interface and omits the intermixing between the BaM and substrate was successfully achieved for a deposited Pt film thickness of 75 nm. Independent of the thickness of the deposited Pt layer, the c-axis orientation as well as coercivity Hc and the anisotropy HA fields were significantly improved due to a remarkable improvement of lattice mismatch in comparison with the BaM layer grown without a Pt underlayer on YSZ(111). By applying high-resolution X-ray diffraction, scanning and transmission electron microscopy (SEM/TEM), and atomically resolved scanning TEM imaging combined with energy-dispersive X-ray spectroscopy, as well as atomic and magnetic force microscopy, a comprehensive investigation of both structure and chemical composition of the deposited BaM films and their interfacial regions was performed. This study aimed to correlate the enhancement of the overall magnetic properties and of the local spin magnetic domain orientation with the modification of BaM microstructure and chemical composition at the nanometer scale due to the Pt underlayer. Finally, we attempted to understand the mechanisms that control the magnetic properties of these BaM films in order to be able to tailor them.

A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization

There are many tricky optimization problems in real life, and metaheuristic algorithms are the most effective way to solve optimization problems at a lower cost. The dung beetle optimization algorithm (DBO) is a more innovative algorithm proposed in 2022, which is affected by the action of dung beetles such as ball rolling, foraging, and reproduction. Therefore, A dung beetle optimization algorithm is proposed based on quasi-oppositional learning and Q-learning (QOLDBO). First, the quantum state update idea is cleverly integrated into quasi-oppositional learning to increase the randomness of the generated population. And the best behavior pattern is selected by adding Q-learning in the rolling stage to improve the search effect. In addition, the variable spiral local domain method is proposed to make up for the shortage of developing only around the neighborhood optimum. For the optimal solution of each iteration, the dimensional adaptive Gaussian variation is selected and the optimal solution is retained. Experimental performance tests show that QOLDBO performs well in both benchmark test functions and CEC 2017. Simultaneously, the validity of the algorithm is verified on several classical practical application engineering problems.

Investigation on the pressure and flow characteristics for a high-speed bullet in high-altitude flight

The pressure analysis and flow characteristics during the flight of a high-speed bullet are significant for aerodynamic performance, flight trajectory control, safety and reliability, performance optimization, guidance and navigation. To accurately monitor the pressure variation for a high-speed bullet in the high-altitude flight, a novel local fluid domain method is introduced in current study. This method overcomes the limitations of conventional global fluid domain simulation methods, which often suffer from poor convergence and stability issues. On the basis, the force analysis is conducted and the mathematical model for the theoretical trajectory is established, which is applied to simulate the flight trajectory and construct fluid domain for the high-speed bullet. The findings reveal that monitoring points within the bullet hole are influenced by both cavity flow and the collision of forward airflows during the high-altitude flight. Furthermore, it is observed that the absolute total pressure and reference pressure at these monitoring points within the bullet hole are influenced by altitude variations. Subsequently, the factors affecting the absolute total pressure within the hole are explored, including the hole depth, the hole diameter, the hole position, and the bullet orientation. The results indicate that modifying the bullet structure to incorporate 45° angled hole, as opposed to standard holes, can lead to a 13.6% reduction in absolute total pressure variation. Additionally, mitigating the interaction between the external and internal fluids emerges as a critical factor in reducing absolute total pressure within the hole. This research not only presents a fresh perspective for pressure monitoring within the bullet hole, but also establishes a theoretical foundation for the precise guidance of high-altitude bullets.

Multiscale understanding the effect of K/Na ratio on electrical properties of high‐performance KNN‐based ceramics

AbstractAlthough certain efforts were paid to the effect of K/Na ratio on the phase structure and piezoelectric properties of potassium sodium niobate {(K, Na)NbO3, KNN}‐based ceramics, insufficient attention was given to the local structure and ferroelectric domains, hindering the further understanding of physical mechanisms. Herein, we studied the effect of the K/Na ratio on KNN‐based ceramics with multi‐phase coexistence from multiscale structure. High Na+ content leads to local stress heterogeneity and retains large ferroelectric domains, while high K+ content rotates the NbO6 octahedron and results in local polarity heterogeneity and distinct dielectric relaxational behavior, consequently fragmenting ferroelectric domains. The improved piezoelectricity is obtained at the equal K/Na ratio and originates from the balanced local polarity and stress heterogeneities. Therefore, an appropriate K/Na ratio should be fully considered when designing compositions with the purpose of improving performance of KNN‐based ceramics in the future.