Potential Of System Research Articles

Electrochemically induced release of a model dye from a pH-sensitive and electroactive microgel monolayer attached to an electrode surface

We present a novel electrochemically-induced release system featuring a pH-responsive and electroactive microgel monolayer specifically designed to enhance drug delivery mechanisms. This microgel, synthesized from acrylic acid and a ferrocene derivative, was formed on a conductive electrode surface, providing a robust platform for controlled release applications. The physical and chemical properties of the microgel were meticulously characterized using dynamic light scattering (DLS), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), nuclear magnetic resonance (NMR), and electrochemical techniques. The microgel particles modified the electrode surface by forming a monolayer through interactions between the sulfur present in the crosslinking agent and the gold surface of the electrode. Cyclic voltammetry was used to analyze the properties of the modified electrode, and the morphology of the monolayer was confirmed by scanning electron microscopy (SEM). Notably, we demonstrated that the incorporation of positively charged crystal violet (CV) dye molecules was facilitated through electrostatic interactions with the negatively charged carboxylic groups of the microgel, resulting in effective dye retention. Our investigation revealed that the electrochemical oxidation of the ferrocene groups significantly weakens the interactions between the crystal violet (CV) dye and the polymer chains within the microgel network, facilitating controlled dye release. The release process was successfully monitored using quartz crystal microbalance with dissipation monitoring (QCM-D), confirming the system's potential for innovative drug delivery platforms and implant construction.

Enhancing Electronic Nose Performance for Differentiating Civet and Non‐Civet Roasted Bean Coffee Using Polynomial Feature Extraction Methods

ABSTRACTCoffee, a popular beverage worldwide, requires thorough quality assessment to ensure its authenticity and meet consumer demands. Traditional methods in the industry are often subjective, expensive, and time‐consuming. This study used a compact, portable electronic nose (e‐nose) with machine learning models to classify and distinguish between civet and non‐civet roasted beans. The polynomial feature extraction method was used to extract important parameters from the sensor response and improve system performance. Classification models like linear discriminant analysis (LDA), logistic regression (LR), quadratic discriminant analysis (QDA), and support vector machines (SVM) were applied to classify the samples. Among these, the LDA model with polynomial features yielded the highest validation and test accuracies, with values of 0.89 ± 0.04 and 0.93, respectively. This was higher than the statistical feature methods, which obtained validation and test accuracies of 0.80 ± 0.07 and 0.87, respectively. The acquired e‐nose results were correlated with compound concentrations in roasted coffee beans measured by gas chromatography–mass spectrometry (GC–MS). These findings demonstrate the e‐nose system's promising potential to effectively distinguish civet from non‐civet roasted coffee beans based on their aroma profiles using polynomial feature extraction methods.

Optimal energy management strategy for electric vehicle charging station based on tied photovoltaic system

The rise of carbon dioxide emissions is a leading contributor to environmental pollution, impacting both human health and the planet. A promising solution is the integration of green energy and electric vehicles (EVs), which reduce dependence on fossil fuels. This paper introduces a novel energy management strategy to optimize energy flow and schedule EV battery charging at a solar-powered charging station. The system, installed at the University of Trieste, Italy, combines photovoltaic (PV) energy with grid power to reduce grid reliance. Using real-time data—such as EV presence, energy demand, available PV power, and battery status—the proposed method prioritizes maximizing PV energy usage while minimizing grid consumption. Unlike traditional methods, this strategy simplifies decision-making through a rule-based approach that eliminates the need for energy forecasting. Simulation results show the proposed strategy effectively optimizes energy usage, reduces grid consumption, protects battery life, and supports the main grid. The findings highlight the system's potential to improve energy efficiency and sustainability.

Design of an Intelligent Computing-based Information System for Automated Decision Making

The rapid growth of information technology has increased the need for intelligent computing-based information systems across sectors, such as business, education, and government, to facilitate quick and accurate decision-making. Previous research primarily focused on data analysis without a seamless integration for automated decision support. This study aims to bridge this gap by designing an information system that leverages machine learning algorithms for automated decision-making. The system incorporates artificial intelligence and big data processing to provide accurate recommendations based on historical and real-time data patterns. Key processes include identifying user needs, selecting suitable algorithms, developing predictive models, and integrating them into a user-friendly, web-based platform. Results indicate that the intelligent system significantly enhances decision-making speed and accuracy, particularly in scenarios demanding real-time analysis. Tests with decision trees and neural network algorithms demonstrate the system's reliability and adaptability to various data types, supporting consistent, data-driven outcomes. This research concludes by highlighting the system's potential to address complex data challenges, enabling efficient decision-making in dynamic environments.

Abstract A006: Development of a circulating tumor cell-derived cancer organoid platform for drug screening against metastatic tumors and longitudinal monitoring of drug resistance

Abstract Metastatic disease remains the primary cause of cancer-related deaths, presenting a significant challenge in oncology drug development. Current drug screening efforts often rely on cell lines or primary tumor-derived models, which may not adequately mimic the behavior of metastatic tumors. To address this, we aimed to develop a system that more accurately reflects metastatic tumors for improved drug screening. We leveraged a specialized culture platform featuring patented coated plates and proprietary media developed by AcroCyte (Taiwan) to cultivate circulating tumor cells (CTCs) into cancer organoids without pre-selection or enrichment. Building on our previous work, where we successfully cultivated CTCs from fresh blood and cryopreserved peripheral blood mononuclear cells (PBMCs) from renal, colon, and lung cancer patients into cancer organoids, we have now demonstrated the ability to freeze these CTC- derived organoids and thaw them for new culture when needed. We also present new data extending this success to breast and prostate cancers. CTCs from five frozen PBMC samples from breast cancer patients and three from prostate cancer patients formed visible colonies, with positive immunostaining for EpCAM, cytokeratin, and CD44. The immune cells in the PBMC samples gradually died off within three weeks of culture, as confirmed by the absence of CD45- positive cells. Interestingly, CTC-derived organoids from breast and prostate cancers did not form the spheroids observed in renal, colon, and lung cancer models. Instead, they appeared as colonies or clusters of CTCs. After 6-8 weeks of active growth, these clusters exhibited a slower growth rate, although they remained viable in the proprietary culture system. We are actively evaluating various culture media supplements, tailored to breast and prostate cancers, to extend the active growth period of these CTC-derived organoids for longer durations. Importantly, these CTC-derived colonies retained drug sensitivity, demonstrating the system's potential for effective drug screening against metastatic tumors. Our platform now supports organoid development from renal, colon, lung, breast, and prostate cancers, and we are actively expanding to include all major solid tumors. Since this technology uses blood, which can be obtained non-invasively, it enables longitudinal monitoring of CTCs as a functional liquid biopsy. Additionally, frozen PBMCs can be used as a source material for CTCs, and the resulting organoids can be utilized for drug sensitivity testing. This platform could be instrumental in oncology drug development and holds promise for monitoring emerging drug resistance in clinical trials by analyzing PBMC samples collected before and after treatment, offering a functional liquid biopsy-based approach to aid in developing therapies targeting metastatic disease. Citation Format: Yi-chun Han, David Hsieh, Ruobo Zhang, Emily Miyashita-Lin, Shian-Jiun Shih. Development of a circulating tumor cell-derived cancer organoid platform for drug screening against metastatic tumors and longitudinal monitoring of drug resistance [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A006.

Kinetic and Mechanistic Release Studies on Hyaluronan Hydrogels for Their Potential Use as a pH-Responsive Drug Delivery Device.

Hyaluronic acid, a biocompatible polymer, holds significant potential for drug delivery applications. Its variable degree of protonation, which entails tunable physical properties, makes it an ideal candidate for developing pH-sensitive hydrogels. Like other smart drug delivery systems, pH-responsive hydrogels can enhance medical treatment and expedite the healing process. However, the inherent complexity of hydrogels poses challenges in identifying suitable matrix systems. This study evaluates the potential of thiolated hyaluronic acid hydrogels, physically cross-linked with deacetylated disaccharide units of the polymer, for use in drug delivery. Using low-molecular-weight dextrans as model drugs, we investigated the system's response to different pH environments in terms of swelling as well as the kinetic and mechanistic release of the encapsulated compound. The data suggest tunable release properties of the gel regarding drug size and pH value. Our results demonstrate the gel system's potential for smart drug delivery. We anticipate that this system is a promising candidate for use in transdermal wound healing applications and strongly encourage further investigations using other sorts of (model) drugs to gain a more detailed insight into its pH-responsive transport qualities.

High-sensitivity pump-probe spectroscopy with a dual-comb laser and a PM-Andi supercontinuum.

Amplifier-based pump-probe systems, while versatile, often suffer from complexity and low measurement speeds, especially when probing samples require low excitation fluences. To address these limitations, we introduce a pump-probe system that leverages a 60-MHz single-cavity dual-comb oscillator and an ultra-low noise supercontinuum. The setup can operate in equivalent time sampling or in programmable optical delay generation modes. We employ this system to study the wavelength-dependent excited-state dynamics of the non-fullerene electron acceptor Y6, a compound of interest in solar cell development, with excitation fluences as low as 1 nJ/cm2, well below the onset of nonlinear exciton annihilation effects. Our measurements reach a shot-noise limited sensitivity in differential transmission of 3.4·10-7. The results demonstrate the system's potential to advance the field of ultrafast spectroscopy.

Design of PV Battery Hybrid Inverter.

This research explores a hybrid renewable energy system integrating solar and additional renewable sources with energy storage for a resilient, eco-friendly power solution. Through adaptive control algorithms, the system autonomously optimizes energy flow based on environmental and grid conditions, ensuring uninterrupted power. Simulations under varying demand scenarios demonstrate enhanced reliability and a reduced carbon footprint. The findings highlight the system's potential as a sustainable model for future energy infrastructure.

Multi-Thermal Recovery Layout for a Sustainable Power and Cooling Production by Biomass-based Multi-generation System: Techno-Economic-Environmental Analysis and ANN-GA Optimization

This paper presents a ground-breaking design for a multigeneration system capable of simultaneously producing electricity, hydrogen, and cooling loads. This research advances sustainable energy systems by introducing an innovative design that optimally utilizes waste heat and integrates biomass gasification with advanced thermodynamic cycles. It also provides a model for future studies on carbon emission reduction and improved efficiency. The proposed system effectively harnesses waste heat from the Brayton cycle to drive the supercritical carbon dioxide cycle, steam Rankine cycle, absorption refrigeration, and proton exchange membrane electrolyzer. This approach improves overall efficiency and offers a promising solution for integrated energy generation. Additionally, employing the sCO2 cycle provides high thermal efficiency, cost-effectiveness, and lower environmental impacts compared to traditional power generation methods. Extensive evaluations, including techno-economic and environmental analyses, confirm the system's practicality and potential for future commercial application. Additionally, a parametric investigation of five essential design parameters provides important insights into the system's performance and flexibility. Analysing the proposed system determined that the gasifier-Bryton unit has the highest irreversibility and cost rate among other subsystems. A novel approach combining artificial neural networks (ANN) with a non-dominated sorting genetic algorithm II (NSGA-II) has been developed to optimize the system, substantially reducing computational time and costs associated with system performance analysis. According to the three-objective optimization, the system in the optimal operating mode provides 45.89 kg/h of hydrogen with an exergy efficiency of 33.15% and a total cost rate of 159.5 $/h. After the optimization process, significant achievements have been observed, including a 5.02% improvement in exergy efficiency, an increase of 7.29 kg/h of hydrogen production, and a decrease of 0.1 ton/MW in the CO2 emission index.

Virtual Roaming of Cultural Heritage Based on Image Processing

ABSTRACTWith the digital protection and development of cultural heritage as a focus, an analysis of the trends in cultural heritage digitization reveals the importance of digital technology in this field, as demonstrated by the application of virtual reality (VR) to the protection and development of the Lingjiatan site. The implementation of the Lingjiatan roaming system involves sequential steps, including image acquisition, image splicing, and roaming system production. A user test was conducted to evaluate the usability and user experience of the system. The results show that the system operates normally, with smooth interactive functions that allow users to tour the Lingjiatan site virtually. Users can learn about Lingjiatan's culture from this virtual environment. This study further explores the system's potential for site preservation and development, and its role in the integration of cultural heritage and tourism.

Bridging Human and Machine Intelligence: Reverse-Engineering Radiologist Intentions for Clinical Trust and Adoption

In the rapidly evolving landscape of medical imaging, the integration of artificial intelligence (AI) with clinical expertise offers unprecedented opportunities to enhance diagnostic precision and accuracy. Yet, the "black box" nature of AI models often limits their integration into clinical practice, where transparency and interpretability are important. This paper presents a novel system leveraging the Large Multimodal Model (LMM) to bridge the gap between AI predictions and the cognitive processes of radiologists. This system consists of two core modules, Temporally Grounded Intention Detection (TGID) and Region Extraction (RE). The TGID module predicts the radiologist's intentions by analyzing eye gaze fixation heatmap videos and corresponding radiology reports. Additionally, the RE module extracts regions of interest that align with these intentions, mirroring the radiologist’s diagnostic focus. This approach introduces a new task, radiologist intention detection, and is the first application of Dense Video Captioning (DVC) in the medical domain. By making AI systems more interpretable and aligned with radiologist’s cognitive processes, this proposed system aims to enhance trust, improve diagnostic accuracy, and support medical education. Additionally, it holds the potential for automated error correction, guiding junior radiologists, and fostering more effective training and feedback mechanisms. This work sets a precedent for future research in AI-driven healthcare, offering a pathway towards transparent, trustworthy, and human-centered AI systems. We evaluated this model using NLG(Natural Language Generation), time-related, and vision-based metrics, demonstrating superior performance in generating temporally grounded intentions on REFLACX and EGD-CXR datasets. This model also demonstrated strong predictive accuracy in overlap scores for medical abnormalities and effective region extraction with high IoU(Intersection over Union), especially in complex cases like cardiomegaly and edema. These results highlight the system's potential to enhance diagnostic accuracy and support continuous learning in radiology. We are also releasing the source code for our project, available here.

Fault Detection in Transmission Line

Abstract: This project focuses on the design and implementation of an advanced fault detection system for overhead transmission lines, utilizing key components such as the ESP 8266 microcontroller, current sensors, a GPS module, and IoT integration. The system is capable of detecting and classifying four major fault types: single line to ground (L-G), line to line (LL), double line to ground (L-L-G), and three-phase faults (L-L-LG). By placing current sensors at the transmission line's sending end, the system identifies faults and accurately determines their location through GPS coordinates .Simulations using protect us software were conducted to verify the system’s performance prior to building a hardware prototype. Upon fault detection, the system provides alerts via an LCD display and a buzzer, while real-time fault data is transmitted to an Android app and web server through the arduino IoT cloud. Testing confirmed the system’s precision in fault identification and location tracking, ensuring timely notifications for control room and remote device operators. This successful implementation highlights the system's potential for improving safety and efficiency in power transmission networks. the project lays the groundwork for further exploration into advanced fault detection and real-time monitoring solutions in electrical engineering.

Thermal Bed Design for Temperature-Controlled DNA Amplification Using Optoelectronic Sensors.

Loop-Mediated Isothermal Loop-Mediated Isothermal Amplification (LAMP) is a widely used technique for nucleic acid amplification due to its high specificity, sensitivity, and rapid results. Advances in microfluidic lab-on-chip (LOC) technology have enabled the integration of LAMP into miniaturized devices, known as μ-LAMP, which require precise thermal control for optimal DNA amplification. This paper introduces a novel thermal bed design using PCB copper traces and FR-4 dielectric materials, providing a reliable, modular, and repairable heating platform. The system achieves accurate and stable temperature control, which is critical for μ-LAMP applications, with temperature deviations within ±1.0 °C. The thermal bed's performance is validated through finite element method (FEM) simulations, showing uniform temperature distribution and a rapid thermal response of 2.5 s to reach the target temperature. These results highlight the system's potential for applications such as disease diagnostics, biological safety, and forensic analysis, where precision and reliability are paramount.

Integration of renewable energy-powered cold storage solutions for reducing post-harvest food waste in rural agricultural areas

Post-harvest food loss remains a critical challenge in rural agricultural areas, exacerbated by inadequate storage facilities and unreliable energy access. This study develops and optimizes an advanced renewable energy-powered cold storage system tailored for rural settings, integrating solar and wind energy with phase change materials (PCMs) for efficient energy storage. The system incorporates Internet of Things (IoT)-based sensors and artificial intelligence (AI)-driven energy management to maintain optimal storage conditions and enhance energy efficiency. Field trials conducted in Lincolnshire, UK, and Appalachian regions of the US demonstrated significant reductions in post-harvest food loss by an average of 43.5%, extensions in produce shelf-life by up to 300%, and increased income for smallholder farmers by approximately 43%. The system also achieved an 80% reduction in greenhouse gas emissions compared to conventional diesel-powered systems. Economic analyses revealed a shorter payback period and higher return on investment, confirming the system's viability. High user satisfaction and adoption rates indicate the system's practicality and potential for widespread implementation. The findings suggest that integrating renewable energy with smart technologies in cold storage solutions offers a scalable and sustainable approach to enhancing food security, promoting economic growth in rural areas, and supporting environmental objectives globally.

The capture of circulating tumor cells by Labyrinth system as a tool for early stage lung cancer detection.

We focus on utilizing the Labyrinth system for the detection of circulating tumor cells (CTCs) in patients with lung nodules. Our aim is to evaluate CTCs isolated through the Labyrinth system as a biomarker for early-stage lung cancer (LC) detection. 167 patients with low dose computed tomography (LDCT) diagnostic results for lung nodules and 31 healthy volunteers (HV) were enrolled. Blood samples were processed for CTC detection. LDCT positive (LDCT+) patients underwent surgery and were categorized into those with LC and those with benign lung diseases (BLD) based on their biopsy results. BLD Patients, LDCT negative (LDCT-) patients and HV served as controls. The correlation of CTC counts with LC, BLD, LDCT- and HV was investigated. Receiver operating characteristic (ROC) curves were used to assess the Labyrinth system's diagnostic potential for early-stage LC. Median CTC counts for LC, BLD, LDCT- and HV were 2.7 CTC/mL, 0.6 CTC/mL, 0.4 CTC/mL, 0 CTC/mL, respectively. Statistical analysis indicated CTC counts could distinguish LC from BLD, LDCT- and HV (p-Values < 0.001). Using a cut-off of 1 CTC/mL, the study showed 84.4% sensitivity and 82.4% specificity for LDCT+ patients. Specificity increased to 85.9% for patients with lung nodules and 88.2% for all participants. In conclusion, CTCs detected by the Labyrinth system can serve as a biomarker for early-stage LC detection for patients with lung nodules. CTCs identified by the Labyrinth system are a promising biomarker for early-stage LC detection in clinical practice.

Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction.

The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system. This system combines a PFM contraction detector with a maximal PFM contraction performance classifier. The contraction detector first identifies whether or not a PFM contraction was performed. Then, the contraction classifier autonomously quantifies the quality of maximal PFM contractions across different features, which are also combined into an overall rating. Both algorithms are based on artificial intelligence (AI) methods. The detector relies on a convolutional neural network, while the contraction classifier uses a custom feature extractor followed by a random forest classifier to predict the strength rating based on the modified Oxford scale. The AI algorithms were trained and tested using datasets measured by vaginal dynamometry, combined in some cases with digital assessment results from expert physiotherapists. The contraction detector was trained on one dataset and then tested on two datasets measured with different dynamometers, achieving 97% accuracy on the first dataset and 100% accuracy on the second. For the contraction performance classifier, the results demonstrate that important clinical features can be extracted automatically with an acceptable error. Furthermore, the contraction classifier is able to predict the strength rating within a ±1 scale point with 97% accuracy. These results demonstrate the system's potential to enhance PFM training and rehabilitation by enabling women to monitor and improve their PFM contractions autonomously.

Seawater alkalization via an energy-efficient electrochemical process for CO2 capture

Electrochemical pH-swing strategies offer a promising avenue for cost-effective and energy-efficient carbon dioxide (CO2) capture, surpassing the traditional thermally activated processes and humidity-sensitive techniques. The concept of elevating seawater's alkalinity for scalable CO2 capture without introducing additional chemical as reactant is particularly intriguing due to its minimal environmental impact. However, current commercial plants like chlor-alkali process or water electrolysis demand high thermodynamic voltages of 2.2 V and 1.23 V, respectively, for the production of sodium hydroxide (NaOH) from seawater. These high voltages are attributed to the asymmetric electrochemical reactions, where two completely different reactions take place at the anode and cathode. Here, we developed a symmetric electrochemical system for seawater alkalization based on a highly reversible and identical reaction taking place at the anode and cathode. We utilize hydrogen evolution reaction at the cathode, where the generated hydrogen is looped to the anode for hydrogen oxidation reaction. Theoretical calculations indicate an impressively low energy requirement ranging from 0.07 to 0.53 kWh/kg NaOH for established pH differences of 1.7 to 13.4. Experimentally, we achieved the alkalization with an energy consumption of 0.63 kWh/kg NaOH, which is only 38% of the theoretical energy requirements of the chlor-alkali process (1.64 kWh/kg NaOH). Further tests demonstrated the system's potential of enduring high current densities (~20 mA/cm2) and operating stability over an extended period (>110 h), showing its potential for future applications. Notably, the CO2 adsorption tests performed with alkalized seawater exhibited remarkably improved CO2 capture dictated by the production of hydroxide compared to the pristine seawater.

Recent advances in mRNA-based cancer vaccines encoding immunostimulants and their delivery strategies

The high prevalence of drug resistance, relapse, and unfavorable response rate of conventional cancer therapies necessitate the development of more efficient treatment modalities. Immunotherapy represents a novel therapeutic approach to cancer treatment in which the immune system's potential is harnessed to recognize and eliminate tumor cells. mRNA cancer vaccines, as a burgeoning field of immunotherapy, have recently drawn particular attention, and among mRNAs encoding tumor-associated antigens, tumor-specific antigens, and immune stimulatory factors, the latter has been relatively less explored. These immunostimulatory mRNAs encode a range of proteins, including stimulatory ligands, receptors, enzymes, pro-inflammatory cytokines, and inhibitory binding proteins, which collectively augment the host immune system's ability against cancerous cells. In this review, we aimed to provide a comprehensive account of mRNA-based cancer vaccines encoding immune stimulants, encompassing their current status, mechanisms of action, delivery strategies employed, as well as recent advances in preclinical and clinical studies. The potential challenges, strategies and future perspectives have also been discussed.

Solar-enhanced DCMD: Dual-tank system for superior efficiency and viability

ABSTRACT This study investigates a novel solar-powered direct contact membrane distillation (DCMD) system designed to significantly enhance desalination efficiency and economic feasibility. The system features a dual-tank configuration integrated with a solar collector, which optimizes thermal energy storage and ensures consistent feedwater temperatures. Therefore, continuous operation is possible despite fluctuations in solar radiation. An assessment of the system's performance under various solar radiation conditions predicted an average daily permeate flux of 14.4 kg/m2 and a specific energy consumption of 1810 kJ/kg, indicating superior thermal efficiency. The economic analysis revealed a competitive levelized water cost of $25.75 per cubic meter, highlighting the system's potential as a viable alternative to conventional desalination technologies. These findings suggest that the proposed DCMD system is a leading technology in the pursuit of sustainable desalination. Considering its thermal management and cost-effectiveness, the system presents a transformative solution for sustainable freshwater production in regions with abundant solar resources.

Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications.

Background: Recent years have seen a surge of interest in dual-modality imaging systems that integrate functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to probe brain function. This review aims to explore the advancements and clinical applications of this technology, emphasizing the synergistic integration of fNIRS and EEG. Methods: The review begins with a detailed examination of the fundamental principles and distinctive features of fNIRS and EEG techniques. It includes critical technical specifications, data-processing methodologies, and analysis techniques, alongside an exhaustive evaluation of 30 seminal studies that highlight the strengths and weaknesses of the fNIRS-EEG bimodal system. Results: The paper presents multiple case studies across various clinical domains-such as attention-deficit hyperactivity disorder, infantile spasms, depth of anesthesia, intelligence quotient estimation, and epilepsy-demonstrating the fNIRS-EEG system's potential in uncovering disease mechanisms, evaluating treatment efficacy, and providing precise diagnostic options. Noteworthy research findings and pivotal breakthroughs further reinforce the developmental trajectory of this interdisciplinary field. Conclusions: The review addresses challenges and anticipates future directions for the fNIRS-EEG dual-modal imaging system, including improvements in hardware and software, enhanced system performance, cost reduction, real-time monitoring capabilities, and broader clinical applications. It offers researchers a comprehensive understanding of the field, highlighting the potential applications of fNIRS-EEG systems in neuroscience and clinical medicine.