Effective Values Research Articles

In-depth study of direct and derived acoustic emission parameters and their information content for classifying lubricated tribological sliding contacts

Acoustic signals (AE) have become a valuable tool for monitoring various tribological systems. This study investigates three key tribological behaviors: the initial running-in process, severe wear, and the lubrication state of journal bearings using four AE postprocessing methods. These methods are friction power-scaled quadratic mean (SRMS), power spectrum (PSD), normalized power spectrum (nPSD), and a statistical hit-rate (HR) analysis of the raw AE signal. The research draws on data from four tests conducted in three set-ups: Ring-on-Disc (RoD), Ring-on-Liner (RoL), and Journal-bearing-adapter (JBA) test configurations. AE measurements were taken at both 1 Hz and 1 kHz for the quadratic mean value (RMS), as well as at 900 kHz for short time intervals during each test to capture the raw signal data. In conjunction with other measured parameters like friction coefficient, temperature, contact potential, and wear, we establish correlations between the acoustic parameters and tribological behaviors. The findings indicate that SRMS and nPSD are valuable for identifying the end of the initial running-in phase. All four methods are effective in recognizing high wear states, with nPSD and HR even distinguishing between different wear phenomena. The lubrication state of journal bearings is best assessed using nPSD, allowing users to monitor certain geometric changes in the bearing.

A state-space growth model for Eucalyptus pilularis in subtropical Australia, fitted with and without seemingly unrelated regression

ABSTRACT Inventory data from even-aged, monoculture blackbutt (Eucalyptus pilularis) forests in subtropical eastern Australia were used with the algebraic difference approach to construct a state-space model that predicted stand growth with age for each of quadratic mean diameter, basal area and dominant height, given some initial measurement of the stand. Conventional regression analysis was used to fit each part of the model separately and seemingly unrelated regression (SUR) was used to fit them jointly. Although using SUR resulted in slightly more efficient parameter estimators than when the models were fitted separately, there was no evidence that its use provided any appreciably better fit to the data or precision of estimates of future sizes. Other work in forest science that has used SUR is reviewed briefly and appears to have gained little advantage from its use.

Development of an Image-based Method for Tablet Microstructure Description and Its Correlation with API Release Rate

The performance of a pharmaceutical formulation, such as the drug (API) release rate, is significantly influenced by the properties of the materials used, the composition of the final product and the tablet compression process parameters. However, in some cases, the knowledge of these input parameters does not necessarily provide a reliable description or prediction of tablet performance. Therefore, the knowledge of tablet microstructure is desirable to understand such formulations. Commonly used analytical techniques, such as X-ray tomography and intrusion mercury porosimetry, are not widely used in pharmaceutical companies due to their price and/or toxicity, and therefore, efforts are made to develop a tool for fast and easy microstructure description. In this work, we have developed an image-based method for microstructure description and applied it to a model system consisting of ibuprofen and CaHPO4∙2H2O (API and excipient with different deformability). The obtained parameter, the quadratic mean of the equivalent diameter of the non-deformable, brittle excipient CaHPO4∙2H2O, was correlated with tablet composition, compression pressure and API release rate. The obtained results demonstrate the possibility of describing the tablet dissolution performance in the presented model system based on the microstructural parameter, providing a possible model system for compressed solid dosage forms in which a plastic component is present and specific API release is required.Graphical

Self‐adaptive search optimization‐based vehicle path prediction and traffic light controller in vehicular ad hoc network

SummaryIn recent days, VANET is considered as the main hopeful equipment in the system of transportation since traffic congestion arises regularly and thus occurred road accidents very easily. Besides, the network traffic is increasing due to the huge quantity of information generated in region of urban. Therefore, one of the primary challenges faced by Intelligent Transportation System (ITS) is ensuring the accurate transmission of information in both vehicle‐to‐vehicle (V2V) and vehicle‐to‐road (V2R) sensing unit communications. So, here, an adaptive routing controller (ARC) is utilized to improve the data transmission between vehicle and road sensing unit (RSU) and to minimize the traffic density in VANET, and the self‐adaptive search optimization is developed in VANET clustering to prioritize vehicles in the lane, in which the multi‐objective function is intended depending on the energy of the node, acceleration, jitter, priority, velocity, and trust factors. The traffic light control is done to facilitate the effective communication in the network. Thus, the proposed technique is calculated in terms of throughput, jitter, quadratic mean of acceleration (QMA), and spatially distributed travel time (SDTT), which acquired the values of 0.53 s, 36.07 kmph, 3.472 s, and 43.572%, respectively, while using 50 vehicles at 50 s.

