Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Excitation Enabled Decoupling, Calibration, and Compensation of Errors for Whole-Angle Hemispherical Resonator Gyroscope

The precise calibration and compensation of detection, drive, system phase, and resonator errors in whole-angle (WA) hemispherical resonator gyroscope (HRG) are essential to improve performance. This article comprehensively considers the influences of four kinds of errors, including the gain mismatch and angle misalignment of detection and drive electrodes; the nonlinearity of capacitance detection and electrostatic drive; the phase delay of each module in the measurement and control system; and the anisodamping and anisoelasticity of resonator. A novel self-excitation enabled decoupling, calibration, and compensation (SE-DCC) process is proposed based on the analysis and modeling of these errors. The detection errors are first calibrated and compensated under high-speed turntable excitation. The drive and system phase errors are then self-calibrated and self-compensated under self-excitation operation. Finally, the characteristics of resonator appear, and the self-calibration and force compensation of anisodamping are completed. Experimental results demonstrate that the angle-dependent errors under high-speed rotation are effectively suppressed, the peak-to-peak value of angle-dependent bias (ADB) is reduced by more than two orders from 31.44°/h to 0.25°/h, the scale factor (SF) nonlinearity in ±500°/s range is reduced by 85 times from 74.6 to 0.879 ppm, the bias stability ( $1\sigma $ ) is reduced from 0.7740°/h to 0.0134°/h, and the bias instability (BI) is reduced by one order from 0.0991°/h to 0.0092°/h. The SE-DCC process is proven to push the accuracy of the tested WA HRG to inertial level and shows promise for expanding to all types of WA gyroscopes with high $Q$ factor and low-frequency spilt.
Potential Immunoregulatory Mechanism of Plant Saponins: A Review

Saponins are extracted from different parts of plants such as seeds, roots, stems, and leaves and have a variety of biological activities including immunomodulatory, anti-inflammatory effects, and hypoglycemic properties. They demonstrate inherent low immunogenicity and possess the capacity to effectively regulate both the innate and adaptive immune responses. Plant saponins can promote the growth and development of the body’s immune organs through a variety of signaling pathways, regulate the activity of a variety of immune cells, and increase the secretion of immune-related cytokines and antigen-specific antibodies, thereby exerting the role of immune activity. However, the chemical structure of plant saponins determines its certain hemolytic and cytotoxicity. With the development of science and technology, these disadvantages can be avoided or reduced by certain technical means. In recent years, there has been a significant surge in interest surrounding the investigation of plant saponins as immunomodulators. Consequently, the objective of this review is to thoroughly examine the immunomodulatory properties of plant saponins and elucidate their potential mechanisms, with the intention of offering a valuable point of reference for subsequent research and advancement within this domain.
Differential and Symmetrical Decoupling Network for Differentially Fed Antennas

This letter proposes the differential and symmetrical decoupling network (DSDN), a novel method specifically designed to decouple differentially fed antennas (DFAs) while preserving their structural symmetry. DFAs offer several advantages over single-fed antennas (SFAs) due to their symmetrical design, but the presence of multiple antenna ports and coupling paths poses challenges for decoupling. Existing methods for SFAs disrupt the structural symmetry of DFAs. The DSDN significantly enhances the isolation between DFAs, and its effectiveness is validated through theoretical derivations, simulations, and experimental measurements on a 1 × 2 DFA array and a 1 × 4 DFA array. The results demonstrate isolation improvements of 53 and 40 dB at the center frequency for the 1 × 2 and the 1 × 4 DFA array, respectively, compared with the coupled array. The DSDN offers a promising solution for decoupling DFAs, allowing for significantly improved performance while maintaining structural symmetry.
The prediction of Bitcoin price through gold price using long short-term memory model

The majority of research on predicting the price of Bitcoin employs technical methods to enhance long short-term memory models' effectiveness. Although some studies employ different machine learning techniques, such as economic or technical indicators, their precision is inadequate. Thus, this research aims to introduce a model that predicts the price of Bitcoin by utilizing the long short-term memory (LSTM) technique and incorporating gold's economic and technical data as features. The research collected gold and Bitcoin price data from FinanceDataReader for around seven years, from January 1, 2016, to January 22, 2023, consisting of six categories: date, open, high, low, close, volume, and change (based on dollars). The normalized closing price data was trained for 50 epochs, resulting in the loss value reaching close to zero. The model's accuracy was measured by mean squared error, resulting in a score of 0.0004. This study's importance is two-fold: firstly, it can provide cryptocurrency-related businesses with more accurate predictions and improved risk management indicators. Secondly, incorporating economic metrics can address the limitations of overfitting and a single model's poor performance.
Towards a new approach to maximize tax collection using machine learning algorithms

