Real-time Signal Research Articles

SDN-controlled multi-user NOMA–OFDM VLC system based on a resource allocation algorithm

We propose a novel, to our knowledge, resource allocation algorithm (RAA) for a multi-user non-orthogonal multiple access orthogonal frequency division multiplexing (NOMA–OFDM) visible light communication (VLC) system. The proposed algorithm considers both the interference between users and their channel diversity. Bit allocation to users in each iteration is based on the ratio between the already allocated number of bits and the demanded number of bits of users, rather than their channel gains. In this way, the inherent issue of prioritizing strong users in a conventional RAA is avoided. We implement a software-defined-network (SDN)-controlled VLC system with real-time signal generation to validate the proposed RAA. The allocation results in the SDN platform are used to configure a FPGA-based transmitter. Experiments of a two-user NOMA–OFDM system with 115–191 Mbit/s throughput show that the system can dynamically react to the change of data demand and channel responses of users. We further demonstrate that the proposed algorithm outperforms conventional RAAs regardless of the data demand, receiving angles, and distances of users.

Smart Park: Innovating the Future of Vehicle Parking

Effective parking solutions become increasingly important as urban areas increase because of wasted space, time-consuming searches, and traffic congestion. By utilizing cutting edge technologies to improve parking management and user experience, the Smart Vehicle Parking System (SVPS) tackles these issues. To deliver dynamic solutions, SVPS combines sophisticated algorithms, sensor networks, and real-time data. Parking space sensors track occupancy and transmit data to a central system, where machine learning maximizes space distribution and directs vehicles to open spaces. This smart parking system is software and hardware based. Different algorithms are used to find vacant slots using machine learning and deep learning. IR sensors, RFID, Servomotors and ESP 32 microcontrollers and Internet of things are useful to find vacant slots in real time signal processing. In traditional method it is difficult to search free space, and this method is time consuming, Smart parking system overcome this problem with new technology and improve parking management system. The SVPS not only improves urban mobility but also contributes to reducing vehicle emissions and enhanced overall efficiency. Keywords— smart parking, integration, secure payment,advanced technologies, convenient.

A Novel Filter Bank and Fourier Transform Convolutional Neural Network for SSVEP Classification

Abstract. Classifying Steady-State Visual Evoked Potentials (SSVEPs) is crucial for enhancing the performance of Brain-Computer Interface (BCI) systems. This study introduces a new method for SSVEP classification within BCI frameworks, called the Filter Bank and Fourier Transform Convolutional Neural Network (FBFCNN). The FBFCNN model effectively extracts key harmonic features by first segmenting SSVEP signals into multiple sub-bands through a filter bank. Subsequently, the Fast Fourier Transform (FFT) is applied to convert these signals from the time domain into the frequency domain. The generated spectrum's real and imaginary components are then fed into a convolutional neural network (CNN). This method improves SSVEP feature extraction and increases classification accuracy by fusing the advantages of CNNs with filter banks. Experimental results, derived from publicly available SSVEP datasets, show that the FBFCNN model surpasses traditional techniques in both accuracy and Information Transfer Rate (ITR). Offering a reliable and efficient solution for real-time brain signal decoding, the FBFCNN model represents a significant advancement in SSVEP-based BCI systems.

Proposed Explainable Interference Control Technique in 6G Networks Using Large Language Models (LLMs)

After the advent of 5th generation (5G) and 6th generation (6G) cellular networks, the complexity of managing real-time signal interference has increased in dense and dynamic environments. Traditional interference techniques, such as frequency reuse and allocation, while effective, lack robust adaptability and transparency needed to reduce interference in advanced communication networks. This paper introduces a novel approach that fuses large language models (LLMs) and Explainable Artificial Intelligence (XAI) to mitigate interference and enhance interference management in the mathematical foundations of 6G networks. The proposed approach provides accurate interference predictions, which the LLM balances with its complex architecture, necessary to meet the demands of beyond 5G and 6G networks, along with interpretable explanations to ensure transparency in decision-making. The proposed framework has been evaluated across various performance metrics. Interference latency consistently achieves lower rates of 0.95 s, compared to traditional techniques, which average around 1 s. Furthermore, the confidence score of the LLM shows a stable value of 0.87 throughout the system, compared to 0.85 in techniques without LLMs. Overall, the XAI-driven LLM demonstrates the potential of incorporating LLMs and XAI into wireless networks to improve resilience in next-generation networks. This proof of concept introduces a novel framework that offers new dimensions in wireless communication, particularly for interference management, prediction, and mitigation.

Ultraflexible Vertical Corbino Organic Electrochemical Transistors for Epidermal Signal Monitoring.

