Comprehensive Study Research Articles

A Comprehensive Study on Peppermint Oil and Cinnamon Oil as Nanoemulsion: Preparation, Stability, Cytotoxicity, Antimicrobial, Antifungal, and Antioxidant Activity.

Recent studies have shown that nanoemulsions prepared with essential oils have significant antimicrobial potential against multidrug-resistant pathogens due to increased chemical stability. Nanoemulsion also promotes controlled and sustained release, which increases their bioavailability and efficacy against multidrug-resistant bacteria. This study aimed to investigate the antimicrobial, antifungal, antioxidant, and cytotoxicity properties of cinnamon essential oil and peppermint essential oil as nanoemulsions compared to pure forms. For this purpose, analyses of the selected stable nanoemulsions were carried out. The droplet sizes and zeta potentials of peppermint essential oil nanoemulsions and cinnamon essential oil nanoemulsions were found to be 154.6±1.42 nm and -17.1±0.68 mV and 200.3±4.71 nm and -20.0±0.81 mV, respectively. Although the amount of essential oil used in nanoemulsions was 25% w/w, antioxidant and antimicrobial activities were found to be more effective compared to pure essential oils. In cytotoxicity studies on the 3T3 cell line, both essential oil nanoemulsions showed higher cell viability than pure essential oils. At the same time, cinnamon essential oil nanoemulsions exhibited a higher antioxidant property than peppermint essential oil nanoemulsions and showed superiority in the antimicrobial susceptibility test conducted against four bacteria and two fungi. Cell viability tests determined that cinnamon essential oil nanoemulsions showed considerably higher cell viability compared to pure cinnamon essential oil. These findings indicated that the prepared nanoemulsions in the current study might positively influence the dosing regimen and clinical outcomes of antibiotic therapy.

Comprehensive Study on Scoring and Grading Systems for Predicting the Severity of Knee Osteoarthritis.

Knee Osteoarthritis (KOA) is a degenerative joint ailment characterized by cartilage loss, which can be seen using imaging modalities and converted into imaging features. The older population is the most affected by knee OA, which affects 16% of people worldwide who are 15 years of age and older. Due to cartilage tissue degradation, primary knee OA develops in older people. In contrast, joint overuse or trauma in younger people can cause secondary knee OA. Early identification of knee OA, according to research, may be a successful management tactic for the condition. Scoring scales and grading systems are important tools for the management of knee osteoarthritis as they allow clinicians to measure the progression of the disease's severity and provide suggestions on suitable treatment at identified stages. The comprehensive study reviews various subjective and objective knee evaluation scoring systems that effectively score and grade the KOA based on where defects or changes in articular cartilage occur. Recent studies reveal that AI-based approaches, such as that of DenseNet, integrating the concept of deep learning for scoring and grading the KOA, outperform various state-of-the-art methods in order to predict the KOA at an early stage.

Cordyceps militaris: A Comprehensive Study on Laboratory Cultivation and Anticancer Potential in Dalton's Ascites Lymphoma Tumor Model.

Cancer, a predominant cause of mortality, poses a formidable challenge in our pursuit of elevating life expectancy. Throughout history, individuals have sought natural remedies with minimal side effects as an appealing substitute for chemotherapeutic drugs. One such remedy is Cordyceps militaris, a renowned medicinal mushroom deeply entrenched in Asian ethnomedicine. Revered for its rejuvenating and curative attributes, it relied upon for ages. The mushroom's soaring demand outpaced natural availability, necessitating controlled laboratory cultivation as the core focus and exploring the potential of methanolic extracts from harvested Cordyceps militaris fruiting bodies against Dalton's Lymphoma Ascites (DLA) cells in vitro, with a specific emphasis on its anticancer traits. For cultivation, we employed a diverse range of rice substrates, among which bora rice showed promising growth of C. militaris fruiting bodies. To assess DLA cell cytotoxicity, several assays, including trypan blue exclusion assay, MTT assay, and LDH assay, were employed at different time points (24-96 h), which provided valuable insights on DLA cell viability and proliferation, shedding light on its therapeutic potential against cancer. Our studies unveiled that methanolic extract prompts apoptosis in DLA cells via AO/EB dual staining, manifesting consistent apoptosis indicators such as membrane blebbing, chromatin condensation, nuclei fragmentation, and cellular shrinkage at 48-96 h of treatment. Furthermore, these striking repercussions of apoptosis were comprehended by an in silico approach having molecular docking simulation against antiapoptotic proteins like BCL-2, BCL-XL, MCL-1, BFL-1 HSP100. Methanolic C. militaris extracts exhibited cytotoxicity and apoptotic alterations in DLA cells.