Traffic Prediction and Signal Controlling Using Mode-search Optimization Based Deep Long Short Term Memory Classifier

Aim: The research aims at developing a traffic prediction and signal controlling model based on deep learning technique in order to provide congestion-free transportation in Intelligent Transport System (ITS). Need for the Research: Recent technical advancements in the ITS, industrialization, and urbanization increase traffic congestion, which leads to high fuel consumption and health issues. This signifies the need for a dynamic traffic management system to handle the traffic congestion issues that negatively affect the transportation service. Methods: For promoting congestion-free transportation in the ITS, this research aims to devise a traffic prediction and control system based on deep learning techniques that effectively controls the traffic during peak hours. The proposed mode-search optimization effectively clusters the vehicles based on the necessity. In addition, the mode-search optimization tunes the optimal hyperparameters of the deep Long Short Term Memory classifier, which minimizes the training loss. Further, the traffic signal control system is developed through the mode-search-based deep LSTM classifier for predicting the path of the vehicles by analyzing the attributes, such as velocity, acceleration, jitter, and priority of the vehicles. Result: The experimental results evaluate the efficacy of the traffic prediction model in terms of quadratic mean of acceleration (QMA), jitter, standard deviation of travel time (SDTT), and throughput, for which the values are found to be 37.43, 0.23, 8.75, and 100 respectively. Achievements: The proposed method attains the performance improvement of 5% to 42% when compared with the conventional methods.

Optimization of water quality index models using machine learning approaches.

To optimize the water quality index (WQI) assessment model, this study upgraded the parameter weight values and aggregation functions. We determined the combined weights based on machine learning and game theory to improve the accuracy of the models, and proposed new aggregation functions to reduce the uncertainty of the model. A new water quality assessment system was established, and took the Chaobai River Basin as a case study. To optimize the weight, two combined weights were established based on game theory. The weight CWAE was combined by the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). The weight CWAL was combined by AHP and machine learning (LightGBM). CWAL was judged to be an optimal composite weight by comparing the coefficient of variation (CV) values and the Kaiser-Meyer-Olkin (KMO) extracted values. To reduce the uncertainty of the model, we proposed two aggregation functions, the Sinusoidal Weighted Mean (SWM) and the Log-weighted Quadratic Mean (LQM). The three water quality assessment models (WQIS, WQIL and WQIW) were established based on the optimal weights besides. All three models had good reliability. Both WQIS and WQIW models had low eclipsing problems (25.49% and 18.63%). The accuracy of the models was ranked as WQIS > WQIW > WQIL. The uncertainty of WQIs (0.000) in assessing poor water quality was low, and so was WQIW (0.259) in assessing good water quality. Overall, the WQIS model was recommended for assessing poor water quality and the WQIW model was recommended for assessing good water quality. The assessment results of WQIS showed that the Chaobai River Basin was "slightly polluted", and the water quality upstream was better than that downstream. TN was the main pollutant in the basin, and there was slight pollution with CODMn, CODCr, BOD5, etc. There was little metal contamination, only a few months exceeded Class I. The model established in this study can provide a reference for the same type work of water quality assessment. The assessment results can provide a scientific basis for the protection of the regional water environment.

Understanding Soil Respiration Dynamics in Temperate Forests in Northwestern Mexico

Temperate mixed forests in Mexico are considered highly important ecosystems because of their high levels of biodiversity and capacity to store carbon. The aim of this study was to evaluate temporal and between-forest soil respiration (CO2 efflux) variability, and to assess the effect of vegetation diversity metrics on soil CO2 fluxes in mixed-uneven-aged forests in Durango, Northwestern Mexico. Soil CO2 efflux, soil moisture, and soil temperature were measured in three temperate forest types. A generalized linear model (GLM) was fitted to analyze the relationship between soil CO2 fluxes and stand variables, diversity metrics, soil moisture, and soil temperature. Furthermore, a two-way analysis of variance was used to assess the effect of forest type, month of the year, and their interaction on soil respiration. Annual average, minimum, and maximum soil CO2 efflux rate values were 3.81 (±2.94), 2.28 (±1.47), and 7.97 (±2.94) µmol m−2 s−1, respectively. Soil respiration was positively related to species richness, aboveground biomass, and quadratic mean diameter; however, forest type did not contribute to understanding the dynamics of soil CO2 fluxes. The results highlight the importance of seasonality, species diversity and aboveground biomass stocks to preserve the ecosystem processes driving soil respiration in temperate forests.

