Multiple Metrics Research Articles

The Use and Perceptions of the Defense and Veterans Pain Rating Scale by Nursing Personnel

BackgroundIn 2010, the Office of the US Army Surgeon General recommended the Veterans Administration (VA) assess pain using the Defense and Veterans Pain Rating Scale (DVPRS). One item in the DVPRS is for measuring pain intensity. This item contains a combination of five response metrics: categories, faces, colors, numbers, and functional descriptors. A few studies have supported patients’ and health care providers’ preferences for the DVPRS and its psychometric properties. However, they also left uncertainties about its usability and validity. AimsTo advance our understanding of the DVPRS, this study examined the use and perceptions of the DVPRS’ pain intensity item by nursing personnel during multi-modal care. DesignA cross-sectional survey design was used. SettingVA Community Living Center. ParticipantsNursing personnel. MethodsNursing personnel answered closed- and open-ended survey questions during a single session. ResultsNursing personnel reported sufficient training before implementing the measure and that patients primarily used the numeric metric. When patients used a non-numeric metric, the nursing personnel responded in variable ways. In addition, the nursing personnel interpreted the functional descriptors differently. The nursing personnel also noted the need to supplement the pain intensity item with patients’ pain duration and pain location. ConclusionsResults from this study inform the nursing community about the DVPRS’ pain intensity item, which combines multiple response metrics. The results support the need for nursing units to generate and standardize procedures for using the item to measure multi-site pain and for interpreting and documenting patients’ non-numeric responses. The effects of such procedures on the measure's usability and psychometric properties warrants additional investigation.

Are multiple ecosystem stability metrics equally sensitive and consistent to environmental gradient? A test for African terrestrial biomes

It is necessary to find effective methods for quantifying ecosystem stability that can forewarn massive collapses of ecosystem structure and inform ecosystem management. Multiple ecosystem stability metrics (ESMs) have been proposed and are increasingly employed to quantify terrestrial ecosystem stability. However, their sensitivity and consistency in depicting stability changes in diverse terrestrial ecosystems have not been thoroughly examined. Here, we evaluated 11 commonly suggested ESMs using two vegetation indices (normalized difference vegetation index, NDVI, and enhanced vegetation index, EVI) and examined their responses to the climatic gradient in diverse African biomes. We found that ESMs exhibited unequal sensitivity as certain metrics showed insignificant trends with climatic drivers whereas others indicated significant changes in stability in the same case. Among the significant results, inconsistent trends indicated by different ESMs were prevalent. Moreover, diverse ESMs exhibited mostly weak correlations despite being based on the same theoretical deduction. The ESMs derived from NDVI and EVI exhibited inconsistencies in some cases, suggesting the influence of the adopted variables on ESM performance. Our study revealed that multiple ESMs were unequally sensitive and inconsistent in indicating ecosystem stability. ESM performance was synthetically influenced by the ESM algorithm (different metrics), the adopted variable for ESM calculation, and the target biome type. Improvements in ecosystem stability evaluation on broad scales, therefore, are needed through further exploration and verification of ESMs.

Robust, scalable, and informative clustering for diverse biological networks

Clustering molecular data into informative groups is a primary step in extracting robust conclusions from big data. However, due to foundational issues in how they are defined and detected, such clusters are not always reliable, leading to unstable conclusions. We compare popular clustering algorithms across thousands of synthetic and real biological datasets, including a new consensus clustering algorithm—SpeakEasy2: Champagne. These tests identify trends in performance, show no single method is universally optimal, and allow us to examine factors behind variation in performance. Multiple metrics indicate SpeakEasy2 generally provides robust, scalable, and informative clusters for a range of applications.

