Efficient Management Research Articles

Modelling a DC Electric Railway System and Determining the Optimal Location of Wayside Energy Storage Systems for Enhancing Energy Efficiency and Energy Management

Global demand for fossil fuels is highly increasing, necessitating energy efficiency to be enhanced in transitioning to low-carbon energy systems. Electric railways are highly efficient in reducing the transportation demand for fossil fuels as they are lightweight and their energy demand can be fed by renewable energy resources. Further, the regenerative braking energy of decelerating trains can be fed to accelerating trains and stored in onboard energy storage systems (ESSs) and stationary ESSs. It is fundamental to model electric railways accurately before investigating approaches to enhancing their energy efficiency. However, electric railways are challenging to model as they are nonlinear, resulting from the rectifier substations, overvoltage protection circuits, and the unpredictability and uncertainty of the load according to the train position. There have been few studies that have examined the ESS location’s impact on improving the energy efficiency of electric railways while using specialised simulation tools in electric railways. However, no single study exists that has studied the location impact of stationary ESSs on the energy efficiency of electric railways while the trains are supported by onboard ESSs. Given these goals and challenges, the main objective of this work is to develop a model using commercial software used by industry practitioners. Further, the energy saving is aimed to be maximised using stationary ESSs installed in optimal locations while trains are supported by onboard ESSs. The model includes trains, onboard ESSs, rail tracks, passenger stations, stationary ESSs, and traction power systems involving power lines, connectors, switches, sectioning, and isolators. In this article, a test scenario is presented comprising two trains running on a 20 km with three passenger stations and two substations. The trains and track are modelled in OpenTrack simulation software (Version 1.9) while the power system is modelled in OpenPowerNet simulation software (Version 1.11). The two simulation tools are used in the railway industry and can produce realistic results by taking into account the entire electrical network structure. A stationary ESS is added on the wayside and moved in steps of 1 km to obtain the optimal location before investigating the impact of stationary ESSs on the performance and energy management of onboard ESSs. It is found that the energy saving when installing a stationary ESS at the optimal location is 56.05%, the peak-power reduction of Substation 1 is 4.37%, and the peak-power reduction of Substation 2 is 18.67%.

An effectual recommendation model using hybrid learning models for early detection of Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of individuals worldwide, causing progressive cognitive decline. Early prediction and diagnosis the AD accurately is crucial for effective intervention and treatment. In this study, we propose a comprehensive framework for AD prediction using various techniques, including preprocessing and denoising with Multilayer Perceptron (MLP) and Ant Colony Optimization (ACO), segmentation using U-Net, and classification with Spatial Pyramid Pooling Network (SPPNet). Furthermore, we employ Convolutional Neural Network (CNN) with SPPNet for training and develop a chatbot for recommendation based on MRI data input. The preprocessing and denoising techniques play a vital role in enhancing the quality of the input data. MLP is utilized for preprocessing, where it effectively handles feature extraction and noise reduction. ACO is employed for denoising, optimizing the data to improve the signal-to-noise ratio, and enhancing the overall performance of subsequent stages. For accurate segmentation of brain regions, we employ the U-Net architecture, which has shown remarkable success in medical image segmentation tasks. U-Net effectively identifies the regions of interest, aiding in subsequent classification stages. The classification phase utilizes SPPNet, a deep learning model known for its ability to capture spatial information at multiple scales. SPPNet extracts features from segmented brain regions, enabling robust classification of AD and non-AD cases. To enhance the training process, we employ CNN with SPPNet, leveraging the power of convolutional layers to capture intricate patterns and improve predictive accuracy. The CNN-SPPNet model is trained on a large dataset of MRI scans, enabling it to learn complex representations and make accurate predictions. Hence the proposed work can be integrated with a chatbot that takes MRI data as input and provides recommendations based on the predicted AD probability. Experimental evaluation shows that the combination of preprocessing, denoising, segmentation, and classification offers a comprehensive solution for accurate and efficient AD diagnosis and management.

Anthracycline-Induced Cardiomyopathy in Cancer Survivors: Management and Long-Term Implications.