Multi-stage generative adversarial networks for generating pavement crack images

The application of machine learning techniques in pavement health monitoring based on computer vision has greatly improved the accuracy and efficiency in the detection of pavement distress levels and categories. However, a persistent challenge in this field is the issue of sample imbalance, primarily arising from the scarcity of cracked pavement images, which hampers their effectiveness in road maintenance engineering. To address this issue and enhance the fast and stable generation of high-quality crack images for engineering purposes, this study proposes two frameworks based on Generative Adversarial Networks (GAN): Multi-Stage GAN-v1 and Multi-Stage GAN-v2. These frameworks break down the complex task of directly generating high-quality images into a series of incremental steps, gradually increasing the image resolution from initially generated low-precision images. Both versions, v1 and v2, consist of multiple sequentially connected generation units, with each unit utilizing the Wasserstein Generative Adversarial Network-Gradient Penalty (WGAN-GP). Furthermore, v2 has the additional capability of generating pavement crack images of specified types and simultaneously providing crack segmentation labels. This feature significantly enhances the practical applicability of the generated data in engineering contexts. In a comprehensive case study, the evaluation results clearly illustrate the superior image generation quality from the two proposed frameworks. Moreover, the results from ablation experiments, involving the training of nine state-of-the-art crack semantic segmentation and object detection networks using both generated images and real images, demonstrate the effective utility of these generated images for training pavement distress detection networks.

Data Security in Cloud Environment by Using Hybrid Encryption Technique: A Comprehensive Study on Enhancing Confidentiality and Reliability

Data Security in Cloud Environment by Using Hybrid Encryption Technique: A Comprehensive Study on Enhancing Confidentiality and Reliability

Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency, and unbiasedness. Recently, techniques in Explainable Artificial Intelligence (XAI) have been attracting considerable attention and have tremendously helped Machine Learning (ML) engineers in understand AI models. However, at the same time, we started to witness the emerging need beyond XAI among AI communities; based on the insights learned from XAI, how can we better empower ML engineers in steering their DNNs so that the model’s reasonableness and performance can be improved as intended? This article provides a timely and extensive literature overview of the field Explanation-Guided Learning (EGL), a domain of techniques that steer the DNNs’ reasoning process by adding regularization, supervision, or intervention on model explanations. In doing so, we first provide a formal definition of EGL and its general learning paradigm. Second, an overview of the key factors for EGL evaluation, as well as summarization and categorization of existing evaluation procedures and metrics for EGL are provided. Finally, the current and potential future application areas and directions of EGL are discussed, and an extensive experimental study is presented aiming at providing comprehensive comparative studies among existing EGL models in various popular application domains, such as Computer Vision and Natural Language Processing domains. Additional resources related to event prediction are included in the article website: https://kugaoyang.github.io/EGL/

[Gd(HB-DO3A)]: Equilibrium, Dissociation Kinetic and Structural Differences in a Simple Homolog of [Gd(HP-DO3A)] (Prohance®).

[Gd(HP-DO3A)] (gadoteridol) as an active compound of ProHance® is a widely employed contrast agent in clinical MRI scans in the last 30 years. Recent concerns about the long-term retention of gadolinium-based contrast agents (GBCAs) led to a deeper investigation of the structural features underlying the integrity of the paramagnetic metal complex. Several human and nonclinical studies have noted marked differences among the macrocyclic GBCAs, with the least retention of Gd traces and most rapid elimination consistently being reported for [Gd(HP-DO3A)]. It was deemed of interest to assess how minor structural/electronic changes associated to the ligand structure may affect basic properties of the metal complex with several [Gd(HP-DO3A)] analogues synthesized and characterized in the last years. We recently reported that the closest homolog of [Gd(HP-DO3A)], i. e.: [Gd(HB-DO3A)], in which a (±)-2-hydroxy-1-propyl pendant arm is replaced by a (±)-2-hydroxy-1-butyl moiety, showed a significantly different retention behaviour in the model interaction with collagen, despite the apparently very minor structural difference. In this paper we report a comprehensive study of the structural, thermodynamic, kinetic and relaxation properties of [Gd(HB-DO3A)], compared to the parent [Gd(HP-DO3A)] and to other closely related macrocyclic GBCAs to assess whether very minor structural changes can modulate the physico-chemical properties of Gd3+ complexes.