Skin-conformal organic electrochemical transistors (OECTs) have attracted significant attention for real-time physiological signal monitoring and are vital for health diagnostics and treatments. However, mechanical harmonization amid the inherent dynamic nature of the skin surface and the acquisition of intrinsic physiological signals are significant challenges that hinder the integration of the ultimate skin interface. Thus, this study proposes a novel 4-terminal (4-T) vertical Corbino OECT, exhibiting high transconductance (>400 mS) and offering remarkable resilience and operational stability at an extremely low voltage of 10mV (1.9% of minimal current change after 104 biasing cycles and endurance up to 103 cycles of repetitive deformation with a 5µm bending radius). Consequently, ultralow-power, motion-resistant epidermal electrocardiogram, electromyogram, and electrooculogram sensors are developed with an exceptional signal-to-noise ratio of 40.1dB. The results of this study present a significant stride in non-invasive, skin-interfaced health-monitoring technologies and herald a new era in integrative health technologies.

MKIDGen3: Energy-resolving, single-photon-counting microwave kinetic inductance detector readout on a radio frequency system-on-chip.

Microwave Kinetic Inductance Detectors (MKIDs) are superconducting detectors capable of single-photon counting with energy resolution across the ultraviolet, optical, and infrared (UVOIR) spectrum with microsecond timing precision. MKIDs are also multiplexable, providing a feasible way to create large-format, cryogenic arrays for sensitive imaging applications in biology, astronomy, and quantum information. Building large, cryogenic MKID arrays requires processing highly multiplexed, wideband readout signals in real time; this task has previously required large, heavy, and power-intensive custom electronics. In this work, we present the third-generation UVOIR MKID readout system (Gen3), which is capable of reading out twice as many detectors with an order of magnitude lower power, weight, volume, and cost-per-pixel as compared to the previous system. Gen3 leverages the Xilinx RFSoC4x2 platform to read out 2048, 1MHz MKID channels per board. The system takes a modern approach to FPGA design using Vitis High-Level Synthesis to specify signal processing blocks in C/C++, Vivado ML intelligent design runs to inform implementation strategy and close timing, and Python productivity for Zynq to simplify interacting with and programming the FPGA using Python. This design suite and tool flow allows general users to contribute to and maintain the design and positions Gen3 to rapidly migrate to future platforms as they become available. In this work, we describe the system requirements, design, and implementation. We also provide performance characterization details and show that the system achieves detector-limited resolving power in the case of few readout tones and minimal degradation with all 2048 tones. Planned upgrades and future work are also discussed. The Gen3 MKID readout system is fully open-source and is expected to facilitate future array scaling to megapixel-sized formats and increase the feasibility of deploying UVOIR MKIDs in space.

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

A Proof-of-Concept Open-Source Platform for Neural Signal Modulation and Its Applications in IoT and Cyber-Physical Systems

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages.

Galectin-3 zebrafish model: identification and pharmacological modulation of arrhythmogenic cardiomyopathy-related phenotypes

Abstract Background Arrhythmogenic Cardiomyopathy (ACM) is rare hereditary cardiac condition characterized by the substitution of ventricular myocardium with fibro-fatty tissue leading to high risk of life-threatening arrhythmias and sudden cardiac death, particularly among young individuals and athletes. Based on genotype-phenotype correlation, the spectrum of the ACM phenotype was shown to be broader than previously believed. Approximately 40% of ACM patients carry one or more putative pathogenic variants in genes encoding desmosomal proteins. Recently LGALS3 gene involvement in ACM early pathogenesis has been advocated. Aim To achieve a comprehensive understanding of LGALS3 role in the pathogenesis of AC. Methods This stable mutant line was generated by CRISPR/Cas9 strategy and then deeply characterized. Phenotyping included heart activity characterization using a recently published software specific for zebrafish, the pyHeart4Fish. Gene expression analysis was carried out by using RNAseq, Real Time PCR and signaling pathway reporter zebrafish lines. Immunofluorescence analysis were performed using and antibody against L-plastin, a leukocyte marker for the detection of inflammatory cells and Acridine Orange/Ethidium Bromide staining to check the possible presence of apoptotic and necrotic events. Results Morphological investigation revealed notable structural abnormalities such as pericardial effusion and/or hemopericardium. The heart activity analysis detected a diminished ventricle contractility and ejection fraction, with bradycardia and occurrence of arrhythmia events. Rna analysis highlighted the dysregulation of Wnt/β-catenin signaling pathway. Moreover, the L-plastin inflammatory marker revealed a significant infiltration of inflammatory cells in the heart as well as an elevated number of apoptotic and necrotic cells (figure 1). The pharmacological treatment, partially rescued a subset of AC-related cardiac phenotypes observed, including heart contractility, bradycardia and arrhythmia. Conclusion Our findings strongly validate our lgals3a zebrafish mutant as an effective model to elucidate its role in ACM related phenotype. This model accurately replicates the cardiac abnormalities and dysregulation in cell signaling that are characteristic of ACM. Further, observed cell death and inflammation and the partial recovery of cardiac phenotypes through pharmacological intervention, underscores the potential of targeting the Wnt/β-catenin signaling pathway as future perspective for ACM.Figure 1

