Relevant Information Research Articles

Validity Evidence for the Self-Absorption Scale (SAS) in Spanish Population.

The Self-Absorption Scale (SAS) is one of the few instruments that measure dysfunctional self-focused attention or self-absorption, a transdiagnostic factor of vulnerability to various emotional disorders. The internal structure of the Spanish version of the SAS and its relationship with other variables have not been examined, nor has whether its subscales provide relevant information. These were the two goals of the present study. The factor structure of the SAS, its internal consistency, and its relationship with depression and post-traumatic stress were analyzed in a Spanish community sample of 519 adults. The SAS presented a symmetrical bifactor structure with a general factor of self-absorption that explained most of the variance in the items and two specific factors of private and public self-absorption. The total scale and the two subscales of the SAS exhibited excellent, good or adequate reliability coefficients (alphas/omegas = .70 – .88) and correlated with depression and post-traumatic stress ( r = .34 – .46). The SAS provides reliable, valid measures of dysfunctional self-focused attention in Spanish adults, but its Private and Public Self-absorption subscales are not much more useful than the information provided by its total scale.

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.

During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.

A Digital Camera-Based Eye Movement Assessment Method for NeuroEye Examination

The ability to perform quantitative and automated neurological assessment could enhance diagnosis and treatment in the pre-hospital setting, such as during telemedicine or emergency medical services (EMS) encounters. Such a tool could be developed by adapting clinically significant information such as symmetry of eye movement or conjugate eye movement. Here we describe a digital camera-based eye tracking method “NeuroGaze” to capture the symmetry of eye movement while performing neurological eye examination. The proposed method was developed based on detecting the center of the pupil for both eyes from a given video and measuring eye conjugacy by transforming the pupil center coordinates to relative gaze. The method was tested on healthy volunteers while performing three neurological eye examinations <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> The NeuroEye dataset is made available at <uri>https://www.kaggle.com/datasets/mahassan8/neuroeye</uri> </fn> . We also compared our proposed approach to state-of-the-art digital camera-based eye-tracking methods and commercial off-the-shelf (COTS) eye trackers. NeuroGaze outperformed digital camera-based eye tracking methods by reporting a mean Spearman rank-order correlation coefficient of 0.86 for the H-test, 0.87 for the Dot-test, and 0.56 for the OKN-Test, and shows similarity in trends for the relative gaze trajectories with a noticeable offset in the scale of the relative gaze angle compared to COTS eye tracker (see Fig. 1). The study demonstrates that by using a pupil-center-based eye-tracking method, a digital camera can measure clinically relevant information regarding eye movement.

A Systematic Analysis, Outstanding Challenges, and Future Prospects for Routing Protocols and Machine Learning Algorithms in Underwater Wireless Acoustic Sensor Networks

Water has covered a wide part of the earth’s surface. Oceans and other water bodies contain significant natural and environmental resources as well as aquatic life. Due to humans’ hazardous and unsuitable underwater (UW) settings, these are generally undiscovered and unknown. As a result of its widespread utility in fields as diverse as oceanography, ecology, seismology, and oceanography, underwater wireless sensor networks (UWSNs) have emerged as a cutting-edge area of study. Despite their usefulness, the performance of the network is hampered by factors including excessive propagation delay, a changing network architecture, a lack of bandwidth, and a battery life that is too short on sensor nodes. Developing effective routing protocols is the best way to overcome these challenges. An effective routing protocol can relay data from the network’s root node to its final destination. Therefore, the state of the art in underwater wireless acoustic sensor network (UWASN) routing protocols is assessed with an eye toward their potential for development. In real-world applications, sensor node positions are frequently used to locate relevant information. As a result, it is crucial to conduct research on routing protocols. Reinforcement learning (RL) algorithms have the ability to enhance routing under a variety of conditions because they are experience-based learning algorithms. Underwater routing methods for UWSN are reviewed in detail, including those that rely on machine learning (ML), energy, clustering and evolutionary approaches. Tables are incorporated for the suggested protocols by including the benefits, drawbacks, and performance assessments, which make the information easier to digest. Also, several applications of UWSN are discussed with security considerations. In addition to this, the analysis of node deployment and residual energy is discussed in this review. Furthermore, the domain review emphasizes UW routing protocol research difficulties and future directions, which can help researchers create more efficient routing protocols based on ML in the future.

Experiences and perspectives related to shared decision-making among outpatients with degenerative joint disease in Taiwan: a qualitative study

ObjectivesVarious treatment options are available for degenerative joint disease (DJD). During clinical visits, patients and clinicians collaboratively make decisions regarding the optimal treatment for DJD; this is the essence of...