Statistical and machine learning applied to the universal consolidated database to predict heat transfer coefficient in flow condensation

Due to complex physical mechanisms, and a general lack of accurate modeling techniques, two phase convective heat transfer is hard to characterize. In this article we show the general effectiveness of statistical and machine learning techniques for predicting two-phase heat transfer coefficients. This is a comprehensive study that: performs a full exploratory data analysis (EDA) to determine the range by which we may apply these models, derives an optimal data set as a basis for predicting heat transfer coefficient, tests all models on an independent data set, and assesses model performance on multiple metrics. We find that random forest methods, gradient boosted machines, and multi-layer perceptrons (deep neural networks) perform the best in predicting heat transfer coefficient on the given data set. We find that in general, the increased data base predicts better due to the inclusion of a few important thermophysical and flow properties.

Analysis of the impact of Suez Canal blockage on the global shipping network

This study aims to quantitatively analyze the impact of the Suez Canal blockage on global maritime transportation networks. The Suez Canal, a critical global maritime transport route, experienced a significant disruption due to a ship grounding accident in March 2021, exposing the fragility of global logistics supply chains. We constructed a comprehensive shipping network, integrating real trajectory data from automatic identification system (AIS) devices on ships collected in 2018. The network comprises 2713 nodes and 137,830 links, with defined targeted (Suez Canal blockage) and random attack modes. Our analysis, based on multiple network topology metrics, reveals that the blockage had a considerable effect on the global shipping network, significantly more than random disruptions of comparable scale. The incident led to a decrease in network accessibility and connectivity, conforming to the law of distance attenuation. The effects varied across continents; Africa experienced the most significant impact, while Asia and Europe were less affected. Europe's developed transport system effectively mitigated the impact. Data on different ship types passing through the Suez Canal showed that container ships and petrochemical tankers were the most affected. This research underlines the Suez Canal's significance to global shipping and the necessity of robust supply chain management strategies to limit the fallout from similar future incidents.

A Reinforcement Learning Method of Solving Markov Decision Processes: An Adaptive Exploration Model Based on Temporal Difference Error

Traditional backward recursion methods face a fundamental challenge in solving Markov Decision Processes (MDP), where there exists a contradiction between the need for knowledge of optimal expected payoffs and the inability to acquire such knowledge during the decision-making process. To address this challenge and strike a reasonable balance between exploration and exploitation in the decision process, this paper proposes a novel model known as Temporal Error-based Adaptive Exploration (TEAE). Leveraging reinforcement learning techniques, TEAE overcomes the limitations of traditional MDP solving methods. TEAE exhibits dynamic adjustment of exploration probabilities based on the agent’s performance, on the one hand. On the other hand, TEAE approximates the optimal expected payoff function for subprocesses after specific states and times by integrating deep convolutional neural networks to minimize the temporal difference error between the dual networks. Furthermore, the paper extends TEAE to DQN-PER and DDQN-PER methods, resulting in DQN-PER-TEAE and DDQN-PER-TEAE variants, which not only demonstrate the generality and compatibility of the TEAE model with existing reinforcement learning techniques but also validate the practicality and applicability of the proposed approach in a broader MDP reinforcement learning context. To further validate the effectiveness of TEAE, the paper conducts a comprehensive evaluation using multiple metrics, compares its performance with other MDP reinforcement learning methods, and conducts case studies. Ultimately, simulation results and case analyses consistently indicate that TEAE exhibits higher efficiency, highlighting its potential in driving advancements in the field.

Development of a national antibiotic multimetric index for identifying watersheds vulnerable to antibiotic pollution

