Bidirectional Field Research Articles

Transformer-based approach for symptom recognition and multilingual linking.

This paper presents a transformer-based approach for symptom Named Entity Recognition (NER) in Spanish clinical texts and multilingual entity linking on the SympTEMIST dataset. For Spanish NER, we fine tune a RoBERTa-based token-level classifier with Bidirectional Long Short-Term Memory and conditional random field layers on an augmented train set, achieving an F1 score of 0.73. Entity linking is performed via a hybrid approach with dictionaries, generating candidates from a knowledge base containing Unified Medical Language System aliases using the cross-lingual SapBERT and reranking the top candidates using GPT-3.5. The entity linking approach shows consistent results for multiple languages of 0.73 accuracy on the SympTEMIST multilingual dataset and also achieves an accuracy of 0.6123 on the Spanish entity linking task surpassing the current top score for this subtask. Database URL: https://github.com/svassileva/symptemist-multilingual-linking.

Efficiency Optimization of LCL-Resonant Wireless Power Transfer Systems via Bidirectional Electromagnetic–Thermal Coupling Field Dynamics

This paper delved into the thermal dynamics and stability of Wireless Power Transfer (WPT) systems, with a focus on the temperature effects on the coil structure. Using the Finite Element Method (FEM), this study investigated both unidirectional and bidirectional coupling field simulations, assessing their impacts on the transmission efficiency of LCL-resonant WPT systems. The boundary conditions and processes of the electromagnetic–thermal coupling field related to coil loss were analyzed, as well as the dynamic thermal balance in the bidirectional coupling field model. It was found that there is a significant temperature variation across the coil, with the highest temperatures at the central position and the lowest at the edges. This temperature rise notably changed the electrical parameters of the system, leading to variations in its operating state and a reduction in transmission efficiency. A constant coil voltage control strategy was more effective in mitigating the temperature rise compared to a constant coil current strategy, providing valuable insight for enhancing the efficiency and stability of WPT systems.

Analysis of Bidirectional Magnetic Field Modulation on Concentrated Winding Spoke-Type PM Machines

Analysis of Bidirectional Magnetic Field Modulation on Concentrated Winding Spoke-Type PM Machines

Comparative analysis and optimization design of dual-side-permanent-magnet Halbach array vernier machine for high torque density

PurposeThe purpose of this paper is to introduce and analyze a dual-side-permanent-magnet Halbach array vernier (DSPMHV) machine and to propose methods for achieving high torque density.Design/methodology/approachFlux harmonics and torque characteristics are analyzed by using finite element analysis. First, a suitable pole-slot combination is selected by comparison. Second, field modulation processes of DSPMHV machine are analyzed to identify the reason for high torque density. And it is compared with dual-side-PM (DSPM) machine to analyze flux harmonic and verify the flux concentrating effect of the Halbach array.FindingsThe permanent magnet (PM) field of the DSPM machine is approximately equal to the superposition of stator-PM field and rotor-PM field, which is the reason for high torque density. And the Halbach array can reduce flux leakage and increase the amplitude of main flux harmonics, then further improves torque. Improvement of torque can be achieved by choosing right pole-slot combination, adopting DSPM machine structure, reducing flux leakage and adopting field modulation principle.Originality/valueThe DSPMHV machine with split-tooth is proposed in this paper by combining the Halbach array with DSPM structure. This paper analyzes the bidirectional field modulation process, the reason for high torque density of the DSPM machine is obtained. Comparison with the DSPM machine verifies the flux concentrating effect of Halbach array. To alleviate the magnetic saturation in part of stator teeth, this paper proposes an improved DSPMHV machine with shaped auxiliary magnet.

Simultaneous measurement of bidirectional magnetic field and temperature with a dual-channel sensor based on the whispering gallery mode

What we believe is a novel dual-channel whispering gallery mode (WGM) sensor for concurrently measuring bidirectional magnetic field and temperature is proposed and demonstrated. Two sensing microcavities [magnetic fluid (MF)-infiltrated capillary and polydimethylsiloxane (PDMS)-coated microbottle, respectively, referred as Channel 1 (CH1) and Channel 2 (CH2)] are integrated into a silica capillary to facilitate the dual-channel design. Resonant wavelengths corresponding to CH1 and CH2 mainly depend on the change in the magneto-induced refractive index and the change in the thermo-induced parameter (volume and refractive index) of the employed functional materials, respectively. The MF-infiltrated capillary enables bidirectional magnetic field sensing with maximum sensitivities of 46 pm/mT and -3 pm/mT, respectively. The PDMS-coated structure can realize the temperature measurement with a maximum sensitivity of 79.7 pm/°C. The current work possesses the advantage of bidirectionally magnetic tunability besides the temperature response, which is expected to be used in field such as vector magnetic fields and temperature dual-parameter sensing.

