Critical Metrics Research Articles

BCCHI-HCNN: Breast Cancer Classification from Histopathological Images Using Hybrid Deep CNN Models.

Breast cancer is the most common cancer in women globally, imposing a significant burden on global public health due to high death rates. Data from the World Health Organization show an alarming annual incidence of nearly 2.3 million new cases, drawing the attention of patients, healthcare professionals, and governments alike. Through the examination of histopathological pictures, this study aims to revolutionize the early and precise identification of breast cancer by utilizing the capabilities of a deep convolutional neural network (CNN)-based model. The model's performance is improved by including numerous classifiers, including support vector machine (SVM), decision tree, and K-nearest neighbors (KNN), using transfer learning techniques. The studies include evaluating two separate feature vectors, one with and one without principal component analysis (PCA). Extensive comparisons are made to measure the model's performance against current deep learning models, including critical metrics such as false positive rate, true positive rate, accuracy, precision, and recall. The data show that the SVM algorithm with PCA features achieves excellent speed and accuracy, with an amazing accuracy of 99.5%. Furthermore, although being somewhat slower than SVM, the decision tree model has the greatest accuracy of 99.4% without PCA. This study suggests a viable strategy for improving early breast cancer diagnosis, opening the path for more effective healthcare treatments and better patient outcomes.

FEM Investigation of the Roughness and Residual Stress of Diamond Burnished Surface

Characterization of surface integrity is possible with three critical metrics: microstructure, surface roughness, and residual stress. The latter two are discussed in this paper for low-alloyed aluminum material quality. Ball burnishing is a regularly used finishing procedure to improve surface roughness, shape accuracy, and fatigue life, taking advantage of the fact that it can favorably influence the variation in stress conditions in the material. The effect of burnishing is investigated using finite element simulation with DEFORM 2D software using the real surface roughness of the workpiece. The FEM model of the process is validated with experimental tests, the surface roughness is measured using an AltiSurf520 measuring device, and the residual stress is analyzed with a Stresstech Xstress 3000 G3R X-ray diffraction system (Stresstech, Vaajakoski, Finland). The results indicate that the burnishing process improves the surface roughness and stress conditions of AlCu6BiPb low-alloyed aluminum, and the study shows that there is good agreement between the FE and experimental results, further revealing the effect of the process parameters on the distribution of the compressive residual stress.

Development of A Radio Frequency Identification System for The Manufacturing Packing Process Via The DMAIC Approach

Quality has long been a critical metric for determining a company's competitiveness in the business world. The use of tools, methods, and concepts to improve and regulate product quality has been widespread. The purpose of this research is to solve the missing accessories issue that occurs during the packing process by using the Lean Six Sigma tool DMAIC (Define, Measure, Analyze, Improve, and Control). As part of the Lean Six Sigma quality effort, the DMAIC technique is frequently defined as a problem-solving methodology and a data-driven quality strategy. The actual root cause for these missing accessories is the lack of a proper validation process during the packing process. A brainstorming session was held to develop solutions to this problem, and one suggestion was to employ the radio frequency identification (RFID) validation system. This new implementation was set up during the kitting process by pasting RFID labels for all accessories. These accessories were validated back during the packing process to ensure no missing accessories after the operator sealed the carton box. After implementing the RFID system, the company managed to reduce the cost of manpower and parts that needed to be replaced in case of missing data. It also improves the efficiency of the business, as the addition of a validation RFID system avoids mistakes by detecting missing accessories before the product is delivered to the customer.

Towards AI driven surface roughness evaluation in manufacturing: a prospective study