Improved surveillance of antibiotics and antibiotic resistance (AR) throughout the environment is an important aspect of the prevention and control of threats posed to human and ecological health. In response to field investigations often limited by resources and time, this study aims to develop a systematic approach to assess watershed vulnerability to antibiotic pollution and AR by integrating modeling and field studies. The national antibiotic pollution vulnerability index was developed to identify watersheds most impacted by antibiotic sources. The index incorporates multiple metrics representing antibiotic pollution driven by both agricultural activities and municipal wastewater (i.e. outpatient antibiotic prescriptions, wastewater treatment plant effluent flow, stream order and dilution factor of effluent-receiving streams, manure application, and animal facilities), alongside climate change indicators (i.e., temperature, precipitation, and runoff). The pollution index was applied at a state level in North Carolina to identify the most-impacted watersheds and inform site selection for targeted field study quantifying azithromycin, ciprofloxacin, sulfamethoxazole, and trimethoprim concentrations. Modeled-informed sites in NC demonstrated the highest reported concentrations of azithromycin, trimethoprim, and sulfamethoxazole compared to previous NC studies, confirming the index effectiveness in identifying watersheds with higher antibiotic concentrations. At the national scale, watersheds relatively more vulnerable to antibiotic pollution are predominantly located in the Midwest, South, and Northeast regions of the U.S., with Iowa and Indiana being the most impacted states. Climate change is expected to exacerbate watershed vulnerability to agriculture-driven AR in the Midwest and Northeast due to an increase in precipitation and mean temperature coupled with intense agricultural activities. In addition, due to climate change-induced reductions in precipitation and runoff, watersheds in the Midwest, Mid-Atlantic, and South Central are dominantly at higher risk of effluent-driven AR occurrences. We have disseminated the developed indices as open-source online tools to aid in prioritizing strategies to mitigate AR occurrence across the U.S.

Role of locality, fidelity and symmetry regularization in learning explainable representations

Despite their success deep neural networks still lack interpretability and are regarded as black boxes. This hampers a wider adoption in applications with societal, environmental or economical implications, and motivated a variety of techniques for explaining their outputs. Such explanations are however typically produced after model training so there is no guarantee that models learn faithful attributions, a goal they were not trained for. We evaluate the impact of different penalty terms in the loss function that promote explainable feature attributions, and that can be learned during training in an unsupervised way. We show that explainability-constrained models produce better saliency maps based on multiple metrics and tests. Regularizers imposing locality, fidelity and symmetry properties lead to the best performances in terms of MoRF and ROAR scores.

Synthesis of Publication Metrics in Kinesiology-Related Journals: Proxies for Rigor, Usage, and Prestige

ABSTRACT This study documented three domains of journal research quality using metrics from six major publishers and two major database services for a large sample of kinesiology-related journals: Rigor/soundness, usage, and prestige. All journal metrics were highly skewed and variable. Median acceptance rates and initial review time were consistent with rigorous peer-review. Four variables were strongly interrelated (0.849 < r < 0.963) and indicate typical usage of 1 to 3 citations for articles in these journals. The 5-year Eigenfactor Score had a relatively weaker association (0.510 < r < 0.758) with the usage metrics, supporting it as an estimate of the prestige afforded these kinesiology journals in Scopus instead of usage. Care must be taken to interpret multiple journal metrics; taking into account the skew, large variability, and confounding factors such as journal size and subject. Differences across quality domains, metrics, and bias from numerous contextual factors refute the insidious misuse of any single journal metric as quality indictor for ranking journals.

Machine learning-based prediction and mathematical optimization of Capecitabine solubility through the supercritical CO2 system

This work represents development of computational intelligence models for assessment of estimating solubility/bioavailability of orally-taken drugs in SCF (supercritical fluid). The SCF considered in this work is CO2. This approach can provide an appropriate opportunity for the development of sustainable and environmentally-friendly technology with valuable advantages in the drug delivery industry. We modeled the solubility of Capecitabine in CO2SCF using multiple machine learning models. The inputs are Pressure and Temperature in the used dataset and the single output (Y) is the solubility of the drug. Three well-known supervised learning models of KNN (K-Nearest Neighbor), MLP (Multilayer Perceptron), and SVR (Support Vector Regression) were selected for modeling in this research. The hyper-parameters of them were optimized using Cuckoo search algorithm (CS) and then final models assessed using multiple metrics. In terms of coefficient of determination, KNN, MLP, and SVR models have score values of 0.959, 0.996, and 0.952, respectively. Also, they have error rates of 13.78, 1.54, and 19.23 with MSE metric. These facts introduce MLP as the best model obviously and so we used this model for final analysis in this study due to its superior architecture.

China's new urban clusters strategy for coordinated economic growth: Evidence from the sports industry.

