IEEE transactions on biomedical circuits and systems最新文献

{"title":"A Miniature Batteryless Bioelectronic Implant Using One Magnetoelectric Transducer for Wireless Powering and PWM Backscatter Communication","authors":"Zhanghao Yu;Yiwei Zou;Huan-Cheng Liao;Fatima Alrashdan;Ziyuan Wen;Joshua E. Woods;Wei Wang;Jacob T. Robinson;Kaiyuan Yang","doi":"10.1109/TBCAS.2024.3468374","DOIUrl":"10.1109/TBCAS.2024.3468374","url":null,"abstract":"Wireless minimally invasive bioelectronic implants enable a wide range of applications in healthcare, medicine, and scientific research. Magnetoelectric (ME) wireless power transfer (WPT) has emerged as a promising approach for powering miniature bio-implants because of its remarkable efficiency, safety limit, and misalignment tolerance. However, achieving low-power and high-quality uplink communication using ME remains a challenge. This paper presents a pulse-width modulated (PWM) ME backscatter uplink communication enabled by a switched-capacitor energy extraction (SCEE) technique. The SCEE rapidly extracts and dissipates the kinetic energy within the ME transducer during its ringdown period, enabling time-domain PWM in ME backscatter. Various circuit techniques are presented to realize SCEE with low power consumption. This paper also describes the high-order modeling of ME transducers to facilitate the design and analysis, which shows good matching with measurement. Our prototyping system includes a millimeter-scale ME implant with a fully integrated system-on-chip (SoC) and a portable transceiver for power transfer and bidirectional communication. SCEE is proven to induce \u0000<inline-formula><tex-math>$&gt;$</tex-math></inline-formula>\u0000 50% amplitude reduction within 2 ME cycles, leading to a PWM ME backscatter uplink with 17.73 kbps data rate and 0.9 pJ/bit efficiency. It also achieves 8.5\u0000<inline-formula><tex-math>$times$</tex-math></inline-formula>\u000010\u0000^-5\u0000 bit-error-rate (BER) at a 5 cm distance, using a lightweight multi-layer-perception (MLP) decoding algorithm. Finally, the system demonstrates continuous wireless neural local-field potential (LFP) recording in an \u0000<italic>in vitro</i>\u0000 setup.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 6","pages":"1197-1208"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACE: Automated Optimization Towards Iterative Classification in Edge Health Monitors","authors":"Yuxuan Wang;Lara Orlandic;Simone Machetti;Giovanni Ansaloni;David Atienza","doi":"10.1109/TBCAS.2024.3468160","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3468160","url":null,"abstract":"Wearable devices for health monitoring are essential for tracking individuals’ health status and facilitating early detection of diseases. However, processing biomedical signals online for real-time monitoring is challenging due to limited computational resources on edge devices. To address this challenge, we propose an application-agnostic methodology called ACE (Automated optimization towards classification on the Edge). ACE converts a health monitoring algorithm with feature extraction and classification into an iterative detection process, incorporating algorithms of varying complexities and minimizing re-computation of shared data. First, ACE decomposes a monolithic model, employing a single feature set and classifier, into multiple algorithms with different computational complexities. Then, our automatic analysis tool integrates buffering logic into these algorithms to prevent re-computation of shared computational-intensive data. The optimized algorithm is then converted into a low-level language in C for deployment. During runtime, the system initiates monitoring with the lowest complexity algorithm and iteratively involves algorithms with higher complexity without recomputing the existing data. The iteration process continues until a pre-defined confidence threshold is met. We demonstrate the effectiveness of ACE on two biomedical applications: seizure detection and emotional state classification. ACE achieves at least 28.9% and 18.9% runtime savings without any accuracy loss on a Cortex-A9 edge platform for the two benchmarks, respectively. We discuss and demonstrate how ACE can be used by designers of such biomedical algorithms to automatically optimize and deploy their applications on the edge.