Radiomics in the characterization of lipid-poor adrenal adenomas at unenhanced CT: time to look beyond usual density metrics

ObjectivesIn this study, we developed a radiomic signature for the classification of benign lipid-poor adenomas, which may potentially help clinicians limit the number of unnecessary investigations in clinical practice. Indeterminate adrenal lesions of benign and malignant nature may exhibit different values of key radiomics features.MethodsPatients who had available histopathology reports and a non-contrast-enhanced CT scan were included in the study. Radiomics feature extraction was done after the adrenal lesions were contoured. The primary feature selection and prediction performance scores were calculated using the least absolute shrinkage and selection operator (LASSO). To eliminate redundancy, the best-performing features were further examined using the Pearson correlation coefficient, and new predictive models were created.ResultsThis investigation covered 50 lesions in 48 patients. After LASSO-based radiomics feature selection, the test dataset’s 30 iterations of logistic regression models produced an average performance of 0.72. The model with the best performance, made up of 13 radiomics features, had an AUC of 0.99 in the training phase and 1.00 in the test phase. The number of features was lowered to 5 after performing Pearson’s correlation to prevent overfitting. The final radiomic signature trained a number of machine learning classifiers, with an average AUC of 0.93.ConclusionsIncluding more radiomics features in the identification of adenomas may improve the accuracy of NECT and reduce the need for additional imaging procedures and clinical workup, according to this and other recent radiomics studies that have clear points of contact with current clinical practice.Clinical relevance statementThe study developed a radiomic signature using unenhanced CT scans for classifying lipid-poor adenomas, potentially reducing unnecessary investigations that scored a final accuracy of 93%.Key Points• Radiomics has potential for differentiating lipid-poor adenomas and avoiding unnecessary further investigations.• Quadratic mean, strength, maximum 3D diameter, volume density, and area density are promising predictors for adenomas.• Radiomics models reach high performance with average AUC of 0.95 in the training phase and 0.72 in the test phase.

Built-up area mapping using Sentinel-1 SAR data

A timely and accurate spatial mapping of built-up areas (BA) is crucial in making cities and human settlements safe, resilient, and sustainable. Synthetic Aperture Radar (SAR) data are useful for BA mapping due to strong coherent backscatter from diverse human-made targets, distinct texture patterns, and sensitivity to its geometric characteristics. However, BA mapping using SAR data is still challenging due to various geometrical and physical factors that often lead to misinterpretations. Therefore, this study presents a novel method to map BA using single-date dual polarized Sentinel-1 SAR data to overcome such challenges in complex built-up scenarios. Here, we propose three normalized descriptors derived from the Stokes vector elements. These three descriptors are found to be vital in characterizing different types of BA structures. We then consider the symmetric quadratic mean of the normalized descriptors to propose a novel dual polarimetric radar built-up index (DpRBI). The proposed index provides information about the average normalized polarized power, which is useful for mapping diverse types of BA or their combination in an image. We assess the proposed methodology utilizing the Sentinel-1 image acquired over three major cities: Delhi, Milan, and Barcelona. The BA map accuracy for all three cities was found to be ≈ 85% with a low BA omission error. We compared the mapping results with available BA/land cover products: World Settlement Footprint 2019 (WSF-19) and World cover 2020 (WC-20). The BA map obtained using the proposed index was better than WSF-19 and WC-20, particularly for BA omission errors. For example, we observed a relatively low BA omission error of 22.34% using DpRBI compared to 31.86% and 41.79% for WC-20 and WSF-19, respectively, over Milan, Italy. The code is available at: https://github.com/navv37/DpRBI

Acute Effects of Blood Flow Restriction Training on Movement Velocity and Neuromuscular Signal during the Back Squat Exercise.