In 2014, the Chinese government unveiled the New Urbanization Plan and Document No. 46, which profoundly influenced the development trajectory of the regional economy and sports industry. Using the coupling coordination model, this study aimed to assess the development progress of the sports industry and urban clusters economy. This study sampled Greater Bay Area urban clusters (GBAUC) and Yangtze River Delta urban clusters (YRDUC). The statistics covered one year after the release of the policies to date. We developed a total of 15 macro indicators to evaluate the sports industry and urban cluster economy as two distinct, yet interdependent, economic systems. Using the entropy weight method, we determined the standardized values and weights for the two systems before calculating the coupling coordination degree (D). Between 2015 and 2021, the sampled sports industry and urban clusters economy exhibited coordinated high growth across all economic metrics, with multiple sports industry metrics exhibiting double-digit growth. In 2015, both showed extreme imbalance: D of GBAUC = 0.092, D of YRDUC = 0.091. In 2017, both improved to bare coordination: D of GBAUC = 0.600, D of YRDUC = 0.566. In 2019, both reached well coordination: D of GBAUC = 0.851, D of YRDUC = 0.814. By 2021, both achieved quality coordination: D of GBAUC = 0.990, D of YRDUC = 1. This study provides the first evidence from the sports industry that China's new urbanization model and Document No. 46 are highly effective for synergistic regional economic growth.

Exploring objective versus subjective social ties using entrepreneurs’ gmail data

It is intuitively appealing and common in the literature to describe social ties as one large category that represents multiple constructs which have similar relations across operationalizations. However, that approach does not capture the nuance in the literature and might obscure notable differences between subjective and objective network tie measures, and how those differences extend to relations with venture-level outcomes. Drawing upon novel objective measures of network ties derived from analyses of entrepreneurs’ Gmail data, we offer an empirical assessment of subjective social ties relative to objective social ties. In examining how those assessments relate to multiple metrics of venture performance, we look at both subjective and objective measures. We find that objective network measures predict overall subjective (but not objective) venture performance at an average of r = .31 (for network size) and r = .29 (for network engagement). We delve into the implications of our work, and discuss how future research can progress more effectively and efficiently with an eye towards the practical usefulness of our findings for entrepreneurs and entrepreneurship service organizations.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of ‹instrument, verb, target› triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

Machine learning: Predicting hospital length of stay in patients admitted for lupus flares.

Although rare, severe systemic lupus erythematosus (SLE) flares requiring hospitalization account for most of the direct costs of SLE care. New machine learning (ML) methods may optimize lupus care by predicting which patients will have a prolonged hospital length of stay (LOS). Our study uses a machine learning approach to predict the LOS in patients admitted for lupus flares and assesses which features prolong LOS. Our study sampled 5831 patients admitted for lupus flares from the National Inpatient Sample Database 2016-2018 and collected 90 demographics and comorbidity features. Four machine learning (ML) models were built (XGBoost, Linear Support Vector Machines, K Nearest Neighbors, and Logistic Regression) to predict LOS, and their performance was evaluated using multiple metrics, including accuracy, receiver operator area under the curve (ROC-AUC), precision-recall area under the curve (PR- AUC), and F1-score. Using the highest-performing model (XGBoost), we assessed the feature importance of our input features using Shapley value explanations (SHAP) to rank their impact on LOS. Our XGB model performed the best with a ROC-AUC of 0.87, PR-AUC of 0.61, an F1 score of 0.56, and an accuracy of 95%. The features with the most significant impact on the model were "the need for a central line," "acute dialysis," and "acute renal failure." Other top features include those related to renal and infectious comorbidities. Our results were consistent with the established literature and showed promise in ML over traditional methods of predictive analyses, even with rare rheumatic events such as lupus flare hospitalizations.