Compressing Features for Learning With Noisy Labels.

Supervised learning can be viewed as distilling relevant information from input data into feature representations. This process becomes difficult when supervision is noisy as the distilled information might not be relevant. In fact, recent research shows that networks can easily overfit all labels including those that are corrupted, and hence can hardly generalize to clean datasets. In this article, we focus on the problem of learning with noisy labels and introduce compression inductive bias to network architectures to alleviate this overfitting problem. More precisely, we revisit one classical regularization named Dropout and its variant Nested Dropout. Dropout can serve as a compression constraint for its feature dropping mechanism, while Nested Dropout further learns ordered feature representations with respect to feature importance. Moreover, the trained models with compression regularization are further combined with co-teaching for performance boost. Theoretically, we conduct bias variance decomposition of the objective function under compression regularization. We analyze it for both single model and co-teaching. This decomposition provides three insights: 1) it shows that overfitting is indeed an issue in learning with noisy labels; 2) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; and 3) it gives explanations on the performance boost brought by incorporating compression regularization into co-teaching. Experiments show that our simple approach can have comparable or even better performance than the state-of-the-art methods on benchmarks with real-world label noise including Clothing1M and ANIMAL-10N. Our implementation is available at https://yingyichen-cyy.github.io/CompressFeatNoisyLabels/.

Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets.

Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine-learning techniques to the analysis of two PAM datasets. We combine pre-trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low- and high-frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large-scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species.

Examining the Significance of Intercultural Communicative Competence Development in Trainees during Pre-service Training.

The aim of this study is to discuss the need for improved intercultural communicative competence (ICC) training for pre-service Chinese as a Second Language (CSL) teachers who travel abroad to teach. Previous studies have shown that pre-service CSL teachers often struggle with intercultural communication, and this study investigates the reasons behind their difficulties and suggests ways to address them. The researchers used the Intercultural Sensitivity Scale (ISS) and the Intercultural Effectiveness Scale (IES) to measure the ICC of 217 pre-service CSL teachers. The study found that while pre-service CSL teachers have strong intercultural communicative emotional ability, their behavioral ability in intercultural communication is weak. In particular, they lacked skills in interactive control, interactive relaxation, identity management, information skills, and behavioral flexibility. The study proposes several strategies to improve ICC training for pre-service CSL teachers. One potential approach is the use of Computer Assisted Language Learning to provide teacher-trainee students with opportunities to practice intercultural communication skills in simulated contexts, receive personalized feedback, and engage with authentic materials and cultural information relevant to their host country. CALL could also facilitate communication between pre-service CSL teachers and former CSL teachers, providing a platform for information exchange and additional psychological support. The study also suggests that pre-service training should distinguish between foreign language learning and intercultural communication skills training. Specifically, it recommends that foreign language proficiency should be improved, while training should focus on educational culture and work culture in the host country, rather than providing a general introduction to the national conditions of the country. Finally, the study proposes that pre-service training should establish contact between pre-service CSL teachers and former CSL teachers as early as possible so that trainees can learn specific relevant information in advance, which can help them get ready, avoid intercultural communication mistakes, and obtain additional psychological support. By incorporating CALL and addressing the areas of weakness identified in the study, pre-service CSL teachers can be better prepared to navigate the challenges of intercultural communication and provide effective and culturally sensitive instruction abroad.

Chromosome-scale genome sequence of Nothopassalora personata (syn. Cercosporidium personatum), a devastating fungal pathogen of peanut

Nothopassalora personata, the causal agent of late leaf spot, is an economically important fungus that poses a significant threat to peanut production worldwide. Currently, studies on N. personata are focused on epidemiology, host resistance, early environmental detection, and molecular studies based on a limited number of genes for classification. A high-quality whole genome sequence can be a valuable tool for studying complex species and identifying molecular mechanisms in pathogenicity and adaptation. This study reports a high-quality, chromosome-scale genome assembly for N. personata with a total length of 54.6 Mb, an N50 of 2.54 Mb, a longest contig length of 4.47 Mb and a BUSCO genome completeness score of 99.5% with mostly single copy genes. Genome annotation revealed 11,295 protein-encoding genes and 12,121 transcripts in which 411 of the 995 predicted proteins were putative effectors. Genome comparison with the closely related fungi Dothistroma septosporum NZE10 and Fulvia fulva (syn. Passalora fulva, Cladosporium fulvum) isolate Race 5 revealed 13 chromosomes. This genome sequence resource can be used as a reference for aligning sequences of multiple isolates to provide relevant information on the diversity and population genetic structure of N. personata to further inform peanut resistance breeding programs and devise the best disease management strategies.