Improving the accuracy of dynamic inclination measurement by machine learning

With the diminishing availability of oil and gas resources, the Rotary Steerable System has become increasingly important. However, the vibrations and shocks during the drilling process pose challenges to the Measurement While Drilling. In recent years, the application of machine learning in the field of petroleum exploration has gradually expanded, especially in the estimation of geological parameters and lithology discrimination. However, there is still limited research on drilling tool attitude measurement. To address the above-mentioned issues, this study proposes a method that combines drilling tool attitude sensor data with artificial neural networks to improve the accuracy of dynamic inclination measurement. This method utilizes machine learning techniques, combining real-time z-axis acceleration signals and magnetic induction signals, and employs a deep learning model to invert the x and y-axis acceleration signals, thereby achieving high-precision measurement of dynamic inclination angles. Experimental results show that Long Short-Term Memory model, under simulated measurement conditions with different rotational speeds, yields dynamic inclination curve errors ranging from 0.4° to 0.7°, significantly reducing the errors compared to the original measurements. This method not only improves the accuracy of inclination angle measurement but also demonstrates strong adaptability to different rotational speeds, providing more accurate data support for drilling operations.

Otomatisasi Penyemprotan Polyester Menggunakan Kawasaki Cobot dengan Human Machine Interface (HMI) Berbasis Web

Brago Luchttechniek BV is a company specializing in the production of PIR (Polyisocyanurate) air ducts, facing challenges in maintaining accuracy and consistency in the polyester insulation spraying process on its products using Kawasaki collaborative robots (cobot) for automation. To address these challenges, this study designed a web-based Human Machine Interface (HMI) to automate the polyester insulation spraying process on PIR air ducts using the Kawasaki Cobot. A web-based HMI was chosen for its ease of development, flexibility, and integration capabilities with modern technology. This HMI uses the TCP/IP protocol to send real-time command signals to automation devices such as the Kawasaki Cobot and devices connected to the Programmable Logic Controller (PLC). Testing results demonstrated an improvement in the spraying process efficiency, reducing the time from 15–18 minutes conventionally to 5–7 minutes with the web-based HMI automation. Additionally, the web-based HMI successfully communicated with other devices connected to the robot, such as the conveyor belt and rotating table, via TCP/IP protocols, with each device having its own ID and IP address to facilitate linear message delivery, prevent collisions, and minimize errors. After eight successful automation tests on four different products, the system operated smoothly and systematically through the available HMI controls. The system was also evaluated by four respondents, with an overall score of 4.33 out of 5, indicating a high level of satisfaction.

Spatiotemporal Information, Near-Field Perception, and Service for Tourists by Distributed Camera and BeiDou Positioning System in Mountainous Scenic Areas

The collaborative use of camera near-field sensors for monitoring the number and status of tourists is a crucial aspect of smart scenic spot management. This paper proposes a near-field perception technical system that achieves dynamic and accurate detection of tourist targets in mountainous scenic areas, addressing the challenges of real-time passive perception and safety management of tourists. The technical framework involves the following steps: Firstly, real-time video stream signals are collected from multiple cameras to create a distributed perception network. Then, the YOLOX network model is enhanced with the CBAM module and ASFF method to improve the dynamic recognition of preliminary tourist targets in complex scenes. Additionally, the BYTE target dynamic tracking algorithm is employed to address the issue of target occlusion in mountainous scenic areas, thereby enhancing the accuracy of model detection. Finally, the video target monocular spatial positioning algorithm is utilized to determine the actual geographic location of tourists based on the image coordinates. The algorithm was deployed in the Tianmeng Scenic Area of Yimeng Mountain in Shandong Province, and the results demonstrate that this technical system effectively assists in accurately perceiving and spatially positioning tourists in mountainous scenic spots. The system demonstrates an overall accuracy in tourist perception of over 90%, with spatial positioning errors less than 1.0 m and a root mean square error (RMSE) of less than 1.14. This provides auxiliary technical support and effective data support for passive real-time dynamic precise perception and safety management of regional tourist targets in mountainous scenic areas with no/weak satellite navigation signals.