Twenty-four university students participated in this study. In two randomized sessions 72 h apart, participants performed a 4-set protocol consisting of 30-15-15-15 repetitions performed at 30% of their one-repetition maximum in the back squat exercise. In both sessions, neuromuscular function was monitored by surface electromyography (EMG) and movement velocity (mean propulsive velocity (MPV), peak concentric velocity (Vmax), and the effort index (EI)). Blood flow restriction (BFR) was applied during exercise in one of the experimental sessions with 80% of full arterial occlusion pressure over lower limbs. The BFR condition showed higher (p < 0.05) EI, peak, and rooted mean square normalized EMG in Set 1 compared to Set 2. Similar MPV and Vmax were observed in each set for both the BFR and control conditions. No significant differences were observed between conditions in any set. BFR did not imply changes in neuromuscular performance during low-intensity resistance training, but it might induce greater intra-series velocity loss and less excitation of the muscles involved.

The Importance of Using Permanent Plots Data to Fit the Self-Thinning Line: An Example for Maritime Pine Stands in Portugal

Density-dependent mortality occurs in the evolution of even-aged populations when these approach crown closure age. This density-dependent mortality is regulated by the so-called “3/2 power law of self-thinning” that assumes a constant slope for the line relating the log of stand density with the log of the average tree size, the self-thinning line or maximum size–density relationship, MSDR. A good estimate of the self-thinning line is therefore an essential component to any forest growth model. Two concepts for the MSDR have emerged: (1) a static upper limit for the species; and (2) a dynamic self-thinning line influenced by several factors (e.g., management techniques, site quality and/or genetics). The objective of this study was to estimate a new static self-thinning line based on the quadratic mean diameter at breast height (Reineke’s self-thinning line) for the generalized use in maritime pine growth models in Portugal. Data from 41 observations obtained in nine long-term permanent experimental trials of maritime pine species were carefully selected from a data set of 186 plots as being under self-thinning. Two methods were used: OLS and mixed linear models. An exploratory analysis on the impact of each environmental variable on the slope and intercept of the self-thinning line led to the selection of a subset of environmental variables later used in an all possible regressions algorithm to find the subsets leading to the lowest values of Akaike information criterion (AIC). The OLS procedure showed that the differences between the plots could be explained by site index, by climate variables (e.g., evaporation or climatic indices) and the use of more than one covariable slightly improved the fit. Nevertheless, the best MSDR line fitted with mixed linear models (ln N = 12.97158 − 1.83926 ln dg) having the plot random effect in the intercept, largely outperformed the best OLS model and is therefore recommended for generalized use in forest growth models.

Marine waters assessment using improved water quality model incorporating machine learning approaches

In marine ecosystems, both living and non-living organisms depend on “good” water quality. It depends on a number of factors, and one of the most important is the quality of the water. The water quality index (WQI) model is widely used to assess water quality, but existing models have uncertainty issues. To address this, the authors introduced two new WQI models: the weight based weighted quadratic mean (WQM) and unweighted based root mean squared (RMS) models. These models were used to assess water quality in the Bay of Bengal, using seven water quality indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), and molybdate reactive phosphorus (MRP). Both models ranked water quality between “good” and “fair” categories, with no significant difference between the weighted and unweighted models’ results. The models showed considerable variation in the computed WQI scores, ranging from 68 to 88 with an average of 75 for WQM and 70 to 76 with an average of 72 for RMS. The models did not have any issues with sub-index or aggregation functions, and both had a high level of sensitivity (R2 = 1) in terms of the spatio-temporal resolution of waterbodies. The study demonstrated that both WQI approaches effectively assessed marine waters, reducing uncertainty and improving the accuracy of the WQI score.

Ultra-fast 3D printing of assembly—free complex optics with sub-nanometer surface quality at mesoscale

Complex-shaped optical lenses are of great interest in the areas of laser processing, machine vision, and optical communications. Traditionally, the processing of complex optical lenses is usually achieved by precision machining combined with post-grinding or polishing, which is expensive, labor-intensive and difficult in the processing of ultra-complex optical lenses. Additive manufacturing is an emerging technology that provides significant advantages in producing highly intricate optical devices. However, the layer-by-layer method employed in such manufacturing processes has resulted in low printing speeds, as well as limitations in surface quality. To address these challenges, we apply tomographic volumetric printing (TVP) in this work, which can realize the integrated printing of complex structural models without layering. By coordinating the TVP and the meniscus equilibrium post-curing methods, ultra-fast fabrication of complex-shaped lenses with sub-nanometric roughness has been achieved. A 2.5 mm high, outer diameter 9 mm spherical lens with a roughness value of RMS = 0.3340 nm is printed at a speed of 3.1 × 104 mm3 h−1. As a further demonstration, a complex-shaped fly-eye lens is fabricated without any part assembly. The designed spherical lens is mounted on a smartphone’s camera, and the precise alignments above the circuit board are captured. Upon further optimization, this new technology demonstrates the potential for rapid fabrication of ultra-smooth complex optical devices or systems.