Detection of anomalies amongst LIGO’s glitch populations with autoencoders

Gravitational wave (GW) interferometers are able to detect a change in distance of ~1/10 000th the size of a proton. Such sensitivity leads to large rates of non-gaussian, transient bursts of noise, also known as glitches, which hinder the detection and parameter estimation of short- and long-lived GW signals in the main detector strain. Glitches, come in a wide range of frequency-amplitude-time morphologies and may be caused by environmental or instrumental processes, so a key step towards their mitigation is to understand their population. Current approaches for their identification use supervised models to learn their morphology in the main strain with a fixed set of classes, but do not consider relevant information provided by auxiliary channels that monitor the state of the interferometers. In this work, we present an unsupervised algorithm to find anomalous glitches. Firstly, we encode a subset of auxiliary channels from Laser Interferometer Gravitational-Wave Observatory Livingston in the fractal dimension (FD), which measures the complexity of the signal. For this aim, we speed up the fractal dimension calculation to encode 1 h of data in 11 s. Secondly, we learn the underlying distribution of the data using an autoencoder with cyclic periodic convolutions. In this way, we learn the underlying distribution of glitches and we uncover unknown glitch morphologies, and overlaps in time between different glitches and misclassifications. This led to the discovery of 6.6% anomalies in the input data. The results of this investigation stress the learnable structure of auxiliary channels encoded in FD and provide a flexible framework for glitch discovery.

Enlightening the Universe behind the Milky Way bulge

Context. The location of the Solar System complicates the detection extragalactic sources beyond the Milky Way plane. The optical observations are hampered in the so-called zone of avoidance (ZOA), where stellar crowding and Galactic absorption are severe. Observations at longer wavelengths are needed in order to discover new background galaxies and complete the census in the ZOA. Aims. The goal of this work is to identify galaxies behind the Milky Way bulge using near-infrared (NIR) data from the VISTA Variables in Vía Láctea (VVV) survey. Methods. To this end, we made use of different VISTA Science Archive (VSA) tools in order to extract relevant information from more than 32 billion catalogued sources in the VVV bulge region. We find that initial photometric restriction on sources from the VSA vvvSource table combined with restrictions on star–galaxy separation parameters obtained from Source Extractor is a successful strategy for achieving acceptable levels of contamination (60%) and high completeness (75%) in the construction of a galaxy target sample. To remove contaminating Galactic sources from the initial target sample, our methodology also incorporates a visual inspection of false-colour RGB images, a crucial quality control carried out following a specifically defined procedure. Results. Under this methodology, we find 14 480 galaxy candidates in the VVV bulge region, making this sample the largest catalogue to date in the ZOA. Moreover, these new sources provide a fresh picture of the Universe hidden behind the curtain of stars, dust, and gas in the unexplored Milky Way bulge region. Conclusions. The results from this work further demonstrate the potential of the VVV/VVVX survey to find and study a large number of galaxies and extragalactic structures obscured by the Milky Way, expanding our knowledge of the Universe in this challenging and impressive region of the sky.

Ethnomedicinal, Phytochemical, and Pharmacological Properties of Diospyros mollis Griff.: A Review

Objective/Background The genus Diospyros L. (Ebenaceae) includes about 778 species worldwide, with over 500 of them known for their significant health benefits. Among them, Diospyros mollis Griff. has been used as a tonic and treatment for many diseases related to digestive disorders and intestinal parasites. Furthermore, the fruit has also been used for dyeing silk fabrics. The purpose of this comprehensive review is to provide researchers with important information about D. mollis, particularly regarding its chemical composition and biological effects. This information can support current understanding and future studies to develop novel herbal formulations based on this species. Additionally, this review aims to compile and summarize the available literature on this species, covering its botanical, ethnomedicinal, phytochemical, and pharmacological properties reported to date. Methods The relevant information on D. mollis was collected via online electronic and databases, including Scopus, Pubmed, Web of Science, Google Scholar, ScienceDirect, Springer Link, SciFinder, and other scientific databases. Results A comprehensive review of the literature reveals that D. mollis contains various chemical constituents such as naphthalene and naphthoquinone derivatives, triterpenoids, alkaloids, sterols, glycosides, and fatty acid esters isolated from the berry, root, bark, and leaf of this species, with several pharmacological effects and other applications. Many pharmacological effects, such as hyaluronidase inhibitory, anthelmintic, and antiparasitic activities of various extracts and isolated compounds of D. mollis, are reported in this review. The review demonstrates the importance of D. mollis in the treatment of diseases, its application in fabric dyeing techniques, and its traditional use. Conclusion In conclusion, D. mollis emerges as a promising botanical resource with its rich history in traditional medicine and diverse chemical constituents, paving the way for potential future applications and innovations in the field of herbal medicine and drug development.