Bias-controlled modulation for monolithic III-nitride optoelectronic integration.

III-nitride multi-quantum well (MQW) diodes can modulate the light emitted by another diode with the same MQW structure by varying the bias voltage owing to the spectral overlap between the electroluminescence spectrum and spectral responsivity curve of the MQW diodes. Here, we investigate bias-controlled modulation by monolithically integrating an optical transmitter, waveguide, electro-absorption modulator (EAM), and slot grating coupler on a silicon-based III-nitride platform using compatible fabrication processes. The modulated light is coupled into a fiber, which is direct to a photodiode for characterization. The bandwidths of forward-biased emission modulation and reverse-biased absorption modulation are of the same order of magnitude, with the latter exhibiting significant performance improvements. In addition, real-time video signal transmission was achieved using an EAM, which provides a meaningful reference for modulation applications of silicon-based GaN optoelectronic integrated systems.

Thyroid Radiofrequency Ablation-Thermal Effects on Recurrent Laryngeal Nerve Using Continuous Intraoperative Neuromonitoring Animal Model.

When performing radiofrequency ablation for thyroid nodules, it is essential to avoid thermal injury to the recurrent laryngeal nerve. This porcine animal model study used continuous intraoperative neuromonitoring to investigate the thermal safety parameters of thyroid radiofrequency ablation. Porcine animal study. University animal laboratory. Twelve piglets were tested at different radiofrequency power levels, and the real-time electromyography signal changes were recorded under continuous intraoperative neuromonitoring. The spread heat study (8 piglets) included spontaneous recovery tests and cold water irrigation tests to investigate the safety distance from the recurrent laryngeal nerve to the active tip during 5-second activation with standard stimulation patterns. The residual heat study (4 piglets) investigated the safety cooling durations by touching the recurrent laryngeal nerve with the tip after a 5-second activation. In the spread heat study, substantial signal attenuation events were observed at an spread heat distance of 2, 3, 5, and 5 mm when the power was set as 10, 20, 30, and 50 W, respectively. No signal recovery could be observed in 20 minutes with or without cold water irrigation in the injured recurrent laryngeal nerve area. The residual heat study shows the residual thermal effect of the tip is minimal, and no substantial signal attenuation event was observed at all experiments. This innovative study established the thermal safety parameters for radiofrequency ablation in a porcine model at various power levels, which can potentially assist operators in delineating a precise ablation field and providing effective thyroid ablation treatment safely.

A mental health monitoring system based on intelligent algorithms and biosensors: Algorithm behaviour analysis and intervention strategies

Introduction: Mental health monitoring encompasses the systematic observation and assessment of an individual’s psychological well-being, aiming to detect, understand and manage mental health conditions. It involves various techniques and interventions tailored to support individuals in achieving and maintaining optimal mental wellness. Recent advancements include the use of biosensors and biomechanics to analyze physiological signals that correlate with mental states, providing a more comprehensive understanding of psychological well-being. Aim: The aim of this research is to construct an innovative mental health monitoring system established on intelligent algorithms and biomechanical data through behaviour analysis and intervention strategies. Methodology: We propose a novel Snow Ablation-driven Bi-directional Fine-tuned Recurrent Neural Network (SA-BFRNN) to identify the state of mental health. In addition to behavioural data, biosensors are employed to collect real-time physiological signals such as heart rate variability, skin conductance, and muscle tension, offering objective markers of mental stress and anxiety. These biomechanical inputs are integrated into the system for multi-modal analysis. We employ the SA algorithm, iteratively removing less influential connections and nodes based on their impact on model performance. This process enhances network efficiency and generalization capabilities, refining the BFRNN for mental health state identification. Utilizing a questionnaire with 25 questions, administered to a selected group of 756 individuals, we validate our proposed model. Biosensor data is synchronized with questionnaire responses to improve the precision of mental state identification. Clustering-derived labels are validated with mean opinion score. These labels inform classifiers for individual mental health prediction, aligning with our objective of robust mental health assessment through data-driven approaches. SA-BFRNN integrates both forward and backward temporal information, enhancing its ability to discern subtle patterns in behaviour. Through iterative fine-tuning, our network learns to adapt to diverse datasets, enabling precise identification of mental health states. Research findings: In the result evaluation phase, we thoroughly examine how well our proposed SA-BFRNN model recognizes various states of mental health across different parameters. Our findings also highlight the significance of incorporating biomechanics, where biosensor data showed a strong correlation with mental health indicators, thereby augmenting the accuracy of the system. Our findings emphasize the efficacy of the SA-BFRNN technique, as demonstrated by its overall performance in terms of recall (92.56%), accuracy (90.13%), F1-score (88.16%) and precision (89.23%). Our experimental results unequivocally demonstrate that our proposed model performed better than other traditional approaches in classifying contents from multimodal data, showing notable enhancements in accuracy and robustness, particularly under dynamic conditions.