Mitigating Joule heating in smart nanochannels: Evaluating the efficacy of AC vs. DC fields

This study delves into the profound implications of different electric field types, namely DC and AC with a sinusoidal waveform, on ion transport within intelligent soft nanochannels, aiming to mitigate Joule heating effects. The investigation considers temperature- and concentration-dependent properties and explores their impact on ionic selectivity, rectification factor, Joule heating, and viscosity dissipation in conical soft nanochannels. Operating in both co-current and counter-current modes, the study accounts for the nanochannel's coating with a dense and strongly negatively charged polyelectrolyte layer, addressing the ionic partitioning effect. Numerically solving the Poisson-Nernst-Planck, Navier-Stokes, and energy equations, the study reveals a significant alignment of Joule heating and viscosity dissipation with ionic current, emphasizing the influence of concentration and temperature ratios. Notably, the bidirectional AC field demonstrates a remarkable 85.51% and 71.72% reduction in Joule heating in co-current and counter-current modes, respectively, compared to DC. These findings highlight the significant impact of the AC field on thermal dynamics within nanochannels. In summary, our study offers essential insights into optimizing nanofluidic systems by effectively balancing ion transport efficiency and thermal effects across various electrical field scenarios. These findings directly address the critical challenge of managing Joule heating in intelligent nanochannels, which is essential for enhancing the performance of lab-on-a-chip devices in sensitive biomedical applications.

Implementing deep learning-based named entity recognition for obtaining narcotics abuse data in Indonesia

<span lang="EN-US">The availability of drug abuse data from the official website of the National Narcotics Board of Indonesia is not up-to-date. Besides, the drug reports from Indonesian National Narcotics Board are only published once a year. This study aims to utilize online news sites as a data source for collecting information about drug abuse in Indonesia. In addition, this study also builds a named entity recognition (NER) model to extract information from news texts. The primary NER model in this study uses the convolutional neural network-long short-term memory (CNNs-LSTM) architecture because it can produce a good performance and only requires a relatively short computation time. Meanwhile, the baseline NER model uses the bidirectional long short-term memory-conditional random field (Bi-LSTMs-CRF) architecture because it is easy to implement using the Flair framework. The primary model that has been built results in a performance (F1 score) of 82.54%. Meanwhile, the baseline model only results in a performance (F1 score) of 69.67%. Then, the raw data extracted by NER is processed to produce the number of drug suspects in Indonesia from 2018-2020. However, the data that has been produced is not as complete as similar data sourced from Indonesian National Narcotics Board publications.</span>

Probing the peripheral self-generated magnetic field distribution in laser-plasma magnetic reconnection with Martin–Puplett interferometer polarimeter

Magnetic reconnection of the self-generated magnetic fields in laser-plasma interaction is an important laboratory method for modeling high-energy density astronomical and astrophysical phenomena. We use the Martin–Puplett interferometer (MPI) polarimeter to probe the peripheral magnetic fields generated in the common magnetic reconnection configuration, two separated coplanar plane targets, in laser-target interaction. We introduce a new method that can obtain polarization information from the interference pattern instead of the sinusoidal function fitting of the intensity. A bidirectional magnetic field is observed from the side view, which is consistent with the magneto-hydro-dynamical (MHD) simulation results of self-generated magnetic field reconnection. We find that the cancellation of reverse magnetic fields after averaging and integration along the observing direction could reduce the magnetic field strength by one to two orders of magnitude. It indicates that imaging resolution can significantly affect the accuracy of measured magnetic field strength.

A six-level ferroelectric storage cell based on a bidirectional imprint field

In this paper, we propose and experimentally demonstrate a six-level ferroelectric cell with six switchable polarization states using a novel fixed-charge method.

Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction.

With the widespread adoption of electronic health records, the amount of stored medical data has been increasing. Clinical data, often in the form of semi-structured or unstructured electronic medical records (EMRs), contains rich patient information. However, due to the use of natural language by physicians when composing these records, the effectiveness of traditional methods such as dictionaries, rule matching, and machine learning in the extraction of information from these unstructured texts falls short of clinical standards. In this paper, a novel deep-learning-based natural language extraction method is proposed to overcome current shortcomings in data governance and Gensini score automatic calculation in coronary angiography. A pre-trained model called bidirectional encoder representation from transformers (BERT) with strong text feature representation capabilities is employed as the feature representation layer. It is combined with bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) models to extract both global and local features from the text. The study included an evaluation of the model on a dataset from a hospital in China and it was compared with another model to validate its practical advantages. Hence, the BiLSTM-CRF model was employed to automatically extract relevant coronary angiogram information from EMR texts. The achieved F1 score was 91.19, which is approximately 0.87 higher than the BERT-BiLSTM-CRF model.