Recent advancements in personalized treatments, such as anthracycline chemotherapy, coupled with timely diagnoses, have contributed to a decrease in cancer-specific mortality rates and an improvement in cancer prognosis. Anthracyclines, a potent class of antibiotics, are extensively used as anticancer medications to treat a broad spectrum of tumors. Despite these advancements, a considerable number of cancer survivors face increased risks of treatment complications, particularly the cardiotoxic effects of chemotherapeutic drugs like anthracyclines. These effects can range from subclinical manifestations to severe consequences such as irreversible heart failure and death, highlighting the need for effective management of chemotherapy side effects for improved cancer care outcomes. Given the lack of specific treatments, early detection of subclinical cardiac events post-anthracycline therapy and the implementation of preventive strategies are vital. An interdisciplinary approach involving cardiovascular teams is crucial for the prevention and efficient management of anthracycline-induced cardiotoxicity. Various factors, such as age, gender, duration of treatment, and comorbidities, should be considered significant risk factors for developing chemotherapy-related cardiotoxicity. Tools such as electrocardiography, echocardiography, nuclear imaging, magnetic resonance imaging, histopathologic evaluations, and serum biomarkers should be appropriately used for the early detection of anthracycline-related cardiotoxicity. Furthermore, understanding the underlying biological mechanisms is key to developing preventive measures and personalized treatment strategies to mitigate anthracycline-induced cardiotoxicity. Exploring specific cardiotoxic mechanisms and identifying genetic variations can offer fresh perspectives on innovative, personalized treatments. This chapter aims to discuss cardiomyopathy following anthracycline therapy, with a focus on molecular mechanisms, preventive strategies, and emerging treatments.

A methodological approach for data standardization and management of Open Data portals for scientific research groups: a case study on mobile and ubiquitous ecosystems

IntroductionIn the scientific research domain, the Open Science movement stands as a fundamental pillar for advancing knowledge and innovation globally. This article presents the design and implementation of the GITCE Open Data Ecosystem (GITCE-ODE) Research Data Management System (RDMS), developed by the Research Group on Emerging Computational Technologies (GITCE) at the Technological University of Panama, as a platform for the long-term storage, publication, and dissemination of research products.MethodsThe architecture of the GITCE-ODE RDMS encompasses the entire data engineering lifecycle, facilitating information processing stages such as extraction, transformation, loading (ETL), as well as the management and analysis of diverse datasets and metadata.ResultsCompliance with the FAIR principles ensures that published data and products are Findable, Accessible, Interoperable, and Reusable, promoting automation in the discovery and reuse of digital resources. Key considerations of the web portal include file format standardization, data categorization, treatment of semantic context, and organization of resources to ensure efficient management and administration of open research data.DiscussionThrough this platform, GITCE aims to foster collaboration, transparency, and accessibility in scientific research, contributing to the ongoing advancement of knowledge transfer and innovation.

Efficient and Robust Dynamic Crosslinking for Compatibilizing Immiscible Mixed Plastics through In Situ Generated Singlet Nitrenes.

Creating a sustainable economy for plastics demands the exploration of new strategies for efficient management of mixed plastic waste. The inherent incompatibility of different plastics poses a major challenge in plastic mechanical recycling, resulting in phase-separated materials with inferior mechanical properties. Here, this study presents a robust and efficient dynamic crosslinking chemistry that effectively compatibilizes mixed plastics. Composed of aromatic sulfonyl azides, the dynamic crosslinker shows high thermal stability and generates singlet nitrene species in situ during solvent-free melt-extrusion, effectively promoting C─H insertion across diverse plastics. This new method demonstrates successful compatibilization of binary polymer blends and model mixed plastics, enhancing mechanical performance and improving phase morphology. It holds promise for managing mixed plastic waste, supporting a more sustainable lifecycle for plastics.