AbstractIn the era of Industry 4.0 and the digital transformation of the manufacturing sector, this article explores the significant potential of machine learning (ML) and deep learning (DL) techniques in evaluating surface roughness—a critical metric of product quality. The integration of edge computing with current computational resources and intelligent sensors has revolutionized the application of AI-driven algorithms in smart manufacturing. It provides real-time data analysis and decision-making capabilities that were unattainable only a decade ago. The research effort intends to improve data-driven decision-making for product quality evaluation by leveraging data integration from manufacturing operations and surface quality measurements. Although a substantial amount of research has been conducted in the related fields, it is still difficult to comprehend and compile all the data on surface roughness research predictive assessment in the form of a process pipeline. This thorough systematic analysis examines scholarly articles published between 2014 and 2024 focusing on surface roughness assessment in precision manufacturing settings. The article is thoroughly classified based on the manufacturing processes, datasets, and ML models used, giving light on the present status, prominent approaches, and existing issues in this sector. A table summarizing the relevant works in this domain providing an easy access to the current trends have been provided. The article not only compiles essential findings and identifies research gaps and similarities in existing methodologies, but it also discusses future research directions and open issues in AI-aided surface roughness evaluation. The critical analysis of the literature reveals a scientific gaps which includes consistent development of benchmarked datasets and making the AI models more explainable using the state-of-the-art explainable AI (XAI) algorithms. The ultimate objective of the article is not only to provide a guide for the practitioners in either of the three domains of AI, manufacturing or surface metrology but also to pave the path for more robust, efficient, and accurate surface quality evaluation processes in production.

Dirac Eigenvalue Optimisation and Harmonic Maps to Complex Projective Spaces

Abstract Consider a Dirac operator on an oriented compact surface endowed with a Riemannian metric and spin structure. Provided the area and the conformal class are fixed, how small can the $k$-th positive Dirac eigenvalue be? This problem mirrors the maximization problem for the eigenvalues of the Laplacian, which is related to the study of harmonic maps into spheres. We uncover the connection between the critical metrics for Dirac eigenvalues and harmonic maps into complex projective spaces. Using this approach we show that for many conformal classes on a torus the first nonzero Dirac eigenvalue is minimised by the flat metric. We also present a new geometric proof of Bär’s theorem stating that the first nonzero Dirac eigenvalue on the sphere is minimised by the standard round metric.

Advanced Wind Power Forecasting

The popularity of LSTM networks in wind prediction is due to their inherent capabilities in handling the long-term dependencies of sequential data. This normally outperforms traditional methods like ARIMA. However, current work has illustrated that EMD-based approaches outperform LSTM models in many critical performance metrics. Although it is very powerful in gathering historical patterns due to the enhanced gating mechanisms, LSTM has a lot of challenges regarding computational efficiency and flexibility. EMD models overwhelmingly outperformed the LSTM model in computational time by probably about 20-25%, thereby solving a weakness that may be one of the intrinsic weaknesses of the LSTM model. In addition, the EMD models improved by about 2-4% in accuracy as compared to LSTM, thus evidencing better ability in the prediction of the wind data. On feature extraction, EMD did better, improving by 7% over LSTM. Moreover, EMD has better robustness and adaptability, with improvements of 6% and 4%, respectively. This introduction of EMD does bring a bit of an increase in model complexity, this disadvantage is rather insignificant when compared with the huge advantages it brings in efficiency and accuracy. While an LSTM network is very good at modeling a long-term relationship, it suffers from some problems in handling nonlinear interactions and continuous series data, thus its effectiveness may be limited in some cases of forecasting. In contrast, EMD has a clear advantage in real-time applications owing to enhanced efficiency and precision. From the results obtained in this study, it becomes easy to envision that the EMD methodology is un-equivocally better than LSTM in all the critical aspects, such as computational efficiency, accuracy in prediction, feature extraction, resilience, and adaptability when used individually. Results indicate that EMD has a more constructive methodology toward wind pattern predictions, beating LSTM on many measures of performance. The advantages presented should be maximized in future studies aimed at further improving and optimizing a wind-forecasting system, with special attention toward EMD's benefits in gaining more accurate and efficient forecasts.

Research on Broadband Measurement Method of Power System based on Wavelet Transform

This study delves into the exploration of broadband measurement techniques for power systems, utilizing wavelet transform as a foundational tool for signal analysis. The research rigorously evaluates the efficacy of several machine learning algorithms, namely Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest, in interpreting and analyzing broadband signals within power systems. Through a detailed analytical process, the performance of each algorithm is meticulously assessed based on several critical metrics: accuracy, precision, recall, and F1-score. The research investigates broadband measurement methods for power systems using wavelet transform and evaluates the performance of Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest. Results show SVM achieving an accuracy of 85%, precision of 86%, recall of 82%, and F1-score of 84%. ANN yields 82% accuracy, 84% precision, 78% recall, and 81% F1 score. KNN demonstrates 87% accuracy, 88% precision, 84% recall, and 86% F1 score. DT achieves 79% accuracy, 80% precision, 75% recall, and 77% F1 score. Overall, the study provides insights into machine learning algorithms’ effectiveness in broadband power system measurement.