A comparison of the acoustic waves generated in proton and carbon ion therapy

Hadron therapy, which employs particles such as protons and carbon-ions, is a promising method of cancer treatment due to its unique ability to deliver maximum energy at the Bragg peak near the tumor, sparing surrounding healthy tissue. Ionoacoustic waves, generated by thermal expansion from electronic collisions and localized heating, can be detected to optimize dose delivery and verify particle range, thus improving treatment precision. These waves offer a unique opportunity for comparative studies of different particle therapies. In this study, a mathematical model and computational simulations are used to compare the characteristics of ionoacoustic waves generated in tissue by proton and carbon-ion beams. In particular, we assess the impact of the nuclear fragmentation tail on the ionoacoustic signals generated in carbon-ion therapy. Our approach will allow us to make some important observations to study the comparative effects of proton and carbon-ion therapy. The aim of this work is to perform a comprehensive comparative analysis of ionoacoustic waves from proton and carbon-ion treatments, focusing on their potential for in-vivo range verification. This research addresses the current gap in understanding the use of ionoacoustic signals for range verification in ion beam therapy, which is critical given the growing clinical application of carbon ion therapy and its under-explored acoustic properties. This study pioneers the feasibility of using acoustic imaging from carbon-ion beams to detect the Bragg peak position and measure tumor dose in real-time. Carbon-ion dose mapping and relative biological effectiveness (RBE) assessment can be facilitated by real-time signal monitoring. Our study aims to significantly advance the field by addressing the lack of a verification technique for carbon-ion beams, focusing on the considerable impact of the nuclear fragmentation tail on ionoacoustic signal waveforms, which provides crucial insights into the unique energy deposition properties of carbon-ions.

Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future.

GPU implementation of a fast active noise control algorithm with multiple reference signals

Traditional active noise control (ANC) systems are mostly implemented on digital signal processor (DSP). Due to the inadequate computational power of the DSP, especially in applications with multiple reference signals, the DSP is unable to update the optimal control filter for different types of noise. Hence, Graphics Processing Unit (GPU) has been incorporated to the ANC system to enhance its computational power. In this work, a DSP-CPU-GPU computing architecture is adopted to improve the computational performance of the ANC system and ensure the real-time signal processing. In addition, a fast ANC algorithm on the GPU is employed to update the control filters to handle different reference signals. Finally, the effectiveness of the method is verified by experiments in the condition of multiple reference signals.

TDP-43 Amyloid Fibril Formation via Phase Separation-Related and -Unrelated Pathways.

Intrinsically disordered regions (IDRs) in proteins can undergo liquid-liquid phase separation (LLPS) for functional assembly, but this increases the chance of forming disease-associated amyloid fibrils. Not all amyloid fibrils form through LLPS however, and the importance of LLPS relative to other pathways in fibril formation remains unclear. We investigated this question in TDP-43, a motor neuron disease and dementia-causing protein that undergoes LLPS, using thioflavin T (ThT) fluorescence, NMR, transmission electron microscopy (TEM), and wide-angle X-ray scattering (WAXS) experiments. Using a fluorescence probe modified from ThT strategically designed for targeting protein assembly rather than β-sheets and supported by TEM images, we propose that the biphasic ThT signals observed under LLPS-favoring conditions are due to the presence of amorphous aggregates. These aggregates represent an intermediate state that diverges from the direct pathway to β-sheet-dominant fibrils. Under non-LLPS conditions in contrast (at low pH or at physiological conditions in a construct with key LLPS residues removed), the protein forms a hydrogel. Real-time WAXS data, ThT signals, and TEM images collectively demonstrate that the gelation process circumvents LLPS and yet still results in the formation of fibril-like structural networks. We suggest that the IDR of TDP-43 forms disease-causing amyloid fibrils regardless of the formation pathway. Our findings shed light on why both LLPS-promoting and LLPS-inhibiting mutants are found in TDP-43-related diseases.

Comprehensive monitoring system for high voltage cables based on the ground current signal of the cable metal sheath

Abstract This paper has presented a comprehensive monitoring system for high voltage cables based on the ground current signal of the cable metal sheath. The system can collect and process the power frequency AC current and three high-frequency partial discharge signals in real time synchronously, which realize the synchronous detection of leakage current, relative dielectric loss, harmonic, partial discharge, and improve the integration of the hardware system.