MFE-transformer: Adaptive English text named entity recognition method based on multi-feature extraction and transformer

English text named entity recognition aims to alleviate the problem of insufficient labeling data in the target domain. Existing methods usually use feature representation or model parameter sharing to realize cross-domain transfer of entity recognition capability, but there is still a lack of full utilization of structured knowledge in text sequences. Therefore, this paper proposes an adaptive English named text entity recognition method based on multi-feature extraction and transformer. Firstly, a bidirectional long term memory conditional random field entity recognition model based on BERT pre-trained language model is constructed on a generic domain dataset. In the training process, the weights of two character vectors of text words are dynamically calculated and combined, which makes the model make full use of the information in the character granularity, and the parts-of-speech information and block analysis are added as additional features. The word vectors, character-level features and additional features are spliced into the BiLSTM-CRF neural network model for training. Finally, experiments are carried out on five English datasets and specific cross-domain named entity recognition datasets respectively. The results show that the average performance of the proposed model is improved by 0.43% and 1.47% compared with the current cross-domain model, indicating that the structured knowledge in feature representation can effectively improve the entity recognition capability of the target domain.

Torque Generation Mechanism of Dual-Permanent-Magnet-Excited Vernier Machine by Air-Gap Field Modulation Theory

The dual-permanent-magnet-excited vernier (DPMEV) machine has attracted increasing interest for its high torque density. However, it is difficult to analyze due to the complex structure. In this article, the torque generation mechanism of DPMEV machines is analyzed from the perspective of magnetic field modulation. First, based on air-gap field modulation theory, the stator and rotor modulation factor models as well as armature and permanent magnet magnetomotive force (MMF) models are established, respectively. Considering the bidirectional field modulation effect of the DPMEV machine, the modulation process of MMF is derived. Then, by analyzing the modulated armature and permanent magnetic field, the torque generation mechanism is investigated. In addition, a machine with stator flux gap structure is proposed and the reasons for its torque increase are explained. Finally, a prototype of DPMEV machine is manufactured and tested to verify the effectiveness of the analytical results.

Design and Analysis of a New Separated Type Permanent Magnet Machine Based on Bidirectional Field Modulation Effect

Design and Analysis of a New Separated Type Permanent Magnet Machine Based on Bidirectional Field Modulation Effect

A management knowledge graph approach for critical infrastructure protection: Ontology design, information extraction and relation prediction

Critical Infrastructures (CI) underpin the basic functioning of society and the economy. Proper governance of CI security management remains a crucial challenge. This study aims to construct a knowledge graph for modeling CI protection. While the previous research has focused on threat intelligence modeling and open knowledge bases, they miss considering the defense side. Accordingly, we propose a knowledge graph for critical infrastructure protection, CIPKG, that extends the management ontology to include the defense side. It addresses the cross-industry and cross-time information gaps that occur in the process of CI protection management, making it more comprehensive in structure than the existing knowledge graph. We employ simplified Structured Threat Information Expression as attack ontology and design a new ontology for the defense side, which could combine with the existing threat ontology to form the CI protection knowledge graph. To dynamically extract information from emerging knowledge, we employ a Bi-directional Long Short-Term Memory and Conditional Random Field model with pre-trained cybersecurity domain-specific Bidirectional Encoder Representations from Transformers to recognize the named entities from CI regulations and standards. To associate the threat part with the management portion of the knowledge graph, we adopt the Knowledge Graph Bidirectional Encoder Representations from Transformer model to capture the semantic information and predict the relationship between threat and management. After information extraction and relation prediction, we build a knowledge graph with 529,360 nodes and about 3,335,000 edges.