A comprehensive study on transparent conducting oxides in compact microbial fuel cells: Integrated spectroscopic and electrochemical analyses for monitoring biofilm growth

Microbial fuel cells (MFCs) are an emerging technology that holds promise for renewable energy production and the mitigation of environmental challenges. This research paper introduces a single-compartment MFC reactor that utilizes transparent conducting oxides (TCOs), such as fluorine-doped tin oxide (FTO) and indium tin oxides (ITO), as the working electrodes. The effectiveness of MFCs based on FTO and ITO was evaluated by characterizing the transparent electrode and examining its performance during biofilm cultivation. Additionally, the optical properties of the biofilm grown directly on these electrodes were investigated using LEDs as a light source. The impressive average current density of 200 μA cm−2 over 100 days demonstrates the efficiency of the see-through electrodes in bioenergy extraction. The correlation between spectroscopic and microscopic analyses substantiates the feasibility of employing transparent electrodes for accurate quantification of biofilm thickness, with an initial accuracy of ±10 μm in the initial cycle, ±22 μm in the subsequent cycle, and a maximum of ±31 μm after seven days of growth. This innovative approach holds great potential for advancing our understanding of MFCs and their application in environmentally friendly energy generation and optical-based monitoring.

A Comprehensive Study on Model Simplification and Parameter Estimation of Brushless Doubly Fed Standalone Generation System

Brushless doubly-fed induction generator (BDFIG) has great potential in stand-alone generation applications. Currently, lots of control strategies have been proposed based on the BDFIG d-q model with different machine parameters ignored for simplification, but the simplification influences on model accuracy have not been discussed systematically, and the application ranges are not clear. Therefore, aiming at controller design, this paper provides a comprehensive study on model simplification and parameter estimation of brushless doubly-fed stand- alone generation. Based on the intuitive DFIG-like BDFIG model and the typical electrical quantity relations widely used in stand-alone controller design, steady-state and dynamic parameter sensitivities of BDFIG are analyzed with operation condition fully considered. Based on the analysis, the universal BDFIG simplified d-q model aiming at stand-alone control is evaluated sufficiently, and the application range is clarified. The simplification of control analysis is illustrated, and a highly-targeted and simple parameter estimation method is proposed for simplified modeling and control. Finally, simulations and experiments verify the analysis accuracy of the simplified DFIG-like BDFIG model and the feasibility of the proposed parameter estimation method.

A comprehensive study on sustainable entrepreneurship for cottage, micro, small, and medium enterprises in Bangladesh

The present study aims at identifying dominant factors for sustainable entrepreneurship for cottage, micro, small, and medium enterprises (CMSMEs) in Bangladesh. For the convenience of study EFA and CFA models were developed for analyzing, identifying, and confirming the factors harbouring sustainable CMSMEs. Four factors were considered and analyzed such as sources of funds and loans, the opportunity to sell the products, the background of entrepreneurs, and governmental support to examine the extent to which they contribute to entrepreneurial sustainability. The study reports that three factors (sources of funds and loans, the opportunity to sell the products, and governmental support) have a significant and positive effect on the sustainability of CMSMEs entrepreneurship, and these could be considered as an indicative to achieving sustainable entrepreneurship for CMSMEs in a market environment.

Exploration on the combustion chemistry of p-xylene: A comprehensive study over wide conditions and comparison among C8H10 isomers