Secondary diabetes due to different etiologies: Four case reports

BACKGROUND As research on diabetes continues to advance, more complex classifications of this disease have emerged, revealing the existence of special types of diabetes, and many of these patients are prone to misdiagnosis and underdiagnosis, leading to treatment delays and increased health care costs. The purpose of this study was to identify four causes of secondary diabetes. CASE SUMMARY Secondary diabetes can be caused by various factors, some of which are often overlooked. These factors include genetic defects, autoimmune disorders, and diabetes induced by tumours. This paper describes four types of secondary diabetes caused by Williams–Beuren syndrome, Prader–Willi syndrome, pituitary adenoma, and IgG4-related diseases. These cases deviate significantly from the typical progression of the disease due to their low incidence and rarity, often leading to their neglect in clinical practice. In comparison to regular diabetes patients, the four individuals described here exhibited distinct characteristics. Standard hypoglycaemic treatments failed to effectively control the disease. Subsequently, a series of examinations and follow-up history confirmed the diagnosis and underlying cause of diabetes. Upon addressing the primary condition, such as excising a pituitary adenoma, providing glucocorticoid supplementation, and implementing symptomatic treatments, all patients experienced a considerable decrease in blood glucose levels, which were subsequently maintained within a stable range. Furthermore, other accompanying symptoms improved. CONCLUSION Rare diseases causing secondary diabetes are often not considered in the diagnosis of diabetes. Therefore, it is crucial to conduct genetic tests, antibody detection and other appropriate diagnostic measures when necessary to facilitate early diagnosis and intervention through proactive and efficient management of the underlying condition, ultimately improving patient outcomes.

Integrating FAIR Experimental Metadata for Multi-omics Data Analysis

AbstractThe technological advancements in bio-science research are resulting in the generation of vast amounts of complex and heterogeneous data sets from individual studies. Efficient Research Data Management solutions based on FAIR principles can guide the research groups toward standardizing and packaging the study-specific research results into uniquely identifiable digital objects that are easily traceable and identifiable, leading to knowledge discovery, collaboration, and innovation. However, to explore the inter-dependencies among data sets originating from different study disciplines, it is crucial to deploy a generic data-centric RDM solution that overcomes inherent challenges and helps manage complex data sets. This solution should respect participating groups’ data and security policies while providing an integrated data view. In this paper, we introduce , a cloud-native search & exploration application that integrates experimental metadata from individual research groups that have deployed PLANTdataHUB (FAIR-RDM) solution. The focus is on the parts of PLANTdataHUB and ARC Registry that facilitate near-real-time integration of experimental metadata from standardized digital objects (Annotated Research Contexts) across participating research groups.

Evaluation of the Efficacy of Fascia Iliaca Compartment Block in Maintaining Cognitive Function Post-Surgery in Elderly Individuals with Hip Fractures

Postoperative cognitive dysfunction (POCD) is a prevalent issue among elderly hip fracture patients’ post- surgery period, leading to cognitive decline and prolonged recovery. This narrative review delves into the efficacy of Fascia Iliaca Compartment Block (FICB) in addressing POCD. FICB, a targeted anesthetic approach, not only provides efficient pain management but also reduces inflammation and minimizes opioid reliance in the elderly. By alleviating pain and inflammation, FICB facilitates enhanced early postoperative mobility, potentially lowering the likelihood of cognitive dysfunction. Research indicates that FICB can decrease tau protein levels and inflammatory markers, attenuating the inflammatory cascade linked to cognitive impairment. Moreover, FICB's ability to offer localized pain relief without systemic opioid repercussions aids in preventing complications like delirium. The utilization of FICB emerges as a pivotal strategy in optimizing postoperative outcomes and diminishing POCD incidence in elderly hip fracture patients. Further exploration and integration of FICB in clinical practice hold promise for enhancing the care of this vulnerable patient cohort.

An Efficient Approach to Extract and Store Big Semantic Web Data Using Hadoop and Apache Spark GraphX

The volume of data is growing at an astonishingly high speed. Traditional techniques for storing and processing data, such as relational and centralized databases, have become inefficient and time-consuming. Linked data and the Semantic Web make internet data machine-readable. Because of the increasing volume of linked data and Semantic Web data, storing and working with them using traditional approaches is not enough, and this causes limited hardware resources. To solve this problem, storing datasets using distributed and clustered methods is essential. Hadoop can store datasets because it can use many hard disks for distributed data clustering; Apache Spark can be used for parallel data processing more efficiently than Hadoop MapReduce because Spark uses memory instead of the hard disk. Semantic Web data has been stored and processed in this paper using Apache Spark GraphX and the Hadoop Distributed File System (HDFS). Spark's in-memory processing and distributed computing enable efficient data analysis of massive datasets stored in HDFS. Spark GraphX allows graph-based semantic web data processing. The fundamental objective of this work is to provide a way for efficiently combining Semantic Web and big data technologies to utilize their combined strengths in data analysis and processing. First, the proposed approach uses the SPARQL query language to extract Semantic Web data from DBpedia datasets. DBpedia is a hugely available Semantic Web dataset built on Wikipedia. Secondly, the extracted Semantic Web data was converted to the GraphX data format; vertices and edges files were generated. The conversion process is implemented using Apache Spark GraphX. Third, both vertices and edge tables are stored in HDFS and are available for visualization and analysis operations. Furthermore, the proposed techniques improve the data storage efficiency by reducing the amount of storage space by half when converting from Semantic Web Data to a GraphX file, meaning the RDF size is around 133.8 and GraphX is 75.3. Adopting parallel data processing provided by Apache Spark in the proposed technique reduces the required data processing and analysis time. This article concludes that Apache Spark GraphX can enhance Semantic Web and Big Data technologies. We minimize data size and processing time by converting Semantic Web data to GraphX format, enabling efficient data management and seamless integration.

Prevalence, under-reporting, and epidemiological surveillance of COVID-19 in the Araguaína City of Brazil.

Asymptomatic and underreported individuals remain a source of coronafig disease 2019 (COVID-19) transmission to others. Data on the prevalence and epidemiological factors influencing transmission are fundamental for establishing control measures, especially in vulnerable regions such as the Amazon. This study aimed to determine the point prevalence and active infection of COVID-19 among the population in Araguaína, a Brazilian city located in the Amazon region, analyzed the socioeconomic and behavioral variables of a statistically representative sample of this population using an epidemiological survey, and identify the viral genomic diversity in the region. During the sixth epidemiological week of 2021 (February 8 to 12), samples of 497 inhabitants of the municipality asymptomatic for respiratory syndromes underwent reverse transcription-quantitative polymerase chain reaction and serological tests (immunoglobulin M and immunoglobulin G). A questionnaire collated data on socioeconomic factors, prevention measures, and health status history. The active infection rate was 6.2%, and the prevalence was 13.5% of the study population. Active infection cases were under-reported; each reported positive case represented 14-28 under-reported cases. Lineages P.2, P.1, and B.1.1 were detected. Working from home was a protective factor against the infection, and clinical signs of fever, dry cough, and loss of taste or smell were associated with testing positive (p <0.05). A descriptive analysis of the indicators revealed that the entire population was susceptible to the disease. Intensified vaccination strategies are required regardless of socioeconomic factors, health conditions, and preventive measures. Implementation of objective, comprehensive, and efficient management tools to minimize the spread of COVID-19 in this municipality can serve as a model for other regions of Brazil.

Revolutionizing the Rental Services in Siargao Island: Basis for Developing an Online Vehicle Rental Management System

The research paper addresses the inefficiencies faced by tourists and local rental service providers in the vehicle rental sector of Siargao Islandin the Philippines. Despite the thriving tourism industry in the island, the current manual processes for vehicle rentals are fraught with challenges such as unreliable service, booking difficulties, and inefficient fleet management. This study aims bridge the gap between these traditional practices and modern technological solutions by proposing an online vehicle rental management system designed to streamline operations and enhance customer experience. Using a mixed-methods approach, the exploration integrates qualitative insights from ethnographic studies and interviews while quantitative data from the surveys targeting tourists, residents, and rental service providers. This methodology enables a thorough understanding of the operational challenges and user needs within the rental ecosystem. The findings revealed significant inadequacies in manual booking thus highlighting a strong demand for an automated and user-friendly online platform. The proposed system incorporates advanced system software to automate bookings, optimize fleet management, and provide real-time updates and notifications by enhancing operational efficiency, reducing errors, and improving service reliability. Additionally, it seeks to empower local rental businesses through provision with tools to manage their operations more effectively and compete in the digital age. The research underscores the potential of the proposed system to revolutionize rental services in Siargao Island by improving accessibility, convenience, and customer satisfaction. The future implementation of this online platform is expected to contribute significantly to the sustainable growth of Siargao's tourism industry, ultimately benefiting tourists, local businesses, and the broader community

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.

Study of Heat Transfer Characteristics of Heat Pipe Using Water as a Working Fluid

Heat pipes are efficient and versatile thermal management devices widely used in various applications to transfer heat. This study focuses on investigating the heat transfer characteristics of a heat pipe employing de-ionized water as the working fluid. The experiment involved analyzing the thermal performance, heat transport capacity, and operational limits of the heat pipe. The aim of the experiment is to assess the heat pipe's performance under different operating conditions, such as varying heat loads. The study aimed to understand the heat transfer limitations and advantages of water-based heat pipes, which can have practical implications for numerous applications, such as electronics cooling, solar thermal systems, and space technology

DC Power Boosting Circuit for Freestanding‐Sliding Triboelectric Nanogenerators with High Intrinsic Impedance and Multi‐Harmonic Output

AbstractThis study presents a freestanding sliding triboelectric nanogenerator (FS‐TENG) designed for low‐frequency motions (below 2 Hz). However, for practical applications, an efficient power management strategy is required to handle the harmonic outputs of the FS‐TENG. The high‐frequency signal is not sustainable for powering low‐power electronic applications. To address the issue, a novel direct current power supply circuit (DPS) is proposed that utilizes a double charge circuit (DCC) and a comb filtering circuit (CFC) to efficiently harness harmonic sources from the FS‐TENG. The direct power supply (DPS) outperforms traditional converters by reducing the impedance of the FS‐TENG and collecting the target harmonic sources, which facilitates a continuous power supply to the load. The results demonstrate that the DPS is capable of providing a continuous DC voltage of 2.2 V to a 10 MΩ load with minimal ripple (0.039%) at a low operating frequency of 0.625 Hz. Additionally, the practical application of a self‐powered temperature sensor is demonstrated to highlight the potential of FS‐TENG and DPS for low‐frequency energy harvesting solutions in real‐world scenarios.

Incorporating demand constraints into piecewise frontier models of public service provision

Evaluating the performance of public service providers is often complicated by the fact that they must choose input levels before demands for their services are known. We consider an even more complicated situation in which service providers have no opportunity to directly influence demands. This means that their predetermined inputs may be more than what is required to meet realised demands. In such cases, conventional measures of revenue efficiency will generally mis-classify rational and efficient managers as inefficient. We develop a more appropriate measure of revenue efficiency that accounts for exogenously-determined demands. We explain how data envelopment analysis (DEA) methods can be used to estimate our measure. The methodology is applied to hospital and health service networks in Queensland (Australia). We find that most of these networks were able to maximise the revenue they could obtain from their predetermined inputs.

Strategic Management of Financial and Economic Security: Modern Challenges and Threats in a Hyperdynamic External Environment

The main purpose of the article is to increase the efficiency of strategic management of the financial and economic security of a modern enterprise in the face of new challenges and threats. The object of the study is the system of financial and economic security of a modern company. The scientific task is to assess modern surpluses and threats in the system of ensuring the financial and economic security of an enterprise. The research methodology involves the use of modern methods of system analysis, pairwise comparison and hierarchical analysis to assess challenges and threats. As a result, a matrix of the most significant challenges and threats in the system of ensuring the financial and economic security of a modern enterprise was built. The innovativeness of the results obtained is revealed through the proposed methodological approach to assessing and streamlining challenges and threats in the system of ensuring the financial and economic security of a modern enterprise.

Effectiveness of Halal Traceability and Self-Declared Certification on Indonesian MSMEs Performance

This study aims to examine in depth the effectiveness of halal tracea-bility in Indonesia and identify the impact of self-declared halal certifi-cation on the performance of small and medium-sized enterprises (SMEs) in the country. The research method used is qualitative, which is reinforced by statistical analysis through survey data from the Popu-lix application as well as a comprehensive literature review. Data anal-ysis was conducted through various techniques, including regression tests, correlation tests, chi-square tests, and content analysis. The re-search findings confirm that the effectiveness of the halal tracking sys-tem significantly contributes to improving the resilience and competi-tiveness of MSMEs. Moreover, self-declared halal certification has been proven to increase consumer confidence in halal products and their integrity. This research makes an important contribution by high-lighting the urgency of integrating efficient halal supply chain man-agement practices, including reliable tracking systems and credible cer-tification. Furthermore, this research underscores the necessity of en-hancing consumer awareness of halal products, thereby facilitating in-formed purchasing decisions and fostering the growth of a more resili-ent halal ecosystem in Indonesia.

Optimization of battery/ultra‐capacitor hybrid energy storage system for frequency response support in low‐inertia microgrid

AbstractModern power system networks are under statutory obligations to integrate renewable energy sources (RES). The primary reason is to meet ever‐increasing energy demand and also to curtail environmental pollution by greenhouse gases. However, the higher penetration of RES has the tendency of reducing inertia of overall power system network. Consequently, frequency stability is affected and deviates beyond allowable permissible limits leading to power blackouts, load shedding, and even total system failure. To address the issues associated with reduced inertia, an optimal control of hybrid energy storage system (HESS) has been proposed. HESS is basically a combination of battery and ultracapacitor, where ultracapacitor addresses rapidly varying power component by mimicking inertia while the battery compensates long‐term power variations. Thus, the HESS is effectively controlled to compensate the loss of inertia by regulating its energy flow. For the purpose of improved efficiency and better power management of the HESS, an improvised particle swarm optimization (MPSO)‐based virtual inertia control design has been proposed. The proposed MPSO is utilized to tune the gains of bidirectional dc–dc converter in such a way that improves frequency nadir with faster response to transient disturbances. This proposed method is simulated in MATLAB and its merits are validated in real time using hardware in loop. On analysing of the results, it can be observed that frequency nadir is improved by 48.96% with significant reduction in rate of change of frequency in comparison to conventional particle swarm optimization.

Soqia-Advice: A Web-GIS Advisory Platform for Efficient Irrigation in Arboriculture

The determination of water requirements for crops holds a crucial role in optimizing irrigation and enhancing agricultural productivity. However, identifying these needs remains a significant challenge due to the variety of factors influencing this decision, such as meteorological conditions, soil structure, and the phenological stages of each crop. In this study, we propose the design and development of a dedicated web-based irrigation advisory platform for arboriculture named ‘Soqia-Advice’. This platform will provide services to farmers, advisors, and decision-makers. The proposed methodology is based on four main steps: (1) need assessments; (2) definition of functionalities to fulfill these needs; (3) design of the overall architecture and the conceptual data model; and (4) implementation of key features of the module dedicated to farmers. The prototype of the “Farmer” module was tested on a farm in Azrou city, Morocco, as a case study. Seven-day weather forecasts were seamlessly integrated using the Weatherbit API. Additionally, the irrigation schedule was accurately displayed, ensuring efficient water management. Functionality tests were conducted on each menu to ensure the seamless and reliable operation of all planned features. The results were rigorously assessed to ensure that each feature aligned with the identified needs.

A novel approach for state-of-charge estimation of lithium-ion batteries by quasi-static component generation of ultrasonic waves

Lithium-ion batteries content complex internal components, such as porous media and electrolytes, which result in strong scattering and high attenuation of ultrasonic waves in these batteries. The low attenuative feature of the quasi-static components (QSCs) of ultrasonic waves offers great potential for nondestructive assessment of highly attenuating and porous materials. This paper presents an innovative approach for estimating the state-of-charge (SOC) of lithium-ion batteries using QSC of ultrasonic waves. Experimental results demonstrate a clear and repeatable linear relationship between the amplitudes of the generated QSC and the SOC of lithium-ion batteries. In addition, the relationships between different SOCs of the battery and the conventional linear ultrasonic parameters, second harmonic generation (SHG), and the QSC were compared to verify the improved sensitivity of the proposed approach. Notably, compared to linear ultrasonic features and the SHG, the generated QSC shows much higher sensitivity to the variations of SOC. We employ the phase-reversal method to further enhance the signal-to-noise ratio of measured QSC signals. The experimental results demonstrate that the proposed method exhibits a heightened sensitivity to changes in the SOC of batteries, resulting in significantly enhanced detection accuracy and resolution. This method effectively addresses the deficiencies observed in the current detection methods such as limited accuracy and sluggish response times. This method provides a new solution to overcome this challenge. Meanwhile, it also confirms that nonlinear ultrasound promises an alternative method for SOC assessment, providing a foundation for efficient and safe battery management practices.