A Novel Blockchain-Assisted Aggregation Scheme for Federated Learning in IoT Networks

With the wide range of Internet of things (IoT) applications, Federated Learning (FL) is commonly adopted to protect the privacy of IoT data. FL enables privacy-preserving model training while keeping the data locally available. To alleviate the additional load caused by FL, an improved hierarchical aggregation framework is presented in this paper to decentralize the model aggregation tasks based on end-device clusters. However, when applying FL to IoT networks, how to keep high efficiency and reliability remains open challenges due to a large number and vulnerability of IoT end-devices. In this paper, we propose a blockchain-assisted aggregation scheme for FL in IoT networks, where the aggregation node selection is applied for efficiency improvement as well as blockchain for performance verification. During model aggregation, a selection strategy is obtained by the Deep Deterministic Policy Gradient (DDPG) algorithm and aims to select the optimal subset of IoT end-devices based on multiple metrics. Furthermore, a new performance verification based on the characteristics of blockchain is applied to achieve mutual verification among a number of untrustworthy nodes with the optimal stopping theory, which provides reliable model performance proofs. Simulation results show that the proposed scheme can maintain FL efficiency and reduce the system latency while protecting data privacy.

HACA3: A unified approach for multi-site MR image harmonization.

The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. The general idea is to disentangle anatomy and contrast information from MR images to achieve cross-site harmonization. Despite the success of existing methods, we argue that major improvements can be made from three aspects. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 can be trained and applied to any combination of MR contrasts and is robust to imaging artifacts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art harmonization performance under multiple image quality metrics. We also demonstrate the versatility and potential clinical impact of HACA3 on downstream tasks including white matter lesion segmentation for people with multiple sclerosis and longitudinal volumetric analyses for normal aging subjects. Code is available at https://github.com/lianruizuo/haca3.

Multi-Objective Robust Beamforming for Integrated Satellite and Aerial Networks Supporting Heterogeneous Services

An integrated satellite and aerial network (ISAN) is considered a promising candidate to provide seamless connectivity for future wireless communication systems. In this paper, we propose a multi-objective based robust beamforming (BF) scheme for an ISAN to support heterogeneous services with high flexibility, where the satellite network serves various heterogeneous satellite terminals through multicast non-orthogonal multiple access (MC-NOMA), while the aerial network offers services to many internet of things devices using layered division multiplexing (LDM). Specifically, we first formulate a multi-objective optimization problem (MOOP) to achieve a good trade-off between sum rate maximization and total transmit power minimization. To tackle this mathematically intractable problem, we exploit the weighted Tchebycheff approach to transform the MOOP into a single-objective problem. Since only the angular information based channel state information is available, we exploit the angular discretization method and sequential convex approximation to design a robust BF algorithm to obtain the Pareto optimal solutions. Finally, simulation results demonstrated that our proposed scheme can achieve a optimal trade-off between multiple performance metrics with high spectrum and energy efficiency, so as to support heterogeneous services in the ISAN and fill the gap of only single type of serivce in the existing ISAN works.

Machine learning for distinguishing saudi children with and without autism via eye-tracking data

BackgroundDespite the prevalence of Autism Spectrum Disorder (ASD) globally, there’s a knowledge gap pertaining to autism in Arabic nations. Recognizing the need for validated biomarkers for ASD, our study leverages eye-tracking technology to understand gaze patterns associated with ASD, focusing on joint attention (JA) and atypical gaze patterns during face perception. While previous studies typically evaluate a single eye-tracking metric, our research combines multiple metrics to capture the multidimensional nature of autism, focusing on dwell times on eyes, left facial side, and joint attention.MethodsWe recorded data from 104 participants (41 neurotypical, mean age: 8.21 ± 4.12 years; 63 with ASD, mean age 8 ± 3.89 years). The data collection consisted of a series of visual stimuli of cartoon faces of humans and animals, presented to the participants in a controlled environment. During each stimulus, the eye movements of the participants were recorded and analyzed, extracting metrics such as time to first fixation and dwell time. We then used these data to train a number of machine learning classification algorithms, to determine if these biomarkers can be used to diagnose ASD.ResultsWe found no significant difference in eye-dwell time between autistic and control groups on human or animal eyes. However, autistic individuals focused less on the left side of both human and animal faces, indicating reduced left visual field (LVF) bias. They also showed slower response times and shorter dwell times on congruent objects during joint attention (JA) tasks, indicating diminished reflexive joint attention. No significant difference was found in time spent on incongruent objects during JA tasks. These results suggest potential eye-tracking biomarkers for autism. The best-performing algorithm was the random forest one, which achieved accuracy = 0.76 ± 0.08, precision = 0.78 ± 0.13, recall = 0.84 ± 0.07, and F1 = 0.80 ± 0.09.ConclusionsAlthough the autism group displayed notable differences in reflexive joint attention and left visual field bias, the dwell time on eyes was not significantly different. Nevertheless, the machine algorithm model trained on these data proved effective at diagnosing ASD, showing the potential of these biomarkers. Our study shows promising results and opens up potential for further exploration in this under-researched geographical context.