Pyrolysis and oxidation of p-xylene over wide conditions were studied in this work in order to provide a comprehensive insight into the combustion chemistry of p-xylene. Concerning this goal, the pyrolysis of p-xylene was investigated in a flow reactor at pressures from 0.04 to 1 atm over the temperature range of 1000-1600 K. Key radicals, isomers and PAH products including three xylyl isomers, benzyl, xylylene isomers, styrene, benzene, fulvene, indene, naphthalene and phenanthrene, were identified and quantified using synchrotron vacuum ultraviolet photoionization mass spectrometry. The oxidation of p-xylene was studied in a jet-stirred reactor at 10 atm, 800-1300 K and equivalence ratios of 0.5-2.0 with a residence time of 0.5 s. Oxidation products were quantified by using gas chromatography combined with mass spectrometry and Fourier transform infrared spectrometry. A kinetic model for p-xylene combustion over wide conditions was developed based on our previously proposed p-xylene oxidation model and validated against the new experimental measurements in this work and literature reported experimental data, including flow reactor oxidation, laminar premixed flame, ignition delay time and laminar burning velocity. In the flow reactor pyrolysis, p-xylene mainly undergoes unimolecular decomposition to yield p-xylyl, which mainly suffers the H-loss reaction to form p-xylylene and isomerization reaction to form o-xylyl. The resonantly stabilized fulvenallenyl and benzyl radicals play an important role in the formation of bicyclic and tricyclic PAHs including indene, naphthalene, and phenanthrene. In the JSR oxidation, p-xylene is predominantly consumed via H-abstraction by oxygenated radicals such as OH, O, and HO2 forming p-xylyl, which can be oxidized by HO2 and eventually converted to p-methylbenzaldehyde. As the most abundantly produced oxygenated aromatics, further consumption of p-methylbenzaldehyde results in the formation of other observed oxygenated aromatics such as cresol, benzofuran, and benzaldehyde. The combustion chemistry of three C8H10 isomers, i.e., p-xylene, o-xylene, and ethylbenzene, were also compared in terms of pyrolysis and oxidation fuel reactivity, and PAHs formation and growth, showing remarkable fuel isomeric effects.

A comprehensive survey on several fire management approaches in wireless sensor networks

The majority of the fires are activated through environmental reasons although a minority of them are self-activated. To detect fires several safety systems were introduced. There are wired systems, cameras, satellite systems, and bluetooth feasible to provide a complete image of the world but after a long search period. These systems are not perfect since it prevents fire from finding just at the time, the fire initiates. But, recent technological development in wireless sensor networks (WSN) has spread out its fire detection application. A comprehensive survey on several fire management approaches in WSN propose to discuss various fire detection approaches like early fire detection, energy efficient fire detection, mobile agent-based fire detection, unmanned aerial vehicle (UAV)-based fire detection, threshold-based fire detection, machine learning based fire detection and secure fire detection approaches. Moreover, the comprehensive tabular study of the fire management technique is given that will assist in the suitable selection of approaches to be applied for the detection of fire. Furthermore, WSN uses the clustering method to minimize redundant dataandsecure fire detection approaches collect authenticated data related to fire detection. Early fire detection approaches detects the fire early. Machine learning algorithm detects the fire efficiently.

Gaze Estimation by Attention-Induced Hierarchical Variational Auto-Encoder.

Appearance-based gaze estimation has been widely studied recently with promising performance. The majority of appearance-based gaze estimation methods are developed under the deterministic frameworks. However, the deterministic gaze estimation methods suffer from large performance drop upon challenging eye images in low-resolution, darkness, partial occlusions, etc. To alleviate this problem, in this article, we alternatively reformulate the appearance-based gaze estimation problem under a generative framework. Specifically, we propose a variational inference model, that is, variational gaze estimation network (VGE-Net), to generate multiple gaze maps as complimentary candidates simultaneously supervised by the ground-truth gaze map. To achieve robust estimation, we adaptively fuse the gaze directions predicted on these candidate gaze maps by a regression network through a simple attention mechanism. Experiments on three benchmarks, that is, MPIIGaze, EYEDIAP, and Columbia, demonstrate that our VGE-Net outperforms state-of-the-art gaze estimation methods, especially on challenging cases. Comprehensive ablation studies also validate the effectiveness of our contributions. The code will be publicly released.