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"82-92"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Bit ECRAM-Based Analog Neuromorphic System With High-Precision Current Readout Achieving 97.3% Inference Accuracy","authors":"Minseong Um;Minil Kang;Kyeongho Eom;Hyunjeong Kwak;Kyungmi Noh;Jimin Lee;Jeonghoon Son;Jiseok Kwon;Seyoung Kim;Hyung-Min Lee","doi":"10.1109/TBCAS.2024.3465610","DOIUrl":"10.1109/TBCAS.2024.3465610","url":null,"abstract":"This article proposes an analog neuromorphic system that enhances symmetry, linearity, and endurance by using a high-precision current readout circuit for multi-bit nonvolatile electro-chemical random-access memory (ECRAM). For on-chip training and inference, the system uses activation modules and matrix processing units to manage analog update/read paths and perform precise output sensing with feedback-based current scaling on the ECRAM array. The 250nm CMOS neuromorphic chip was tested with a 32 × 32 ECRAM synaptic array, achieving linear and symmetric updates and accurate read operations. The proposed circuit system updates the 32 × 32 ECRAM across 100 levels, maintaining consistent synaptic weights, and operates with an output error rate of up to 2.59% per column. It consumes 5.9 mW of power excluding the ECRAM array and achieves 97.3% inference accuracy on the MNIST dataset, close to the software-confirmed 97.78%, with only the final layer (64 × 10) mapped to the ECRAM.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"590-604"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 6-9 GHz 1.28 Gbps 76 mW Amplitude and Synchronized Time Shift Keying IR-UWB CMOS Transceiver for Brain Computer Interfaces","authors":"Geunhaeng Lee;Junyoung Jang;Kyoungseok Song;Tae Wook Kim","doi":"10.1109/TBCAS.2024.3465533","DOIUrl":"10.1109/TBCAS.2024.3465533","url":null,"abstract":"This paper proposes a low-power, high-speed impulse radio-ultra-wideband (IR-UWB) transceiver for brain computer interfaces (BCIs) using amplitude and synchronized time shift keying technique (ASTSK). The proposed IR-UWB transmitter (Tx) generates two pulses (sync pulse and data pulse) per symbol rate. The time difference between two pulses is used for synchronized time shift keying and the amplitude of the two pulses is used for amplitude shift keying. The receiver (Rx) demodulates the time difference with a low power time-to-digital converter (TDC) and peak detector (PD) based amplitude demodulation is suggested to relax analog-to-digital converter (ADC) burden for low power receiver. Especially the Tx-based synchronized operation eliminates the need for complex clock circuitry such as phase-lock loop (PLL) and reference crystal oscillator. Therefore, it can achieve low power and high-speed operation. The prototype, fabricated in 65 nm CMOS, has a frequency range of 6-9 GHz, communication speed of 1.28 Gbps, and power consumption of 18 mW (Tx) and 58 mW (Rx). This work is a fully integrated RF transceiver adapted for high-order modulation and designed to include the receiver.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"605-615"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking of Wrist and Hand Kinematics With Ultra Low Power Wearable A-Mode Ultrasound","authors":"Giusy Spacone;Sergei Vostrikov;Victor Kartsch;Simone Benatti;Luca Benini;Andrea Cossettini","doi":"10.1109/TBCAS.2024.3465239","DOIUrl":"10.1109/TBCAS.2024.3465239","url":null,"abstract":"Ultrasound-based Hand Gesture Recognition has gained significant attention in recent years. While static gesture recognition has been extensively explored, only a few works have tackled the task of movement regression for real-time tracking, despite its importance for the development of natural and smooth interaction strategies. In this paper, we demonstrate the regression of 3 hand-wrist Degrees of Freedom (DoFs) using a lightweight, A-mode-based, truly wearable US armband featuring four transducers and WULPUS, an ultra-low-power acquisition device. We collect US data, synchronized with an optical motion capture system to establish a ground truth, from 5 subjects. We achieve state-of-the-art performance with an average root-mean-squared-error (RMSE) of <inline-formula><tex-math>$7.32^{circ}$</tex-math></inline-formula> <inline-formula><tex-math>$pm$</tex-math></inline-formula> <inline-formula><tex-math>$1.97^{circ}$</tex-math></inline-formula> and mean-absolute-error (MAE) of <inline-formula><tex-math>$5.31^{circ}$</tex-math></inline-formula> <inline-formula><tex-math>$pm$</tex-math></inline-formula> <inline-formula><tex-math>$1.42^{circ}$</tex-math></inline-formula>. Additionally, we demonstrate, for the first time, robustness with respect to transducer repositioning between acquisition sessions, achieving an average RMSE value of <inline-formula><tex-math>$11.11^{circ}$</tex-math></inline-formula> <inline-formula><tex-math>$pm$</tex-math></inline-formula> <inline-formula><tex-math>$4.14^{circ}$</tex-math></inline-formula> and a MAE of <inline-formula><tex-math>$8.46^{circ}$</tex-math></inline-formula> <inline-formula><tex-math>$pm$</tex-math></inline-formula> <inline-formula><tex-math>$3.58^{circ}$</tex-math></inline-formula>. Finally, we deploy our pipeline on a real-time low-power microcontroller, showcasing the first instance of multi-DoF regression based on A-mode US data on an embedded device, with a power consumption lower than <inline-formula><tex-math>$30 mathrm{mW}$</tex-math></inline-formula> and end-to-end latency of <inline-formula><tex-math>$approx$</tex-math></inline-formula> <inline-formula><tex-math>$80 mathrm{ms}$</tex-math></inline-formula>.