Efficient tax debt collection is a challenge for Moroccan local tax authorities. This article explores the potential of machine learning techniques and novel strategies to enhance efficiency in this process. We present a practical use case demonstrating the application of machine learning for taxpayer segmentation, improving accuracy in identifying high-risk debtors. Using a comprehensive dataset of tax payment behavior, we showcase the effectiveness of machine learning algorithms in segmenting taxpayers based on their likelihood of non-compliance or debt accumulation. We also investigate innovative strategies that integrate behavioral economics principles to enable better targeted interventions. Real-world case studies in local tax debt collection highlight the impact of these strategies. The findings underscore the transformative potential of machine learning techniques and novel strategies in improving the efficiency of local tax debt collection. Accurate identification of high-risk debtors and tailored enforcement actions help maximize revenue while minimizing resource waste. This research contributes to the existing knowledge by providing insights into the implementation of machine learning techniques and novel strategies in tax debt collection. It emphasizes the importance of data-driven approaches and highlights how local tax authorities can drive efficiency and optimize revenue collection by embracing these advancements.
Implementation and Analysis of Single Digit BCD Multipliers Without Generating Partial Products

Multipliers play a crucial role in signal processing systems and any improvement in their speed directly enhances the overall system performance. Traditionally, multipliers consist of two main components: partial product generation and addition of these partial products to obtain the final results. Binary-coded decimal (BCD) multipliers additionally require a binary to BCD conversion step to achieve the desired output. Performance enhancement of multipliers can be done by reducing the number of partial products formed or by using compression techniques to reduce the number of bits to be added. Fast adders with reduced propagation delays can also be utilised. While using faster binary multipliers for BCD multiplication, binary to BCD converters are needed. The converters add more delay to the multiplier circuit. The proposed multipliers do away with the need for both partial product generation and its addition along with binary to BCD converters. By eliminating the need for partial product generation and addition, the proposed multiplier achieves notable speed improvements. The multiplier uses Vin-culum method for bit reduction. The multiplier is implemented on Basys3 board using Xilinx Vivado 2020.2. When implemented on Artix-7 FPGA, the multiplier showed a speed improvement of 53 % compared to BCD multiplier using traditional array multiplier and BCD converter
Artificial Intelligent Management for Internet of Things: A Review

Nowadays, advances in communication technologies and information processing have formed the Internet of Things. The Internet of Things (IoT) is a technology that is used to send and receive the data of sensors by the use of the internet. The Internet of Things concept has developed in new fields of technology. Because the rapid developments of data carry novel challenges for IoT, the data is a huge artificial intelligence with Internet of Things systems that make managing these data more efficient to store them on the internet of things and protect it from intruders. Combining internet of thing technology with intelligent management has significant study in different areas. Machine learning and fuzzy logic are the most widely used in a broad application of the Internet of Things. This paper introduces the developments of artificial intelligence (AI) and the Internet of Things (IoT). Also, this study highlights the challenges of using artificial intelligence in the IoT that address difficulties for different applications. This article displays the meaning and importance of artificial intelligence and some applications and benefits of artificial intelligence and the Internet of Things. Also, we show the related works of AI and IoT. In the future, it was found through previous studies that the integration of artificial intelligence with the Internet of Things can be used in many areas, in addition to protecting data and institutions from hackers and natural disasters such as fire, flood, theft, etc. via security methods and techniques of artificial intelligence.
Emotion Recognition System to Suggest and Respond to Vietnamese Using PhoBert Model

The article proposes an emotion recognition system to suggest and automatically respond in conversations to increase user experience and give immediate feedback. We propose to build an emotion recognition system including six types of emotions (disgust, pleasure, sad, fear, anger, and others) using the PhoBERT model. Besides, we use the dataset built in the article [1] to compare and demonstrate the improvement from the proposal model. The input data is text. The output is the emotional label corresponding to the input. The system has an accuracy of up to 99.8% with the training dataset and 96.4% with the testing dataset. This result is an improvement 15% comparedto [1] showing that we can build a more accurate emotion recognition system for users and be applicable in practice.
Structure of Tris[2-(4-pyridyl)ethyl]phosphine, Tris[2-(2-pyridyl)ethyl]phosphine, and Their Chalcogenides in Solution: Dipole Moments, IR Spectroscopy, and DFT Study