Data-driven employee engagement: A pathway to superior customer service

This paper explores the significant correlation between employee engagement and customer service quality, emphasizing the role of data-driven strategies in enhancing organizational outcomes. The paper highlights the importance of using data analytics to understand and improve employee engagement by analyzing key theories that link engagement to customer satisfaction. It discusses the critical metrics used to measure engagement and how data-driven insights can inform HR strategies, leading to superior customer service. The paper also examines the implications of implementing these strategies, addressing potential challenges such as data privacy, misinterpretation, and cultural resistance. Finally, it suggests future research directions, particularly in integrating emerging technologies like AI and machine learning, to refine engagement strategies further and adapt them to diverse work environments. This review underscores the evolving role of data analytics in HR and its potential to transform employee engagement into a key driver of business success.

Enhancing sustainable healthcare practices through energy-efficient wireless body area networks

This paper explores how the integration and performance of Wireless Body Area Networks (WBANs) contribute to sustainable healthcare practices. WBANs play a crucial role in reducing the environmental footprint of healthcare systems by providing continuous monitoring that can prevent unnecessary hospitalizations and reduce the consumption of disposable medical supplies. Additionally, the design of WBANs using environmentally friendly materials, low-power devices, and recyclable components is discussed to optimize the sustainability of healthcare practices. To enhance infrastructure, services, and quality of life, new technologies have been integrated to improve WBANs. Patients' essential health data may be continuously and instantly collected thanks to these networks, and healthcare providers can receive the data right away for quick analysis and action. WBANs have healthcare benefits, but there are also major drawbacks. Technologies that can combine low power consumption, minimal delay, and high enough data rates are needed for WBANs in order to support a variety of medical applications. The effectiveness of the LoRaWAN and IEEE 802.15.6 technologies in WBAN is assessed in this paper. With the use of seven crucial metrics which are throughput, arrival rate, delay, energy consumption, residual energy, network lifetime, and packet delivery ratio (PDR), this paper seeks to study which technology is appropriate with WBANs using NS3 as a simulation program. With network parameters, 1000 seconds, and 50 nodes, the paper evaluated the performance of each technology. The results show that IEEE 802.15.6 is superior in terms of throughput, PDR, and arrival rate whereas LoRaWAN is superior in terms of energy consumption, residual energy, network lifetime, and delay.

Text Analytics on YouTube Comments for Food Products

YouTube is a popular social media platform in the contemporary digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments about food-related videos on YouTube, specifically within two pivotal food categories: plant-based and hedonic product. We labeled comments using sentiment lexicons such as TextBlob, VADER, and Google’s Sentiment Analysis (GSA) engine. Comment sentiment was classified using advanced Machine-Learning (ML) algorithms, namely Support Vector Machines (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and F1 score. The results from GSA showed a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics, such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

Enhanced Estimation of Crown-Level Leaf Dry Biomass of Ginkgo Saplings Based on Multi-Height UAV Imagery and Digital Aerial Photogrammetry Point Cloud Data