Using LiDAR Data to Estimate Biomass in Afforested Bottomland Oak Sites in the Southern United States

Abstract The extent of bottomland hardwood forests in the Lower Mississippi Alluvial Valley (LMAV) has diminished, and federal programs like the Conservation Reserve Program provide incentives to afforest marginal agricultural areas with oaks to provide ecosystem services. Remote sensing technologies, like light detection and ranging (LiDAR), can be used to estimate biomass of these stands to potentially allow landowners to take advantage of carbon markets, but data are expensive to collect. Therefore, we determined whether freely available low-density LiDAR data could capture variability in tree- and stand-level characteristics in the LMAV, including aboveground biomass. We found that multiple regression LiDAR models captured more variability in tree-level than stand-level parameters and including soil type generally improved models. Model r2 values predicting tree and stand parameters including tree height, height to the live crown, quadratic mean diameter, crown area, trees per hectare, stand basal area, and stand biomass ranged from 0.34 to 0.82 and root mean square percent error (RMSPE) ranged from 7% to 36%. Specifically, models for stand biomass had an RMSE of about 19 Mg/ha or about 19% of mean values across sites. Therefore, freely available LiDAR data was useful in evaluating afforested bottomland oak sites for tree- and stand-level structural components in the LMAV.

Remaining Useful Life Estimation of Spindle Bearing Based on Bearing Load Calculation and Off-Line Condition Monitoring

Spindles are key components of machine tools. An efficient estimation of the spindle condition and its prognosis can improve production efficiency and quality due to predictive maintenance planning. This paper proposes a method for predicting the remaining useful life (RUL) of machine tool spindle bearings using a combined calculation and experimental approach. The calculation model based on the ISO 281 standard uses monitored real loading conditions caused by the machining process and the machine tool operation. The model enables the updated calculation of the spindle lifetime L10h using real load distribution. Since the operation hours of the spindle are also monitored, the remaining useful life (RUL) of the spindle can be calculated. This RUL value is corrected using a bearing condition assessment based on the effective value of the vibration velocity RMS according to the ISO 20816 standard and measured data from the machine tool control system. The proposed method is tested on two different spindle types featuring three pieces of every type. The experimental results of six spindles are compared and validated with a concurrent blind evaluation conducted by a skilled expert. The validation shows a very good match of the proposed method and the expert opinion. The method combining a calculation of the spindle lifetime using monitored real load distribution and subsequent result correction using vibration signal enables the implementation of a full automated estimation of the spindle RUL.

Numeraire-Invariant Quadratic Hedging and Mean–Variance Portfolio Allocation

The paper investigates quadratic hedging in a semimartingale market that does not necessarily contain a risk-free asset. An equivalence result for hedging with and without numeraire change is established. This permits direct computation of the optimal strategy without choosing a reference asset and/or performing a numeraire change. New explicit expressions for optimal strategies are obtained, featuring the use of oblique projections that provide unified treatment of the case with and without a risk-free asset. The analysis yields a streamlined computation of the efficient frontier for the pure investment problem in terms of three easily interpreted processes. The main result advances our understanding of the efficient frontier formation in the most general case in which a risk-free asset may not be present. Several illustrations of the numeraire-invariant approach are given.

Structure and Spatial Distribution of the Abundance of &amp;lt;i&amp;gt;Spondias mombin&amp;lt;/i&amp;gt; L. Populations in the Subequatorial Zone of Benin (West Africa)