Routing and Scheduling in Multigraphs With Time Constraints—A Memetic Approach for Airport Ground Movement

Routing and scheduling problems with increasingly realistic modelling approaches often entail the consideration of multiple objectives, time constraints, and modelling the system as a multigraph. This detailed modelling approach has increased computational complexity and may also lead to violation of the additivity property of the costs. In the worst scenario, increased complexity makes the problem intractable for exact algorithms. Even when the problem is solvable, exact algorithms may not provide solutions within the given time budget, and the found solutions are not guaranteed to be optimal due to the additivity property violation. Approximate solution methods become more suitable in this case. This paper focuses on one particular real-world application, the Airport Ground Movement Problem, where both time constraints and parallel arcs are involved. We introduce a novel Memetic Algorithm for Routing in Multigraphs with Time constraints (MARMT) and present a comprehensive study of its different variants based on diverse genetic representation methods. We propose a local search operator that enhances search efficiency and effectiveness. MARMT is tested on real data based on two airports of different sizes. Our results show that MARMT does not suffer from the non-additivity property problem as it outperforms the state-of-the-art exact algorithm when allowed to converge. When a time budget of 10 seconds is imposed on MARMT, it is able to provide solutions with quality comparable (within 1-5% degradation) to the ones given by the exact algorithm with respect to the aggregated objective values. MARMT can be adapted for other applications, such as train operations.

A Comprehensive Study of Trajectory Forgery and Detection in Location-Based Services

A Comprehensive Study of Trajectory Forgery and Detection in Location-Based Services

Research Front and Future Prospect: A Bibliometric Study of Metaphor Comprehension

Metaphor comprehension is an important component in the process of constructing metaphorical thinking. Therefore, the topic of “metaphor comprehension” should be a crucial part of metaphor studies. This study uses Bibliometrix to conduct a bibliometric study of research on metaphor comprehension from 2013 to 2023, focusing on the knowledge structure, the hot spots, and the research fronts. The co-citation analysis shows that the Contemporary Theory of Metaphor (CTM) in Cognitive Linguistics has provided the dominant knowledge foundation for the research of metaphor comprehension. A large number of hypotheses from CTM not only provide theoretical guidance for the current understanding of metaphor but also provide a reference for experimental paradigms. The coupling analysis shows that most of the influential studies in the time span have conducted empirical studies, demonstrating the explanatory power of empirical results and the interdisciplinary trend of metaphor in psychology and neuroscience. In the past decade, theoretical approaches to metaphor comprehension have been divided into linguistic and interdisciplinary categories, in which different experimental paradigms and conditions were used to reveal the comprehension mechanism. However, while providing insights for metaphor comprehension study through the existing research results, the bibliometric analysis reflects that researchers should fully consider the gap between metaphor in use and metaphor in experiments. This may be mediated through the progress of metaphor comprehension models in different contexts. This study suggests that future research on metaphor comprehension should enrich their theoretical models, properly use or combine research methods and designs, and look into different types of research objects.

SOCIO-ECONOMIC SIGNIFICANCE OF TOURISM DEVELOPMENT ON THE GREAT SILK ROAD (KAZAKHSTAN SECTION)

The Great Silk Road is a historical place for Kazakhstan, promoting mutual understanding and peace between peoples, connecting cultures, developing tourism, trade and business. Therefore, a comprehensive study of the tourism and recreational potential of the sites of the Kazakhstan section of the Great Silk Road, the socio-economic significance of tourism development and tourism attractiveness is very important. The purpose of this study was to determine the socio - economic significance of tourism development in the Kazakhstan section of the Great Silk Road. In the study of tourism and recreational resources of the Great Silk Road, the methods of analysing the theoretical basis, studying statistical data and questionnaires were used comprehensively. As a result, the socio-economic significance of tourism development in Kazakhstan, favourable socio-cultural impact of the Great Silk Road in connection with improving the welfare of the population of the territory, increasing the flow of tourists, income from the tourism industry and improving infrastructure was revealed. The survey showed that 40.9 % of respondents rated the level of satisfaction with transport infrastructure from 5 points to 3 points. 34.1 % of respondents rated the quality of roads as 4 points and 13.6 % as 5 points. It was also found that the most visited tourist sites on the Kazakh part of the Great Silk Road in recent times are the mausoleum of Khoja Ahmed Yasawi, Otrar State Archaeological Museum-Reserve, mausoleum of Aisha Bibi and Karakhan. Thus, it was proposed to form an information base of tourist sites of the Kazakhstan part of the Great Silk Road, to attract qualified personnel in the ser vice sector, to popularise tourist sites by improving Internet resources, advertising and shooting feature films.