Ginkgo is a multi-purpose economic tree species that plays a significant role in human production and daily life. The dry biomass of leaves serves as an accurate key indicator of the growth status of Ginkgo saplings and represents a direct source of economic yield. Given the characteristics of flexibility and high operational efficiency, affordable unmanned aerial vehicles (UAVs) have been utilized for estimating aboveground biomass in plantations, but not specifically for estimating leaf biomass at the individual sapling level. Furthermore, previous studies have primarily focused on image metrics while neglecting the potential of digital aerial photogrammetry (DAP) point cloud metrics. This study aims to investigate the estimation of crown-level leaf biomass in 3-year-old Ginkgo saplings subjected to different nitrogen treatments, using a synergistic approach that combines both image metrics and DAP metrics derived from UAV RGB images captured at varying flight heights (30 m, 60 m, and 90 m). In this study, image metrics (including the color and texture feature parameters) and DAP point cloud metrics (encompassing crown-level structural parameters, height-related and density-related metrics) were extracted and evaluated for modeling leaf biomass. The results indicated that models that utilized both image metrics and point cloud metrics generally outperformed those relying solely on image metrics. Notably, the combination of image metrics obtained from the 60 m flight height with DAP metrics derived from the 30 m height significantly enhanced the overall modeling performance, especially when optimal metrics were selected through a backward elimination approach. Among the regression methods employed, Gaussian process regression (GPR) models exhibited superior performance (CV-R2 = 0.79, rRMSE = 25.22% for the best model), compared to Partial Least Squares Regression (PLSR) models. The common critical image metrics for both GPR and PLSR models were found to be related to chlorophyll (including G, B, and their normalized indices such as NGI and NBI), while key common structural parameters from the DAP metrics included height-related and crown-related features (specifically, tree height and crown width). This approach of integrating optimal image metrics with DAP metrics derived from multi-height UAV imagery shows great promise for estimating crown-level leaf biomass in Ginkgo saplings and potentially other tree crops.

Classifying fabric wrinkle via regularized random vector function link optimized by African vultures optimization algorithm

The wrinkling of fabrics not only affects their aesthetic appeal and comfort but also leads to increased wear and a reduced lifespan. Consequently, wrinkle resistance has become a critical metric in evaluating fabric performance. Traditional wrinkle grading methods rely heavily on expert assessments, which are susceptible to visual fatigue, resulting in subjective and inconsistent classification outcomes. To address this issue, this article proposes an automatic wrinkle grading model based on an improved African Vultures Optimization (AVO) algorithm, which optimizes a regularized random vector functional link (RRVFL) network. The model enhances convergence speed and classification accuracy by incorporating the Harris Hawks Optimization (HHO) algorithm to provide a superior initial population for the AVO algorithm. Additionally, the AVO algorithm’s local search capability is strengthened during the optimization process, improving the model’s stability and resistance to overfitting. Experimental results demonstrate that the proposed HHO-AVO-RRVFL model achieves an average classification accuracy of 97.86% across multiple datasets, significantly outperforming other comparative algorithms while also excelling in classification stability and convergence speed.

On Waterfall's cryptoeconomic forecast

This work presents a system dynamics modeling approach to evaluate critical economic metrics on the Waterfall platform, a decentralized public network. The constructed model serves as a significant tool for understanding the intricate economic processes within the platform and supports informed decision-making by stakeholders. By using input parameters such as the number of network validators and transactions per second (TPS) over time, the model projects key metrics like the inflation rate and total supply of coins, providing insights into the platform's economic stability. The Waterfall platform is designed as a scalable smart contract ecosystem, underpinned by cryptoeconomics, to ensure a self- sustaining and self-regulating environment that maximizes stakeholder benefits. The platform's mainnet, launched in June 2024, has shown consistency between testnet results and theoretical calculations, demonstrating the robustness of the underlying economic model. However, as with any economic system, forecasting is essential for decision-making and improving efficiency. The system dynamics model allows for the exploration of different scenarios, considering various growth assumptions for validators and TPS, to project future economic conditions of the platform. Three scenarios – pessimistic, realistic, and optimistic – are considered, each with distinct growth trajectories for the number of validators and TPS. The outcomes highlight that while the initial stages of the network are characterized by high reward rates due to a smaller number of validators, these rates decline as more validators join, affecting the inflation rate over time. The relationship between the minting of new coins (as validator rewards) and the burning of transaction fees determines the system's inflationary dynamics. Under certain conditions, an increase in TPS could lead to deflation, enhancing the platform's economic sustainability. The results suggest that Waterfall's cryptoeconomic model has a strong potential for long-term stability and growth. The model's flexibility allows for adjustments based on real-time data and expert judgments, making it a valuable tool for both internal stakeholders and external analysts. This approach could also be applied to studying the cryptoeconomics of other decentralized networks, providing a broader understanding of their economic stability and long-term viability.

Four-dimensional homogeneous critical metrics for quadratic curvature functionals

We determine all homogeneous metrics which are critical for some quadratic curvature functional in dimension four.