Conservation and sustainable use strategies for reliable management plans of valuable species are lacking due to the gap in basic scientific information on their structure and status. The objectives of this work were to study the structural characteristics and the spatial distribution of the abundance of <i>Spondias mombin</i> L. populations in the subequatorial zone of Benin. The data collection was based on the tessellation of study zone with ArcGIS analysis tools. The grid cell used was a square of 100 m side (1ha of surface). The occurrence of species in each grid was obtained using GBIF database. One hundred and seventy-five (175) trees of <i>S. mombin</i> were sampled through the four Phytodistricts of study zone. The main dendrometric variables measured from these individuals were diameter at breast height (1.30 m above ground level) and height. The parameters such as density, mean quadratic diameter, basal area and mean Lorey height were calculated. To study the spatial abundance distribution of <i>S. mombin,</i> we used Inverse Distance Weighting (IDW) interpolation method to realize abundance map from the occurrences. The results of structure showed a density of 3 trees/ha. The basal area was 1500 cm²/ha and the mean quadratic diameter was 25.10 cm. The mean of Lorey height obtained was 12 m<i>.</i> The diameter structure showed a predominance of bell-shape distribution with a relative abundance of young individuals. The low numbers of individuals in some diameter classes, the erratic distribution and the presence of old population of <i>S. mombin</i> indicate the strong anthropogenic pressure on the species. The abundance map showed that the species was present in all the phytodistricts and covers 93% of the study area’s municipalities. The conservation strategies, including both the species and its habitat, need to be developed to allow sustainable use of <i>S. mombin</i>.

RETRACTED ARTICLE: Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient

We, the Editors and Publisher of Statistics, have retracted the following article: Antoine Godichon-Baggioni. Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient. Statistics. 57:3. 2023. 637–668. doi:10.1080/02331888.2023.2213371 The author raised concern regarding an error from an inequality (9) on page 647. This inequality cannot be verified if the gradient is bounded. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Canopy and surface fuel estimations using RPAS and ground-based point clouds

Abstract Forest management activities intended to reduce wildfire risk rely on accurate characterizations of the amount and arrangement of canopy and surface fuels. Metrics that describe these fuels are typically estimated with various systems that transform plot-level field data into metrics that can be used within fire behaviour models. Remote sensing data have long been used to estimate these metrics across large spatial scales, but more advanced, high-density point clouds have the potential to estimate these metrics with higher accuracy. This study collected LiDAR and digital aerial photogrammetric (DAP) point clouds from a remotely piloted aerial system (RPAS), as well as mobile laser scanning (MLS) point clouds from a mobile ground-based system, and compared their ability to estimate fuel metrics. This involved the extraction of predictor variables from each point cloud, of which small subsets were used to estimate various fuel metrics. These included six overstory canopy metrics (stand height, canopy cover, tree density, canopy fuel load, canopy bulk density and canopy base height), three diameter at breast height (DBH)–related metrics (stand density index, basal area and quadratic mean diameter) and three surface fuel metrics (total woody debris (TWD), coarse woody debris (CWD) and fine woody debris (FWD)). Overall, canopy metrics were estimated most accurately by the RPAS LiDAR models, although none of the point clouds were able to accurately estimate DBH-related metrics. For the other six canopy metrics, RPAS LiDAR models had an average R2 value of 0.70; DAP – 0.63 and MLS – 0.63. CWD (&amp;gt;7 cm) and TWD loads were estimated most accurately by the MLS models (average R2 values – 0.70), followed by the RPAS LiDAR – 0.38 and DAP – 0.13. None of these models were able to accurately estimate FWD loads (≤7 cm in diameter), with the three types of point clouds having a maximum R2 value of 0.08. Overall, this research shows the relative ability of three types of high-density point clouds to estimate metrics relevant for fire behaviour modeling.

Modeli rasta prirodnih sastojina kalabrijskog bora u središnjem mediteranskom području Turske

Stand growth models are needed for a variety of forestry practices, primarily management plans and silvicultural studies. The goal of this study was to create stand-level models for natural, pure even-aged stands of Calabrian pine in the central Mediterranean region of Turkey. The study area consists of pure and natural Calabrian pine stands located within the boundaries of the Antalya and Mersin Regional Forestry Directorates in the central Mediterranean region of Turkey. Data was collected from 486 temporary plots scattered throughout the region. Two trees (the dominant tree and the tree representing the quadratic mean diameter of the stand) were measured in each plot, yielding 972 trees. The data showed that the age varied from 6 to 135 years, the site index (SI) from 8.5 to 33.5 m, and the density from 0.3 to 12.4. The density-dependent yield tables were generated using regression equations based on stand age, SI, and stand density with individual, two-factor, and three-factor interaction effects. For SI classes I, II, and III, the optimal rotation period that would result in the highest yields for pure Calabrian pine stands is 60, 65, and 75 years, respectively. The stand growth models developed (i.e., density-dependent yield tables) agreed with the fundamental growth principles and data provided in the literature.