Evaluating the efficacy of fisheries management strategies in China for achieving multiple objectives under climate change

It is increasingly recognized that effective fisheries management needs to take into account not only the target species, but also the broader scope of biological, environmental, socio-economic and institutional factors pertaining to human utilization of fishery resources. This recognition has stimulated a global call for the Ecosystem-Based Fisheries Management (EBFM), with an increasing appreciation of its merits in the face of climate change. In this study, we applied the concept of EBFM to China's fisheries and tested the performance of various fisheries management strategies, including status quo management and alternative strategies with different objective priorities, under contrasting climate warming scenarios. We used the Ecopath with Ecosim (EwE) as an operating model, with an explicit consideration of trophic interactions, and specifically quantified the trade-offs across multiple metrics (i.e., sustainable fishery production, economic profitability, seafood security, and ecosystem health). No single strategy was superior across all the four dimensions. Importantly, the existing approach to fisheries management underperformed in most metrics, with the current fishing intensity exceeding the level that would maximize multispecies yields (fMSY) and total profits (fMEY). In contrast, an alternative harvest control rule (HCR) strategy, designed to respond to variations in biomass, provided inherent resilience of the ecosystem under climate warming, resulting in increased commercial fish biomass, improved profit and seafood supply, as well as enhanced ecosystem structure and function across various time-climate scenarios. From a tactic perspective, the multiple species management strategies of “fMSY” and “fMEY” can be better suited for the highly mixed fishery context in China. These alternatives also consistently demonstrated superior performance compared to the existing fisheries management strategy. Our work reveals potential trade-offs among diverse ecosystem services and provides a framework using EwE model for quantifying possible performance of management strategies under climate change, leading to more informed and transparent decision-making.

DEBT STRUCTURE AND FIRM PERFORMANCE IN VIETNAMESE STOCK MARKET

This study investigates the relationship between debt structure and firm performance among a comprehensive sample of 546 Vietnamese listed firms over the period 2010 to 2020. The purpose of the research carried out in the article is to establish the relationship between the debt structure and the results of the firm's activities. Employing a fixed-effect method, we analyze the intricate interplay between debt composition and various dimensions of firm performance within the unique economic and institutional context of Vietnam. Our findings reveal a significant and negative association between debt structure and firm performance, shedding light on the intricate dynamics that shape corporate financial decisions and outcomes in the Vietnamese market. Specifically, our analysis indicates that firms with a higher proportion of debt experience decreased levels of performance across multiple performance metrics, including profitability, growth, and operational efficiency. This negative relationship between debt structure and firm performance underscores the importance of optimal capital structure decisions and strategic financial management for Vietnamese listed firms. Our study contributes to the existing literature by providing empirical evidence that extends our understanding of the nuanced determinants of firm performance within emerging market economies. Moving forward, further research could delve deeper into the underlying mechanisms that drive the observed negative relationship. Additionally, exploring potential moderating factors, such as industry-specific characteristics or changes in economic conditions, could provide a more nuanced perspective on the interplay between debt structure and firm performance. Ultimately, this study contributes to the ongoing dialogue on optimal capital structure decisions and their ramifications for the long-term success and sustainability of Vietnamese listed firms, guiding them toward more informed and effective financial strategies.