Effective utilization of waste plastics and ammonia as biodiesel to assess performance and emission

PurposeThis study aims to examine the environmental effects of plastic waste on the atmosphere and its implications for disaster waste management. It focuses on using ammonia, pyrolyzed plastic oil and the effectiveness of alumina nanoparticles as a catalyst.Design/methodology/approachThe research explores different combinations of conventional diesel and nano Al2O3 derived from pyrolyzed plastic oil (ranging from P10 to P40). Critical performance metrics evaluated include brake mean effective pressure (BMEP), brake specific fuel consumption, brake thermal efficiency and emissions of CO2, CO and NOx. The study specifically investigates the impact of adding 50 ppm of Al2O3 nanoparticles to these blends.FindingsThe findings indicate that using blended fuels with nanoadditives significantly lowers pollution. Specifically, the P30 blend with 50 ppm of Al2O3 nanoparticles greatly reduced CO emissions. Additionally, the same blend reduced NOx emissions and CO2 emissions. The P30 mix showed improved BMEP and brake thermal efficiency due to its density, calorific value and viscosity (6.3 bar). The P30 blend exhibited higher thermal efficiency due to decreased heat loss, whereas conventional diesel demonstrated the best mechanical efficiency due to its longer ignition delay.Originality/valueThis study highlights the potential of using Al2O3 nanoparticles and pyrolyzed plastic oil to reduce emissions and enhance the performance of internal combustion engines. It underscores the environmental benefits and implications for disaster waste management by converting plastic waste into useful resources and reducing air pollution.

Intrusion detection systems for the internet of things: a probabilistic anomaly detection approach

The expansion of IoT domains presents new cybersecurity challenges. In this paper, we propose an enhanced Intrusion Detection System (IDS) designed to tackle these emerging security issues. Our approach harnesses the power of unsupervised learning techniques, particularly the Variational Autoencoder (VAE), to reduce data dimensionality while preserving essential features. By integrating advanced clustering algorithms–including K-means, Gaussian Mixture Model (GMM), and Variational Bayesian Gaussian Mixture Model (VBGMM)–we facilitate precise data categorization within IoT networks. Our conducted experiments using the IoT Network Intrusion Dataset demonstrate that VAE-based methods consistently outperform traditional Autoencoders (AE), achieving superior performance across critical metrics such as False Alarm Rate (FAR), Detection Rate (DR), Accuracy, Precision, and Area Under the ROC Curve (AUC).

Optimizing Indoor Localization Precision: Advancements in RFID Strategies for Enhanced Performance

Abstract RFID technology is being used more and more for complex interior localization, as a result of the quick spread of mobile devices and the smooth incorporation of artificial intelligence (AI). But conventional approaches have severe difficulties with antenna placement and constrained signal feature sizes, which limits their performance. To improve indoor localization and get over these drawbacks, this study presents a novel method that combines the Unscented Kalman Filter (UKF) with Wavelet Neural Networks (WNN).The most significant development is the incorporation of phase data into input vectors along with Received Signal Strength Indication (RSSI), which significantly increases accuracy. Empirical results show notable improvements of 88.9%, 99.2%, 95.2%, and 37.5% in critical metrics like Mean Localization Error (MLE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Localization Error Offset (LEO) in particular. This work highlights the critical role of sophisticated techniques like WNN and UKF, highlighting the importance of phase as a critical component, to improve the performance of RFID-based indoor localization. The results indicate a promising trajectory for future advancements in this field as well.

Steam Stripping for Recovery of Ammonia from Wastewater Using a High-Gravity Rotating Packed Bed

Steam stripping of ammonia from ammonia-rich wastewater (5000–20,000 mg/L) was conducted in a continuous-flow rotating packed bed (RPB) at a pH of 11. This study aimed to elucidate the influence of key operational parameters, including the steam-to-liquid ratio, rotational speed (ω), initial ammonia concentration, steam inlet temperature (TSi), and liquid inlet temperature (TLi), on critical performance metrics such as the ammonia removal efficiency (ARE), the volumetric liquid mass transfer coefficient (KLa), and the concentration of the recovered ammonia solution (CR). The findings revealed that a CR of 22.88 wt.% was achieved under the optimal conditions of a steam-to-liquid ratio of 0.175 kg/kg, an initial concentration of 20,000 mg/L, a TSi of 120 °C, and a TLi of 70 °C. Key experimental factors, including the initial ammonia concentration, TSi, and TLi, significantly impacted the achievement of higher ARE and CR values. The KLa values exhibited a decrease with the increase in the steam-to-liquid ratio, while they increased with ω. However, the KLa remained relatively consistent with ω values within the range of 600 to 1200 rpm. In comparison with prior studies, steam stripping of ammonia exhibits a higher ARE than air stripping with RPB and a higher CR than conventional stripping methods. Moreover, RPB requires a smaller size to achieve equivalent ARE compared to conventional stripping apparatuses. Thus, the steam stripping process with RPB equipment emerges as a suitable method for ammonia recovery from ammonia-rich wastewater.

Optimizing Deep Geothermal Drilling for Energy Sustainability in the Appalachian Basin

This study investigates the geological and geomechanical characteristics of the MIP 1S geothermal well in the Appalachian Basin to optimize drilling and address the wellbore stability issues encountered. Data from well logs, sidewall core analysis, and injection tests were used to derive elastic and rock strength properties, as well as stress and pore pressure profiles. A robust 1D-geomechanical model was developed and validated, correlating strongly with wellbore instability observations. This revealed significant wellbore breakout, widening the diameter from 12 ¼ inches to over 16 inches. Advanced technologies like Cerebro Force™ In-Bit Sensing were used to monitor drilling performance with high accuracy. This technology tracks critical metrics such as bit acceleration, vibration in the x, y, and z directions, Gyro RPM, stick-slip indicators, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface compared to downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings.

130 Meta-analysis of the impact of Quebracho and Chestnut tannin extract supplementation on the growth performance in growing and finishing beef cattle

Abstract Integrating tannin extracts (TE) into the diet of growing and finishing beef cattle has shown promise in optimizing protein utilization while potentially mitigating methane emissions. Despite these potential benefits, there is a notable gap in a comprehensive analysis of the influence of TE on growth rate. This study addressed this gap by collecting data on the supplementation of a blend of condensed (from Schinopsis spp.) and hydrolyzable (from Castanea spp.) tannins, which has about 70% total tannins, and its impact on critical metrics, such as average daily gain (ADG, kg/d), dry matter intake (DMI, kg/d), and feed efficiency ratio (FER, g/kg). Our objective was to conduct meta-regression analyses to understand better the nuanced effects of condensed tannin supplementation on the overall performance of beef cattle during the critical stages of growth and finishing. The dataset was constructed by collecting published studies that reported in vivo experimental data on growing and finishing beef cattle supplemented with different levels of TE. It contained 132 observations from 35 publications. The meta-analysis was conducted by linear regression with a mixed-effects model fitted by the restricted maximum likelihood method using the nlme package of R software (ver. 4.3.1). Studies were assumed as random variables, and their interaction with the intercept and slope were included in the regression. The number of animals per treatment was used as a study-specific weight. Studies accounted for 32%, 35%, or 27% of the total variance when regressing ADG, DMI, or FER, respectively, on TE (% DM). After adjusting for random effects, the ADG increased (P < 0.001) with increasing levels of dietary TE (P < 0.001): 1.344 (± 0.0071) + 0.240 (±0.0298) × TE (n = 99, r2 = 0.40). It was observed that as the dietary TE level increased, its impact on the ADG tended to decrease from 0.33 kgּ d-1ּ % TE-1 when fed up to 0.5% TE to 0.24 kgּ d-1ּ % TE-1 when fed up to 1.5% TE, suggesting a possible nonlinear relationship between these two variables. Similarly, DMI decreased slightly (P < 0.001) at 0.61 kg/d/% TE, but with a lower precision (n = 115, r2 = 0.14). Consequently, FER increased (P < 0.001) at 17 kgּ d-1ּ % TE-1 (n = 93, r2 = 0.34). Initial findings suggest that incorporating TE into the diet of growing and finishing cattle, up to a concentration of 0.5% DM, positively influences the growth rate and enhances FER while minimally affecting DMI. Subsequent research endeavors should delve into a more intricate examination of optimal TE inclusion levels and explore additional performance indicators for a comprehensive understanding of its impact.