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"536-548"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Cyto-Silicon Hybrid System with On-Chip Closed-Loop Modulation","authors":"Jun Wang;Seok Joo Kim;Wenxuan Wu;Jongha Lee;Henry Hinton;Rona S. Gertner;Han Sae Jung;Hongkun Park;Donhee Ham","doi":"10.1109/TBCAS.2024.3466549","DOIUrl":"10.1109/TBCAS.2024.3466549","url":null,"abstract":"We introduce a bioelectronic interface between biological electrogenic cells and a mixed-signal CMOS integrated circuit with an array of surface electrodes, where not only is the CMOS electrode array capable of electrophysiological recording and stimulation of the cells with 1,024 recording and stimulation channels, but it can also provide low-latency artificial signal pathways from cells it records to cells it stimulates. This on-chip closed-loop modulation has an intrinsic latency less than 5 µs. To demonstrate the utility of the on-chip closed loop modulation as an artificial feedback pathway between biological cells, we develop a silicon-cardiomyocyte self-sustained oscillator with a tunable frequency to which both the relevant part of the CMOS chip and cells are locked, and also a silicon-neuron interface with a silicon inhibitory connection between neuronal cells. This line of cyto-silicon hybrid system, where the boundary between biological and semiconductor systems is blurred, may find applications in prosthesis, brain-machine interface, and fundamental biology research.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"577-589"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Implementation of Integrated Dual-Mode Pulse and Continuous-Wave Electron Paramagnetic Resonance Spectrometers","authors":"Jui-Hung Sun;Difei Wu;Peter Qin;Constantine Sideris","doi":"10.1109/TBCAS.2024.3465210","DOIUrl":"10.1109/TBCAS.2024.3465210","url":null,"abstract":"Electron paramagnetic resonance (EPR) is a powerful spectroscopic technique that allows direct detection and characterization of radicals containing unpaired electron(s). The development of portable, low-power EPR sensing modalities has the potential to significantly expand the utility of EPR in a broad range of fields, ranging from basic science to practical applications such as point-of-care diagnostics. The two major methodologies of EPR are continuous-wave (CW) EPR, where the frequency or field is swept with a constant excitation, and pulse EPR, where short pulses induce a transient signal. In this work, we present the first realization of a fully integrated pulse EPR spectrometer on-chip. The spectrometer utilizes a subharmonic direct-conversion architecture that enables an on-chip oscillator to be used as a dual-mode EPR sensing cell, capable of both CW and pulse-mode operation. An on-chip reference oscillator is used to injection-lock the sensor to form pulses and also to downconvert the pulse EPR signal. A proof-of-concept spectrometer IC with two independent sensing cells is presented, which achieves a pulse sensitivity of \u0000<inline-formula><tex-math>$4.6times 10^{9}$</tex-math></inline-formula>\u0000 spins (1000 averages) and a CW sensitivity of \u0000<inline-formula><tex-math>$2.9times 10^{9}$</tex-math></inline-formula>\u0000 spins/\u0000<inline-formula><tex-math>$sqrt{text{Hz}}$</tex-math></inline-formula>\u0000 and can be powered and controlled via a computer USB interface. The sensing cells consume as little as 2.1mW (CW mode), and the system is tunable over a wide frequency range of 12.8–14.9GHz (CW/pulse). Single-pulse free induction decay (FID), two-pulse inversion recovery, two-pulse Hahn echo, three-pulse stimulated echo, and CW experiments demonstrate the viability of the spectrometer for use in portable EPR sensing.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 6","pages":"1209-1219"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System With In-Pixel Digitization and Crosstalk Compensation","authors":"Xinguo Wang;Songyu Han;Peng Yan;Yang Lin;Chen Wang;Lei Qian;Pujia Xing;Yue Cao;Xinglei Song;Guoxing Wang;Timothy G. Constandinou;Yan Liu","doi":"10.1109/TBCAS.2024.3460388","DOIUrl":"10.1109/TBCAS.2024.3460388","url":null,"abstract":"Simultaneous electrophysiological and chemical recording allows for multi-modal neural instrumentation and provides insights into chemical synapses and ion channels across the cell membrane. However, inter-modal interference can hinder highly synchronized recording in large-scale systems with high temporal and spatial resolution. In this work, we propose a 1024-channel lab-on-CMOS system for dual-modal neural recording with in-pixel digitization and interference suppression. A foreground calibration scheme with tunable capacitance is implemented in-pixel to compensate for the crosstalk between electrical and chemical recording. Active pixels for both electrical and chemical modalities are designed based on a pulse width modulation (PWM) analog-to-digital conversion scheme. CMOS-compatible post-processing is implemented to realize in-pixel electrodes and chemical sensing membranes. The prototype, implemented in a 180 nm CMOS technology, occupies a total area of 33 mm² with 1024 pixels, and each unit pixel includes one electrical recording site and two chemical recording sites, with dimensions of 150 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m <inline-formula><tex-math>$times$</tex-math></inline-formula> 130 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m. The total system power consumption is 19.68 mW at a frame rate of 9k and 3k for electrical and chemical imaging respectively. The <italic>in-vitro</i> experiment demonstrated the concurrent high density electrophysilogical and electrochemical recording with sub millisecond temporal resolution.