Forecasting surface movements based on PSI time series using machine learning algorithms

ABSTRACT Surface movements pose a critical issue requiring prompt detection and monitoring to ensure safety. To address this concern, the detection and continuous monitoring of surface deformations have gained importance. While numerous point-based surveying methods exist, multi-temporal interferometric synthetic aperture radar (InSAR) analysis has emerged as a powerful technique capable of detecting deformations across large areas with freely available SAR images and software. However, the significance of surface movement forecasting must be addressed, as it plays a significant role in preventing accidents and implementing precautionary measures. In light of this, a comprehensive study was conducted at the new Istanbul Airport in Türkiye, employing the Persistent Scatterer InSAR (PSI) method with a collection of 211 Sentinel-1 SAR images. The results obtained were investigated, and six test case regions were determined for the forecasting analysis in terms of time series movement type (subsidence, uplift, and stable) and structure type (runway and building) using various machine learning (ML) algorithms. To accomplish this, regression-based and sequential-based ML algorithms were employed and compared, showing the potential of cutting-edge techniques. The Stacked LSTM algorithm was the most effective in generating accurate forecasts. Furthermore, by incorporating exogenous variables into the forecasting analysis, the study improved the accuracy of the results. To gain deeper insights into the impact of these exogenous variables, feature importance analysis was conducted using the permutation feature importance method. The results obtained were examined, considering both the structure type and the characteristics of the time series data.

Elucidating the Diagnostic Complexity of Round Cell Sarcoma with EWSR1-CREM Fusion: A Comprehensive Case Study

Sarcomas, particularly undifferentiated small round cell sarcomas of bone and soft tissue, pose significant diagnostic challenges due to their nonspecific morphology and the necessity for comprehensive molecular analyses. This paper discusses a rare case of round cell sarcoma exhibiting the EWSR1-CREM fusion, offering insights into the complexities of its diagnosis and management. The patient, a 15-year-old female with a history of Type 1 diabetes, presented with persistent right thigh tenderness and swelling. MRI revealed a large necrotic mass in the retroperitoneal region. Histological analysis showed a well-demarcated tumor with diverse cellular morphologies and distinct necrotic areas. Immunohistochemical (IHC) tests identified dot-like staining for Desmin and Vimentin but negative results for several markers, including Cytokeratin and CD45. Strong ALK positivity was noted. Next-generation sequencing with the Illumina TruSight™ Oncology 500 assay revealed the fusion gene EWSR1-CREM, along with benign and uncertain mutations in other genes. The tumor’s morphology and immunoprofile, along with molecular findings, led to a diagnosis of round cell sarcoma with EWSR1-CREM fusion. This case adds to the spectrum of tumors associated with this fusion, often presenting diverse morphologies. The rarity of EWSR1-CREM fusion sarcomas poses a challenge in treatment, highlighted by the development of pulmonary metastases and disease progression after surgical excision in this patient despite the lack of an effective targeted therapy. In conclusion, this case emphasizes the need for a multidisciplinary diagnostic approach in complex sarcomas and highlights the importance of continued research on rare sarcomas, their genetic underpinnings, and potential therapeutic targets.

Unraveling the Activity and Mechanism of TM@g-C4N3 Electrocatalysts in the Oxygen Reduction Reaction.

In this work, a high-throughput screening strategy and density functional theory (DFT) are jointly employed to identify high-performance TM@g-C4N3 (TM = 3d, 4d, 5d transition metals) single-atom catalysts (SACs) for the oxygen reduction reaction (ORR). Comprehensive studies demonstrated that Cu@, Zn@, and Ag@g-C4N3 show high ORR catalytic activities under both acidic and alkaline conditions with favorable overpotentials (ηORR) of 0.70, 0.89, and 0.89 V, respectively; among them, Cu@g-C4N3 is the best candidate. The ORR follows a four-electron mechanism with the final product H2O/OH-. Cu@, Zn@, and Ag@g-C4N3 catalysts also exhibit good thermal (500 K) and electrochemical (0.93-3.14 V) stabilities. Cu@, Zn@, and Ag@g-C4N3 demonstrate superior activities with low ηORR due to its moderate adsorption strength of *OH. The ηORR and the Gibbs free energy changes of *OH (ΔG4(acidic)/ΔG4(alkaline)) resemble a volcano-type relationship under acidic/alkaline conditions, respectively. Additionally, the O-O bond length in *OOH emerged as an effective structural descriptor for rapidly identifying the promising electrocatalysts. This research provides valuable insights into the origin of the ORR activity on TM@g-C4N3 and offers useful guidance for the efficient exploration of high-performance catalyst candidates.