Tris(hetaryl)substituted phosphines and their chalcogenides are promising polydentate ligands for the design of metal complexes. An experimental and theoretical conformational analysis of tris[2-(4-pyridyl)ethyl]phosphine, tris[2-(2-pyridyl)ethyl]phosphine, and their chalcogenides was carried out by the methods of dipole moments, IR spectroscopy and DFT B3PW91/6-311++G(df,p) calculations. In solution, these compounds exist as an equilibrium of mainly non-eclipsed (synclinal or antiperiplanar) forms with a predominance of a symmetrical conformer having a gauche-orientation of the Csp3–Csp3 bonds of pyridylethyl substituents relative to the P=X bond (X = lone pair, O, S, Se) and a gauche-orientation of the pyridyl rings relative to the zigzag ethylene bridges. Regardless of the presence and nature of the chalcogen atom (oxygen, sulfur, or selenium) in the studied molecules with many axes of internal rotation, steric factors—the different position of the nitrogen atoms in the pyridyl rings and the configuration of ethylene bridges—determine the realization and spatial structure of preferred conformers.
Performance analysis of congestion-aware Q-routing algorithm for network on chip

A network on chip’s performance is greatly impacted by network congestion due to the substantial increase in latency and energy utilized. Designing routing strategies that keep the network informed of the status of traffic is made easier by machine learning techniques. In this work, a reinforcement-based congestion-aware Q-routing (CAQR) technique has been presented. The proposed algorithm performed better in comparison to the conventional XY routing method tested against the SPEC CPU2006 benchmark suite in the gem5 NoC simulator tool. The suite used has 4 benchmarks, namely, namd, lbm, leslie3d and bzip2 which can be used for the cores in the network in any combination. The tests were run with 16 cores on a 44 network with the maximum instruction count supported by the system (here 5,000). The proposed Q-routing algorithm showed an average of 19% reduction for benchmark simulation as compared to the Dimension-ordered (X-Y) routing for readings of average packet latency which is a crucial factor in determining a network’s efficiency. The analysis also shows an average reduction of 24%, 10%, 23% and 47% in terms of average packet network latency, average flit latency, average flit network latency and average energy consumption across various benchmarks.
K-centroid convergence clustering identification in one-label per type for disease prediction

Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3I system with the most important features and remain the same accuracy.
DISCRETE ELEMENT ANALYSIS OF COLLAPSING AND SLIDING RESPONSE OF SLOPE TRIGGERED BY TIME DIFFERENCE COUPLING EFFECTS OF P AND S SEISMIC WAVES——TAKING TANGJIASHAN LANDSLIDE IN BEICHUAN COUNTY FOR EXAMPLE

Collapsing and sliding responses of Tangjiashan landslide near Beichuan county and its running out process triggered by time difference coupling effects(considering horizontal and vertical seismic force effects all together),the regionality and spatial heterogeneity of P and S seismic waves generating horizontal and vertical seismic loads are elaborated;and formation mechanism and key controlling factors of collapsing and sliding of the slope are confirmed by applying DEM numerical simulation. The result shows that initial collapsing and sliding of the slope are triggered by combined action of horizontal and vertical tension caused by P seismic wave,and the vertical tension plays a superiority role. Ejecting and flowing of the broken slope mass are triggered by combined action of P and S seismic waves. Secondly,combined action between horizontal and vertical tension of P wave is the key controlling factor inducing the slope collapsing and sliding. At the same time,slope topography is the key controlling factor leading to collision and debris flow of the slope mass fractured in latter process. Finally, amplification effect of parameters in slope dynamic response shows that the amplification coefficient of vertical acceleration is larger than that of horizontal acceleration,and the vertical velocity and horizontal velocity decreasing one by one,and this trend is consistent with key controlling factor inducing slope fracturing,i.e. vertical seismic loads playing a superiority role. It is of great theoretical and practical value for studying collapsing and sliding of slopes triggered by seismic loads.
Low-Cost Optical Wireless CDMA Transceiver Design for IoT Applications

In this work, we implement a low-cost optical wireless communication (OWC) system based on code-division multiple access (CDMA). The system can be used in Internet of Things (IoT) applications as it is low-cost and can easily be deployed in small IoT devices. Due to the line-of-sight nature of OWC, the system provides a secure communication means. The framework uses white light LEDs for transmission and an array of 9 photodiodes for reception. Pulsed on-off-keying (OOK) modulation is used with CDMA applied for multi-user scenarios. Each user in a CDMA network is assigned a unique code. Two users modulate their signals using the CDMA codes which are jointly transmitted from a single transmitter to multiple receivers. Many formulations exist for generating these unique codes, however, in this work, we use a very popular set of codes called Walsh codes (also known as Hadamard matrices).
Efficient supercontinuum generation in a filter-less cascade of silica, fluoride, and chalcogenide fibers

We present an efficient supercontinuum generation spanning in the wavelength range of 1.7-4.5 µm in a passive and filter-less cascade of silica, ZBLAN, and Ge<inf>10</inf>As<inf>22</inf>Se<inf>68</inf> fibers. The supercontinuum is obtained with an energy conversion efficiency of ~17% and pulse energy of ~1.7 nJ.
The limbic circuit for response vigour

Motivation can be defined as the amount of energy an animal puts into actions to achieve goals. This not a static property but can be learned. Recently, abstract models have been developed for this kind of learning which propose that tonic dopamine is responsible to energise behaviour. However the underlying circuitry is still not understood and it is still unclear how tonic dopamine acts on the motor system to energise behaviour. In this paper we present a limbic system model of response invigoration which is based on neurophysiological data and anatomical connections. We argue that the indirect pathway from NAcc core D2 neurons via the ventral pallidum to the SNr conveys response vigour, while the direct pathway from NAcc core D1 neurons is responsible for action selection. Once this detailed anatomically mapped circuit has been established we then condense this realistic model into an abstract model which then shows that tonic DA acts in a multiplicative way on the motor output and thus is able to energise behaviour. We demonstrate the model with both a simulated and real robot experiment.
Wireless Threats Against V2X Communication

As the automotive industry increasingly relies on wireless technologies, a new attack surface emerges, posing significant security threats to modern cars. This paper investigates the vulnerabilities and risks of wireless vehicle attacks, including eavesdropping, message tampering, spoofing, and jamming. It highlights vulnerabilities in the CAN bus communication interface. By exploring these attacks and their potential consequences, this paper aims to shed light on the urgent need for robust security measures to safeguard the safety and privacy of vehicle owners. The focus is on understanding the evolving landscape of wireless threats in the automotive industry, providing valuable insights for researchers, practitioners, and stakeholders involved in developing effective countermeasures and enhancing overall vehicle security. In contrast to other research articles, this paper presents the ISO/SAE DIS 21434 standard, which offers a systematic and structured approach to enhance cybersecurity in the automotive industry, even in the face of emerging wireless threats. In addition, this paper highlights notable examples of attacks on modern cars, where researchers gained access to vehicle systems through wireless vulnerabilities, demonstrating the potential dangers of interconnected car systems to illustrate the real-world implications.
Artificial intelligence research in Nigeria: Topic modelling and scientometric analysis

In developing countries such as Nigeria, artificial intelligence (AI) research has the potential to drive rapid advancement in various aspects of development, including the economy and technology. However, it is crucial to understand the focus of Nigerian AI researchers and identify unexplored areas of research that could lead to unprecedented development. To address this need, we used natural language processing, machine learning, and statistical algorithms to investigate the main areas of interest of Nigerian AI researchers. We identified ten topics and used scientometric analyses to reveal key concepts, keyword co-occurrences, and authorship networks. Our study found that Covenant University was the most prolific institution, with 375 publications, followed by the Federal University of Technology with 135 publications and the University of Ibadan with 121 publications. Overall, our research provides valuable insights into the structure and progression of AI research in Nigeria and highlights areas for improvement.
Full Waveform Inversion with Reconstructed Wavefield

We propose a novel approach to time domain full waveform inversion with reconstructed wavefield (RFWI). RFWI relaxes the constraint that the forward modeled data exactly solves the wave equation in conventional full waveform inversion (FWI) to an L2 approximation. RFWI estimates earth models and jointly reconstructs the wavefield by minimizing the data misfit and wave equation error. The potential benefits include avoiding cycle skipping and mitigating some of the problems with local minima. The applicability of RFWI will be illustrated on both synthetic and field examples.
Rear-Side Irradiance Simulation of Field PV Modules

Assessing the durability of photovoltaic (PV) module backsheet is critical to increasing module lifetime. Historically, laboratory-based accelerated testing has insufficient in pre-dicting large-scale failures of commercial polymeric materials. Additionally, there is growing concern that standard condition tests do not reflect non-uniformities in field exposure, and that certain modules experience more severe degradation due to their location. Anisotropy in field exposures is installation-dependent and reflects different levels of exposure to irradiance due to mounting geometry, ground surface albedo, and climatic zone. “Bificial_Radiance” [1], simulates the amount of reflected irradiance on the Polyethylene Naphthalate (PEN) backsheet of a PV array located in Maryland. In our work, site specific weather data are gathered for the entire length of exposure for the modules. The total full-spectrum dose for the incident irradiance on the backsheet was then determined. The simulation results are integrated with historical field survey data to better understand real orld outdoor degradation.