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"549-561"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 13.56-MHz 93.5%-Efficiency Optimal On/Off Timing Tracking Active Rectifier With Digital Feedback-Based Adaptive Delay Control","authors":"Jisan Ahn;Hyun-Su Lee;Kyeongho Eom;Woojoong Jung;Hyung-Min Lee","doi":"10.1109/TBCAS.2024.3457848","DOIUrl":"10.1109/TBCAS.2024.3457848","url":null,"abstract":"This paper presents an adaptive active rectifier with digital feedback delay controllers (DFDC) which quickly tracks optimal on/off timing against input voltage and load variations. To efficiently generate the on/off transition, the proposed active rectifier adopts dynamically controlled coarse/fine delay lines rather than using conventional power-hungry static comparators, while removing the risk of unwanted multiple driving pulses to pass transistors. DFDC conducts the dual-loop digital feedback to independently adjust on/off timing with high-speed 13.56-MHz loop bandwidth, improving the voltage conversion ratio (VCR) and power conversion efficiency (PCE). DFDC can enable real-time power-saving mode control that automatically masks clock-toggling to non-essential blocks to minimize dynamic power loss while driving power transistors. To validate the efficacy of the proposed adaptive rectifier during digital feedback and settling procedures, experiments were carried out with 0.25 μm CMOS prototype at the carrier frequency of 13.56-MHz, input voltages between 1.7 and 2.6 V, and load ranges from 0.33 to 2.2 kΩ. The proposed active rectifier employing DFDC achieves a peak PCE of 93.5% and the peak VCR of 96.3% at the output power of 12.52 mW and 2.02 mW, respectively.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"562-576"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closed-Loop Implantable Neurostimulators for Individualized Treatment of Intractable Epilepsy: A Review of Recent Developments, Ongoing Challenges, and Future Opportunities","authors":"Hossein Kassiri;Abdul Muneeb;Rojin Salahi;Alireza Dabbaghian","doi":"10.1109/TBCAS.2024.3456825","DOIUrl":"10.1109/TBCAS.2024.3456825","url":null,"abstract":"Driven by its proven therapeutic efficacy in treating movement disorders and psychiatric conditions, neurostimulation has emerged as a promising intervention for intractable epilepsy. Researchers envision an advanced implantable device capable of long-term neuronal monitoring, high spatio-temporal resolution data processing, and timely responsive neurostimulation upon seizure detection. However, the stringent energy constraints of implantable devices and significant inter-patient variability in neural activity pose substantial challenges and opportunities for biomedical circuits and systems researchers. For seizure detection, various ASIC solutions employing both deterministic and data-driven algorithms have been developed. These solutions leverage a subset of numerous signal features (spanning time and frequency domains) and classifiers (such as SVMs, DNNs, SNNs) to achieve notable success in terms of detection accuracy, latency, and energy efficiency. Implementations vary widely in computational approaches (digital, mixed-signal, analog, spike-based), training strategies (online versus offline), and application targets (patient-specific versus cross-patient). In terms of treatment, recent efforts have focused on the personalization of stimulation waveforms to enhance therapeutic efficacy. This personalization faces complex challenges, including a limited understanding of how stimulation parameters influence neuronal activity, the lack of a comprehensive brain model to capture its intricate electrochemical dynamics, and recording neural signals in the presence of stimulation artifacts. This review provides a comprehensive overview of the field, detailing the foundational principles, recent advancements, and ongoing challenges in enhancing the diagnostic accuracy, treatment efficacy, and energy efficiency of implantable patient-optimized neurostimulators. We also discuss potential future directions, emphasizing the need for standardized performance metrics, advanced computational models, and adaptive stimulation protocols to realize the full potential of this transformative technology.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 6","pages":"1268-1295"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}