Enhanced Global Convolution Networks for Defect Detection in Full Wavefield Imaging

Non-Destructive Evaluation (NDE) techniques based on ultrasonic guided waves can provide relevant information about the health status of an inspected structure. The analysis of the wavefields recorded through Scanning Laser Doppler Vibrometers (SLDVs) has been used successfully in NDE systems to detect damages in planar isotropic materials, such as interlaminar fractures in composites. However, the high spatial point resolution required to ensure an accurate localization and quantification of defects typically implies a time-consuming acquisition process, which limits the applicability of such approaches. In this work, we exploit the potential of convolutional neural networks (CNNs) to perform segmentation on full wavefield images, obtained by reconstruction from a low resolution grid having a spatial sampling rate below the Nyquist frequency, for the purpose of detecting and localising delaminations in carbon fibre reinforced plastic (CFRP) plates. In particular, we trained an improved version of Global Convolutional Networks (GCN) using a public dataset containing 475 simulated cases of full wavefield Lamb waves propagation in a CFRP plate, generated by an actuator with a carrier frequency of 50 kHz. We adopted channel and spatial attention mechanisms to improve the accuracy of the networks and applied our method on (i) the resulting image given by the root mean square value (RMS) in time for each spatial position and on (ii) the 3-dimensional animation representing the full wavefield propagation. Our networks show the ability to accurately locate target damages with a spatial resolution 8 times higher than the dimension of the adopted sampling grid, achieving an Intersection over Union score equal to 0.78 with a number of scanning point more than 60 times lower than the number of pixels in the output segmentation mask.

Compressive sensing of complex-valued data using Gaussian entropy

In this paper, we propose an effective compressive sensing algorithm based on Gaussian entropy for complex-data. Compared with the traditional mean squared error (MSE) method, we consider the full second order statistics information of Gaussian noise in the new algorithm, including relevant information and conjugate information, which makes the recovered signal closer to the original input signal. Simulation results of the synthesized 1D signal and 2D signal show that the proposed algorithm has better performance than the MSE method.

The prevalence factor of post-traumatic stress disorders among students of Mariupol

The subject of the study is post-traumatic stress disorder (PTSD) of people exposed to stress factors in the city of Mariupol during the active phase of the Special Military Operation in the city. Taking into account the tasks of socio-psychological rehabilitation of the civilian population located in an area of increased risk of psychological and physical trauma, identifying the level of prevalence of PTSD in Mariupol is extremely relevant from both a scientific and applied point of view. Obtaining relevant information is important both for further in-depth research and for the development and implementation of appropriate programs of socio-psychological and (or) depth psychological rehabilitation. In addition, this information is relevant in the context of solving the problem of forming the all-Russian civic identity among new generations of Russians. The study used structured clinical interview technology, as well as the Clinical-Administered PTSD Scale (CAPS) and a PTSD screening questionnaire. Based on the data obtained during the study, it was concluded that, in comparison with similar indicators identified among the civilian population of Afghanistan and Chechnya, who were in the territory of intense military operations and experienced the effects of powerful stress factors, as well as among people who witnessed the shooting of the Russian parliament in 1993, the prevalence of PSTD among residents of Mariupol who were in the city during the operation to liberate it is quite low. In the opinion of the authors of the article, this fact is explained by the fact that the majority of Russian citizens who remained in Mariupol and returned to it perceive the military actions of 2022 as a “forced necessity”. This leads to the conclusion about the extreme importance of using psychological and psychological-pedagogical technologies in the sphere of protecting traditional values.

Prospects for realtime characterization of core-collapse supernova and neutrino properties

Core-collapse supernovae (CCSNe) offer extremely valuable insights into the dynamics of galaxies. Neutrino time profiles from CCSNe, in particular, could reveal unique details about collapsing stars and particle behavior in dense environments. However, CCSNe in our galaxy and the Large Magellanic Cloud are rare and only one supernova neutrino observation has been made so far. To maximize the information obtained from the next Galactic CCSN, it is essential to combine analyses from multiple neutrino experiments in real time and transmit any relevant information to electromagnetic facilities within minutes. Locating the CCSN, in particular, is challenging, requiring disentangling CCSN localization information from observational features associated with the properties of the supernova progenitor and the physics of the neutrinos. Yet, being able to estimate the progenitor distance from the neutrino signal would be of great help for the optimisation of the electromagnetic follow-up campaign that will start soon after the propagation of the neutrino alert. Existing CCSN distance measurement algorithms based on neutrino observations hence rely on the assumption that neutrino properties can be described by the Standard Model. This paper presents a swift and robust approach to extract CCSN and neutrino physics information, leveraging diverse next-generation neutrino detectors to counteract potential measurement biases from Beyond the Standard Model effects.