Anisotropy induced in magnetic field in GNPs/epoxy composites used as an effective heat dissipation electronic packaging material

AbstractThis article investigates the effect of different magnetic fields on modulating the alignment of graphene nanoplatelets (GNPs) and their impact on the thermal conductivity of epoxy nanocomposites. Various nanocomposite specimens were fabricated with different GNP contents by using magnetically field‐aligned GNPs in the fabrication process to enhance the thermal conductivity. The results showed that the in‐plane thermal conductivity of the Bmf (Bidirectional magnetic field) sample with 5 wt% GNPs added reached 0.75 W m−1 K−1, which is 276.6% higher than that of pure epoxy resin and the anisotropy coefficient (K∥/K⊥) reached 3.3. In addition, temperature rise tests were performed to simulate the thermal conductivity of nanocomposites used as an electronic packaging material, and the results indicated that the composites achieved a significant increase in in‐plane thermal conductivity with a lower amount of filler. This provides valuable insights into the preparation of thermally conductive composites and demonstrates their potential as a thermal management packaging material for next‐generation electronic devices.

Heterogeneous lattice hydrodynamic model and jamming transition mixed with connected vehicles and human-driven vehicles

As communication and perception technologies develop, connected vehicles (CVs) advance rapidly. In the process of popularization of CVs, it is bound to lead to the coexistence of CVs and human-driven vehicles (HDVs) on the road network for some time to come, and the previous study assumes that road vehicle is homogeneous, which could not quantify the difference between mixed vehicular flow, based on this, we consider the differences between CAVs and CVs in the way of information acquisition, a heterogeneous lattice hydrodynamics model mixing the HDVs and CVs are presented. Subsequently, by using the small perturbation method, we analyze the linear stability of the proposed model and derive the corresponding stability criteria; when the above stability norm does not hold, in order to investigate the nonlinear phenomenon, nonlinear stability analysis is performed and a modified Korteweg–de Vries equation (mKdV) corresponding to the proposed model is derived. By solving the mKdV equation, we obtain the kink - antikink solitary wave solution which can be used to explain how traffic jams form and propagate. At last, several numerical simulations were conducted to test the impact of the penetration rate of CVs and the bi-directional visual field for both forward-looking and backward-looking on the heterogeneous traffic flow stability, i.e., the first two are positive, while the last is negligible, this conclusion is consistent with the conclusion of theoretical derivation.

Flow Structures and Charge Behaviors of Electroconvective Silicone Flows Induced by a Surface Dielectric Barrier Injection Actuator Subjected to Bipolar Pulsed DC Signals

Surface dielectric barrier injection (SDBI) actuators have received increasing interest due to the presence of dielectric barriers, which can greatly improve the applied voltage. The success of SDBI actuators in driving silicone oil makes silicone flow a very promising application in the field of microfluidics. However, polarity-dependent flows that are different from conventional cases are observed experimentally under bi-polar pulsed DC signals. To interpret this specific phenomenon, a new model of electrohydrodynamic (EHD) wall jet considering both electrochemical injection and the initial presence of an electrical double layer (EDL) at the dielectric solid/liquid interface is developed for the first time based on the finite element method. The results show that the flow field in the simulation is consistent with the experimental bidirectional flow field. The silicone vortex movement is accompanied by charge injection, migration and accumulation in the near-wall region. Through studies of the effect of signal parameters on flow behavior, including voltage amplitude, duty cycle, frequency and waveform, a square wave signal with a frequency of 0.2 Hz proves to be the most efficient in generating a high-velocity silicone flow.

Congruent visual cues speed dynamic motor adaptation

We demonstrate that adaptation to novel dynamics is stronger when additional online visual cues that are congruent with the dynamics are presented during adaptation, compared with either a constant or incongruent visual cue. This effect was found regardless of whether a bidirectional or unidirectional velocity-dependent force field was presented to the participants. We propose that this effect might arise through the inclusion of this additional visual cue information within the state estimation process.

Improving part-of-speech tagging in Amharic language using deep neural network

To date, several POS taggers have been introduced to facilitate the success of semantic analysis for different languages. However, the task of POS tagging becomes a bit intricate in morphologically complex languages, like Amharic. In this paper, we evaluated different models such as bidirectional long short term memory, convolutional neural network in combination with bidirectional long short term memory, and conditional random field for Amharic POS tagging. Various features, both language-dependent and -independent, have been explored in a conditional random field model. Besides, word-level and character-level features are analyzed in deep neural network models. A convolutional neural network is utilized for encoding features at the word and character level. Each model's performance has evaluated on the dataset that contained 321 K tokens and manually tagged with 31 POS tags. Lastly, the best performance obtained by an end-to-end deep neural network model, convolutional neural network in combination with bidirectional long term short memory and conditional random field, is 97.23% accuracy. This is the highest accuracy for Amharic POS tagging task and is competent with contemporary taggers currently existing in different languages.

A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator

The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator.