Precise Parameters Research Articles

Extrinsic calibration method for integrating infrared thermal imaging camera and 3D LiDAR

PurposeIn the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.Design/methodology/approachPrior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.FindingsThe average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.Originality/valueThis paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

Combining finite-time control and prescribed tracking performance for uncertain PMSM driven steer-by-wire system with unknown disturbance

To solve the uncertainties in permanent magnet synchronous motor (PMSM) driven steer-by-wire (SbW) system, a prescribed-performance-control (PPC) based finite-time fuzzy controller is proposed. Specifically, the dynamics of SbW system driven by PMSM is analyzed, and combine PPC technology to transform the tracking error coordinates. Then, the velocity signal is obtained from the sigmoid function-based tracking differentiator by using the position signal. To solve the problem of mismatched disturbance, fuzzy logic system (FLS) are used as a universal approximator to estimate the lumped nonlinear friction and other unknown functions, the boundary of lumped disturbance is estimated by the designed adaptive updating law. By fusing the designed virtual controller and the intermediate control law, the controller needs no precise parameters of system, under which the effect of uncertainty of control gain can be compensated completely. Finally, rigorous theoretical analysis based on the Lyapunov stability theory is provided to demonstrate the finite-time stability of the closed-loop system under consideration. Simulation and experiment results are presented to show the effectiveness of the developed control approach, and some comparisons are given to show the rapid and accurate position tracking control performance.

Review of transcutaneous electrical acupoint stimulation and related devices

Abstract A review was undertaken of the operation process and development of transcutaneous electrical acupoint stimulation (TEAS) and related devices for TEAS, with the aim to offer a reference for developing an international standard for the basic safety and essential performance of the devices. The articles related to TEAS and instruction of devices for TEAS were searched using the EMBASE, MEDLINE, and Web of Science databases with the time period from inception to July 18, 2023. In the absence of a parameter description of the stimulators, a multimeter was used to measure the output voltage, resistance, and current. Thirty-two related devices for TEAS were obtained. The safety parameters of most devices were neither clearly defined, nor standardized, and in some cases were missing. There was a noticeable disparity in the upper safety limits of the output current among the devices. The sizes of the skin electrode pads as well as the lengths of the electrode connecting wires of most devices were not clearly indicated. Acupoints on different parts of the human body, including the upper limbs, head, auricle, chest, abdomen, trunk, and lower limbs, required different maximum tolerable current intensities and current densities. It is important to indicate comprehensive output/safety parameters and essential performance for devices for TEAS to meet the need of global distribution, achieve precise stimulation parameters at different acupoints across the human body, and allay any safety concern of national therapeutic device authorities, the regulators, manufacturers, and end users.

In-depth simulation of rainfall–runoff relationships using machine learning methods

ABSTRACT Measurement inaccuracies and the absence of precise parameters value in conceptual and analytical models pose challenges in simulating the rainfall–runoff modeling (RRM). Accurate prediction of water resources, especially in water scarcity conditions, plays a distinctive and pivotal role in decision-making within water resource management. The significance of machine learning models (MLMs) has become pronounced in addressing these issues. In this context, the forthcoming research endeavors to model the RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, and Multivariate Adaptive Regression Splines (MARS). The simulation was conducted within the Malwathu Oya watershed, employing a dataset comprising 4,765 daily observations spanning from July 18, 2005, to September 30, 2018, gathered from rainfall stations, and Kappachichiya hydrometric station. Of all input combinations, the model incorporating the input parameters Qt−1, Qt−2, and R̄t was identified as the optimal configuration among the considered alternatives. The models' performance was assessed through root mean square error (RMSE), mean average error (MAE), coefficient of determination (R2), and developed discrepancy ratio (DDR). The GEP model emerged as the superior choice, with corresponding index values (RMSE, MAE, R2, DDRmax) of (43.028, 9.991, 0.909, 0.736) during the training process and (40.561, 10.565, 0.832, 1.038) during the testing process.

VALIDASI METODE PENETAPAN KADAR VITAMIN C EKSTRAK DAUN KEMANGI (Ocimum sanctum L.) DENGAN METODE SPEKTROFOTOMETRI UV-VIS

Basil leaves (Ocimum sanctum L.) are a type of wild plant that easy to find. Basil is known to be analgesic, antihyperlipid and has antioxidant properties. Besides, basil is also known to treat stomach and liver diseases. Components known to be found in basil plants include flavonoids, alkaloids, saponins, triterpenoids, steroids, tannins, phenols and vitamin C, that is needed by a person to be able to carry out metabolism properly and develop normally. The aim of the research to validate the determination of vitamin C levels from basil leaf extract. The basil leaf filtrate was identified for vitamin C by a color reaction and then the levels were determined by UV-Vis spectrophotometry which was previously validated with the parameters of accuracy, precision, linearity and selectivity. According to the research results, it is known that the accuracy test meets the percent recovery range in the range of 98-102%, the precision test produces an RSD of less than 2%, the linearity test produces a correlation value (r) = 0.9986 and the selectivity test is good. The method for determining vitamin C levels by Uv-Vis spectrophotometry was validated on parameters that met the requirements of 0.0128% w/w.

Sprayable hydrogel with optical mRNA nanosensors for Real-Time monitoring and healing of diabetic wounds

Sprayable hydrogels offer a promising approach for treating diabetic wounds. They possess the capability to conform to irregular surfaces, create a moist environment that supports the growth of new tissue, and offer protection against infections. Yet, applying previously reported sprayable hydrogels has proven to be challenging, often necessitating the use of specialized equipment or the simultaneous injection of multiple components. In this study, we develop a user-friendly therapeutic hydrogel that can be applied using a general spray bottle. This hydrogel is formulated using the bioactive agent, LL37, which is known for its antimicrobial properties and ability to enhance angiogenesis. Additionally, optical mRNA nanosensors, NanoFlares, were incorporated into the hydrogel to measure the expression of proliferation and inflammation biomarkers, providing precise molecular-based parameters for evaluating diabetic wound status. The efficacy of LL37- and NF-incorporated hydrogels in promoting in vivo wound healing in normal and diabetic mice is evaluated, and a Wound Healing Index (WHI) is presented to assist medical professionals in making informed decisions about wound care and treatment. Overall, our study demonstrates the potential of sprayable hydrogels for promoting diabetic wound healing and monitoring the diabetic wound status, which could have significant potential for future clinical applications.

QbD Approach to Method Development, Validation and Degradation Profiling of Antifungal Drugs by RP-HPLC

For the simultaneous estimation of Itraconazole (ITZ) and Terbinafine hydrochloride (TBF HCl) in tablet dosage form, a simple, novel, cost effective and rapid RP-HPLC method was developed. Method development becomes quick, accurate, sensitive, and robust by using quality by design (QbD). RP-HPLC was performed using a C18 (agilent, 4.6 x 250mm, 5µm particle size) column. Methanol and 0.05% OPA (pH 3.5) was used as mobile phase in 84:16 ratio. The analysis was conducted in gradient mode with a flow rate of 0.95mL/min. Detection wavelength was set at 270nm. Retention time for TBF and ITZ was at 2.93 and 6.96min, respectively. The accuracy, precision, repeatability, linearity, LOD, LOQ and robustness parameters have been validated as per the ICH Q2 (R1) guideline: Validation of analytical procedures–text and methodology. Values for LOD and LOQ were found to be 0.131 and 0.398µg/mL and 0.350 and 1.061µg/mL respectively for ITZ and TBF HCl. Thus, the technique can be employed for regular QC analysis of bulk ITZ and TBF HCl and their pharmaceutical formulations. Degradative stress conditions like acid, base, neutral, oxidation applied and degradations were studied.

Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm.

To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive models. A cross-sectional study was conducted among 309 older patients aged ≥ 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two models. The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of 0.909. This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS. The identified influencing factors can provide critical insights for the development of early prevention and intervention strategies to reduce delay in medical decisions-making among older patients with AIS and promote patients' health. The LightGBM algorithm is the optimal model for predicting the delay in medical decision-making among older patients with AIS.

Making Predictions Using Poorly Identified Mathematical Models

Many commonly used mathematical models in the field of mathematical biology involve challenges of parameter non-identifiability. Practical non-identifiability, where the quality and quantity of data does not provide sufficiently precise parameter estimates is often encountered, even with relatively simple models. In particular, the situation where some parameters are identifiable and others are not is often encountered. In this work we apply a recent likelihood-based workflow, called Profile-Wise Analysis (PWA), to non-identifiable models for the first time. The PWA workflow addresses identifiability, parameter estimation, and prediction in a unified framework that is simple to implement and interpret. Previous implementations of the workflow have dealt with idealised identifiable problems only. In this study we illustrate how the PWA workflow can be applied to both structurally non-identifiable and practically non-identifiable models in the context of simple population growth models. Dealing with simple mathematical models allows us to present the PWA workflow in a didactic, self-contained document that can be studied together with relatively straightforward Julia code provided on GitHub. Working with simple mathematical models allows the PWA workflow prediction intervals to be compared with gold standard full likelihood prediction intervals. Together, our examples illustrate how the PWA workflow provides us with a systematic way of dealing with non-identifiability, especially compared to other approaches, such as seeking ad hoc parameter combinations, or simply setting parameter values to some arbitrary default value. Importantly, we show that the PWA workflow provides insight into the commonly-encountered situation where some parameters are identifiable and others are not, allowing us to explore how uncertainty in some parameters, and combinations of parameters, regardless of their identifiability status, influences model predictions in a way that is insightful and interpretable.

Bandgap accuracy and characteristics of fluid-filled periodic pipelines utilizing precise parameters transfer matrix method

The phononic crystal theory provides a novel approach for effectively controlling the bending vibrations in fluid-filled pipelines. This paper innovatively proposes the precise parameters transfer matrix method to investigate the band calculation accuracy and bandgap characteristics of fluid-filled periodic pipelines with various beam types. Firstly, the differential equations for bending vibration of fluid-filled pipelines are established based on deformation and force analysis. The parameters of the system state are precisely represented by the structural form of multiplying the constitutive matrix with the derivative matrix. Combined with Bloch's theorem, the novel precise parameters transfer matrix method for calculating the band structure is proposed. Secondly, the validity of this method is verified through a comparison with finite element simulation results. A detailed analysis is provided regarding the mechanism of bandgap formation and the effect of fluid filling on the band structure. Then, the influence of shear deformation, moment of inertia, and their coupling on the band calculation accuracy for fluid-filled periodic pipelines is studied based on various beam theories. Finally, it delves into the bandgap characteristics of fluid-filled periodic pipelines under different material parameters, structural parameters, and excitation conditions. This research offers valuable insights for the structural design and vibration damping application in fluid-filled periodic pipelines, providing theoretical support for accurately determining their bandgaps.

Controlled Insertion of Silver Nanoparticles in LbL Nanostructures: Fine-Tuning the Sensing Units of an Impedimetric E-Tongue

Silver nanoparticles (AgNPs) possess unique characteristics ideal for enhancing device sensitivity, primarily due to their high surface-to-volume ratio facilitating heightened interaction with analytes. Integrating AgNPs into polymers or carbon-based materials results in nanocomposites with synergistic properties, enabling the detection of minute changes in the environment across various applications. In this study, we investigate the adsorption kinetics of AgNPs within multilayered layer-by-layer (LbL) structures, specifically examining the impact of AgNPs concentration in the LbL film formation that is further explored as sensing units in an impedimetric microfluidic e-tongue. Although absorption kinetic studies are infrequent, they are crucial to optimize the AgNPs adsorption and distribution within LbL structures, significantly influencing upcoming applications. Through systematic variation of AgNPs concentration within identical LbL architectures, we applied the films as sensing units in a microfluidic e-tongue capable of distinguishing food enhancers sharing the umami taste profile. Across all tested scenarios, our approach consistently achieves robust sample separation, evidenced by silhouette coefficient, principal component analyses, and long-term stability. This work contributes to exploring controlled nanomaterial-based developments, emphasizing the importance of precise parameter control for enhanced sensor performance across diverse analytical applications.

An edge feature-based external calibration of monocular camera and LiDAR using in-orbit data

Multimodal perception represents a vital approach for observing non-cooperative targets in space. The accurate determination of the extrinsic parameters for monocular camera and LiDAR systems serves as the cornerstone for achieving effective space multimodal perception. However, due to factors like launch and operation, these extrinsic parameters can experience drift, thereby compromising the overall performance of the multimodal perception system. To tackle this challenge, this study introduces a novel approach: an edge feature-based external calibration method for monocular camera and LiDAR using in-orbit data. The proposed methodology involves several steps. Firstly, edges are extracted from the image, followed by distance transformation based on the edge image. The subsequent integration of the edge image and distance image yields the search scene image. Then, edge feature is extracted from the point cloud data. Finally, after constructing the objective function, genetic algorithms are used to determine precise external parameters. Simulation results underscore the effectiveness of the proposed algorithm, affirming its ability to achieve high-precision calibration results for external parameters even within intricate spatial environments.

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites’ redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS’s performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS’s positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites’ signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investigate ABC’s performance, the method was applied for the selection of an optimal GNSS satellite subset according to the number of total available tracked GNSS satellites (up to 31 satellites), leading to more than 300 million possible combinations of 15 GNSS satellites. ABC was able to select the optimal satellite subsets with 100% accuracy.

Enhancing secret key distribution through advanced color modulation in visible light communication

Visible light communication (VLC) has emerged as a dynamic area of research poised to revolutionize high-speed wireless communication. VLC technology uses light-emitting diodes (LEDs) within existing infrastructure to emit light within the visible spectrum. VLC complements traditional radio frequency (RF) communications, addressing its inherent limitations and drawbacks. To navigate the demands of modern urban environments, VLC systems must prioritize secure data transmission, accessibility, and economic feasibility, particularly within the framework of smart cities. We introduce what is to our knowledge a novel privacy-enhanced VLC system for optical wireless communication. Leveraging color data modulation techniques and the intricacies of a hyperchaotic three-dimensional map, this innovative approach ensures robust security. By employing diverse LED colors for data transmission and exploiting the unpredictable mathematical properties of hyperchaotic maps, enhanced privacy is achieved. The performance of the proposed system was rigorously evaluated through various tests, manipulating initial control parameters of the encryption process with the hyperchaotic map, as well as adjusting message length and content. Tests were conducted over a 1 m connection distance at a symbol transmission rate of 2 baud. Remarkably, the proposed system demonstrated high accuracy in message recovery, achieving a symbol error rate (SER) of only 0.02 at an incident optical power of 22 µW. We highlight the critical importance of precise decryption parameter values in the proposed method, demonstrating the necessity for accuracy within the range of 10−15 for each decryption parameter; it underscores the indispensability of meticulous parameter calibration to ensure the correct decryption of transmitted symbols. These results pave the way for applications where absolute security is imperative, particularly in smart city environments, such as for key distribution purposes.

Volt-VAR Control in Active Distribution Networks Using Multi-Agent Reinforcement Learning

With the advancement of power systems, the integration of a substantial portion of renewable energy often leads to frequent voltage surges and increased fluctuations in distribution networks (DNs), significantly affecting the safety of DNs. Active distribution networks (ADNs) can address voltage issues arising from a high proportion of renewable energy by regulating distributed controllable resources. However, the conventional mathematical optimization-based approach to voltage reactive power control has certain limitations. It heavily depends on precise DN parameters, and its online implementation requires iterative solutions, resulting in prolonged computation time. In this study, we propose a Volt-VAR control (VVC) framework in ADNs based on multi-agent reinforcement learning (MARL). To simplify the control of photovoltaic (PV) inverters, the ADNs are initially divided into several distributed autonomous sub-networks based on the electrical distance of reactive voltage sensitivity. Subsequently, the Multi-Agent Soft Actor-Critic (MASAC) algorithm is employed to address the partitioned cooperative voltage control problem. During online deployment, the agents execute distributed cooperative control based on local observations. Comparative tests involving various methods are conducted on IEEE 33-bus and IEEE 141-bus medium-voltage DNs. The results demonstrate the effectiveness and versatility of this method in managing voltage fluctuations and mitigating reactive power loss.

An optimization-free Fisher information driven approach for online design of experiments

Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One of the most challenging tasks in kinetic model identification is the precise estimation of unknown kinetic model parameters. This challenge can be effectively addressed through the application of Model-Based Design of Experiments (MBDoE) techniques, which enable the design of experiments facilitating precise model parameter estimation with minimal runs and analytical resources. Nevertheless, MBDoE techniques rely on an optimization procedure that is susceptible to parametric uncertainty, making the design procedure computationally intensive and prone to issues of local optimality. MBDoE techniques are also employed in online procedures to expedite the identification of kinetic models in autonomous platforms. As a result, ensuring rapid convergence becomes imperative to mitigate numerical issues during operational processes. In this paper a Fisher Information Matrix Driven (FIMD) approach is introduced to tackle these challenges. The methodology integrates a sampling-based experimental design approach with experiment ranking based on FIM to select the most informative experiment at each iteration. The effectiveness of the proposed design methodology is examined and discussed via two different case studies of increasing complexity: a fed-batch reactor in which the fermentation of baker’s yeast is carried out and a nucleophilic aromatic substitution in a flow reactor system.

Model-Free Optimal Vibration Control of a Nonlinear System Based on Deep Reinforcement Learning

Optimal control of nonlinear vibration requires precise knowledge of the system and the solution to Hamilton–Jacobi–Bellman (HJB) equation. However, in practical engineering applications, acquiring precise system parameters poses challenges, and the analytical solutions for the HJB equation are difficult to obtain. In this paper, two reinforcement learning algorithms, Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and Soft Actor-Critic (SAC) algorithm, are employed to train neural network-based optimal controllers for the van der Pol vibration system in the presence of unknown system parameters. To validate their performance, the controllers undergo testing in a series of experiments, including assessments of free vibration, frequency sweep excitation, and Gaussian noise excitation. The results indicate that both the TD3-trained and SAC-trained neural network-based controller are capable of proficiently suppress the vibration of the van der Pol oscillator. Additionally, these two model-free controllers can approximate the optimal control law which solved based on the dynamic model of the nonlinear system.

An Approach to a Group Movie Recommender System using Matrix Factorization-based Collaborative Filtering

The growth of online movie streaming platforms has driven the demand for recommender systems that are able to deal with the daunting challenge of users finding movies that match their preferences. However, these recommender systems tend to focus on the needs of individual users, whereas in the real world, there are circumstances in which recommendations are needed for a group of users. Therefore, this study proposes a Group Recommender Systems (GRS) using Matrix Factorization (MF) with aggregation model to recommend movies for a group of users. We employ three Matrix Factorization methods to three distinct group sizes, which are small, medium, and large. Our goal is to identify the most effective approach for each group size. To evaluate the performance, we use precision and recall as measurement metrics. The results show that the MF method, After Factorization (AF) outperforms the other MF methods, i.e., Before Factorization (BF) and Weighted Bfore Factorization (WBF) in terms of precision parameters for small groups (2-4 users), which achieving a score of 0.86. Meanwhile, BF method surpassing both AF and WBF in precision parameters for medium groups (5-8 users) with a score of 0.81.

What can we learn when fitting a simple telegraph model to a complex gene expression model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

A highly efficient clean hydrometallurgy process for gold leaching in a Fenton oxidation assisted thiourea system

Clean hydrometallurgy processes, formed by “oxidant-ligand” systems, present promising strategies for gold leaching to mitigate the environmental impact of cyanidation. In this work, a Fenton oxidation-assisted thiourea system was proposed, employing reactive oxygen species (ROS, generated from Fenton reaction) as oxidant and thiourea as the ligand, respectively. By precise parameters control, 100% leaching of gold was rapidly achieved within 30 min from a roasted gold concentrate, highlighting extremely fast kinetics. Furthermore, this system was optimized by incorporating nitrilotriacetic acid (NTA) as an additive, which significantly reduced the thiourea consumption and produced the same gold leaching (100%, 60 min). Experimental results demonstrate that NTA lowers the solution potential (ORP) and eliminates the side reactions between iron ions and thiourea, thereby reducing thiourea decomposition. Surprisingly, the combination of Fenton oxidation and thiourea showed a high selectivity of the generation of ROS, with superoxide radical anion (•O2−) dominating, singlet oxygen (1O2) co-existing, but hydroxyl radical (•OH) absenting. Importantly, the moderate •O2− primarily contributes to gold leaching, which is generated through a chain reaction pathway between iron complex and H2O2 (Fe3+-Ligand+H2O2 → Ligand-Fe3+-OOH → Fe2+-Ligand+•O2−). Besides, the generation process of •O2− can be regulated by adding NTA. This work presented a prospective way in establishing the clean hydrometallurgy processes for efficient gold leaching.