THE CONVENTION ON BIOLOGICAL DIVERSITY AND ECOLOGICAL PROTECTION-RELATED LAWS AND POLICIES IN GHANA

This study sets out to examine the implementation of the Convention on Biological Diversity (CBD) in Ghana. A key objective of the study was to understand how the country has attempted to achieve sustainable development while reducing the risk to biodiversity and environmental pollution given its abundant natural resources. In an effort to achieve this and other related objectives of the study, the researcher adopted a qualitative approach to research which enabled him to gather relevant information in non-numerical format. The study consequently finds that though the country has introduced several legislations and policies to ensure ecological protection, several challenges still remain. Indeed, in Ghana today, attempting to ensure the functionality, orderliness and the usefulness of ecological protection policies such as those aimed at the effective implementation of the CBD necessarily requires that such policies have firm legitimacy among the local norms; legality alone is not enough. It is in view of this that the study advocates for a balanced obligation.

First assessment of passive acoustics as a tool to monitor the endangered Mediterranean monk seal in the Madeira Archipelago (Portugal)

Abstract The rarest seal and the world's most endangered pinniped species, the Mediterranean monk seal (Monachus monachus), has a small and isolated population in the Madeira Archipelago (Portugal). This species tends to be extremely wary of humans and, therefore, very difficult to approach and study. Passive acoustic monitoring (PAM) is a non‐invasive, cost‐effective tool that can be a valuable complement for the traditional monitoring methods, providing insight for effective conservation of the seal in the Madeira Archipelago. In this pilot study, custom‐designed autonomous underwater recorders were deployed in two marine protected areas (Garajau Partial Nature Reserve and the Desertas Islands Nature Reserve) to assess the potential of PAM to detect and monitor this elusive and endangered species in the Madeira Archipelago. Two call types putatively produced by M. monachus were detected in a subsample of audio files recorded over a 3‐month acoustic deployment; these call types share similarities with the /growl/ and /hiccup/ recently described for M. monachus in a Mediterranean population. The most common sound type detected was the low‐frequency growl. No obvious pattern was found in the abundance of sounds according to sampling date, and no significant difference was found in the abundance of sounds in different periods of the day. The ability to detect the species' underwater vocalizations with PAM opens the possibility of future monitoring plans based on data obtained from audio recordings. These data can provide relevant information for conservation, namely, on the presence and abundance of the seals.

VariantDetective: an accurate all-in-one pipeline for detecting consensus bacterial SNPs and SVs.

Genomic variations comprise a spectrum of alterations, ranging from single nucleotide polymorphisms (SNPs) to large-scale structural variants (SVs), which play crucial roles in bacterial evolution and species diversification. Accurately identifying SNPs and SVs is beneficial for subsequent evolutionary and epidemiological studies. This study presents VariantDetective (VD), a novel, user-friendly, and all-in-one pipeline combining SNP and SV calling to generate consensus genomic variants using multiple tools. The VD pipeline accepts various file types as input to initiate SNP and/or SV calling, and benchmarking results demonstrate VD's robustness and high accuracy across multiple tested datasets when compared to existing variant calling approaches. The source code, test data, and relevant information for VD are freely accessible at https://github.com/OLF-Bioinformatics/VariantDetective under the MIT License.

Design and Analysis of Battery Management System for Electric Vehicles

Abstract: This article presents a design for both the hardware and software components of the BMS, enabling battery monitoring and management. The Battery Management System (BMS) is a fundamental component of electric vehicles, primarily utilized to ensure battery safety and enhance battery lifespan. The hardware component encompasses the design of voltage acquisition circuitry, second-order filtering circuitry, sampling and holding circuitry, CAN bus communication circuitry, and other relevant features. The software section comprises subroutines for battery information collection, equalization circuitry, SOC estimation, and other relevant features. The BMS developed in this study successfully collects voltage, temperature, current, and other relevant information, and accurately estimates SOC and other crucial parameters. Testing confirmed that the battery management system precisely collects battery voltage, current, and temperature information, while the SOC estimation achieves a relatively high degree of accuracy.