Incomplete Samples Research Articles

Zirconium isotope composition indicates s‐process depletion in samples returned from asteroid Ryugu

AbstractNucleosynthetic isotope variations are powerful tracers to determine genetic relationships between meteorites and planetary bodies. They can help to link material collected by space missions to known meteorite groups. The Hayabusa 2 mission returned samples from the Cb‐type asteroid (162173) Ryugu. The mineralogical, chemical, and isotopic characteristics of these samples show strong similarities to carbonaceous chondrites and in particular CI chondrites. The nucleosynthetic isotope compositions of Ryugu overlap with CI chondrites for several elements (e.g., Cr, Ti, Fe, and Zn). In contrast to these isotopes, which are of predominately supernovae origin, s‐process variations in Mo isotope data are similar to those of carbonaceous chondrites, but even more s‐process depleted. To further constrain the origin of this depletion and test whether this signature is also present for other s‐process elements, we report Zr isotope compositions for three bulk Ryugu samples (A0106, A0106‐A0107, C0108) collected from the Hayabusa 2 mission. The data are complemented with that of terrestrial rock reference materials, eucrites, and carbonaceous chondrites. The Ryugu samples are characterized by distinct 96Zr enrichment relative to Earth, indicative of a s‐process depletion. Such depletion is also observed for carbonaceous chondrites and eucrites, in line with previous Zr isotope work, but it is more extreme in Ryugu, as observed for Mo isotopes. Since s‐process Zr and Mo are coupled in mainstream SiC grains, these distinct s‐process variations might be due to SiC grain depletion in the analyzed materials, potentially caused by incomplete sample digestion, because the Ryugu samples were dissolved on a hotplate only to avoid high blank levels for other elements (e.g., Cr). However, local depletion of SiC grains cannot be excluded. An alternative, equally possible scenario is that aqueous alteration redistributed anomalous, s‐process‐depleted, Zr on a local scale, for example, into Ca‐phosphates or phyllosilicates.

Estimating trauma prevalence from incomplete human skeletal remains

Traumatic lesions on human skeletal remains are widely used for reconstructing past accidents or violent encounters and for comparing trauma prevalence across samples over time and space. However, uncertainties in trauma prevalence estimates increase proportionally with decreasing skeletal completeness, as once-present trauma might have gone missing. To account for this bias, samples are typically restricted to skeletal remains meeting a predefined minimum completeness threshold. However, the effect of this common practice on resulting estimates remains unexplored. Here, we test the performance of the conventional frequency approach, which considers only specimens with ≥ 75% completeness, against a recent alternative based on generalized linear models (GLMs), integrating specimen completeness as a covariate. Using a simulation framework grounded on empirical forensic, clinical, and archaeological data, we evaluated how closely frequency- and GLM-based estimates conform to the known trauma prevalence of once-complete cranial samples after introducing increasing levels of missing values. We show that GLM-based estimates were consistently more precise than frequencies across all levels of incompleteness and regardless of sample size. Unlike GLMs, frequencies increasingly produced incorrect relative patterns between samples and occasionally failed to produce estimates as incompleteness increased, particularly in smaller samples. Consequently, we generally recommend using GLMs and their extensions over frequencies, although neither approach is fully reliable when applied to largely incomplete samples.

Quality Diagnosis for Rocket Body Structure Manufacturing Process Based on Product Quality DNA

ABSTRACTIn the current multivariety and small‐batch production mode, it is difficult to fully reflect the evolution law of the sampling population due to incomplete sample information in the manufacturing of the rocket body structure. By referring to the principle of biological DNA, a knowledge‐based quality diagnosis strategy is adopted, and a quality diagnosis method for the rocket body structure manufacturing process based on product quality DNA is proposed in this paper. The accumulated experience or knowledge in the past is stored in the product quality DNA knowledge base in the form of cases. Through knowledge retrieval, historical cases with the highest similarity to the product quality DNA to be diagnosed are found, and reasonable control and rectification schemes are provided for the quality problems in the production process, so as to avoid the same quality problems in the future. The feasibility of the method is verified by two case studies of two welds. The results show that the method can effectively and accurately diagnose weld quality.

Fault diagnosis based on incomplete sensor variables with a hierarchical semi-supervised Gaussian mixture classifier

We propose a hierarchical semi-supervised variational semi-Bayesian Gaussian mixture classifier based on the partially incomplete and unlabeled samples for the fault diagnosis of mechanical and electrical systems. These systems are typically complex in structure and are monitored by multiple sensors simultaneously. Some sensor variables in the collected data may be incomplete due to sensor malfunctions or transmission errors. Additionally, labeling the data can be a time-consuming and labor-intensive task, resulting in many unlabeled samples. To address these challenges, the missing sensor variables and the unavailable labels are treated as hidden values and handled within the framework of the Expectation Maximization algorithm. We employ a semi-Bayesian technique with variational inference to estimate the model parameters. Specifically, we introduce prior distribution to the mean and covariance matrix to address the possible singularity of the empirical covariance matrix. The weighting coefficients are left without putting prior distribution so that their values can decay to zero, effectively removing redundant components of the mixture model. The factorized distribution is utilized to approximate the posterior distribution of the model parameters, as well as the missing labels and other latent variables. Numerical and real case studies are carried out to verify the effectiveness of the proposed technique.

Reconstructing redshift distributions with photometric galaxy clustering

The accurate determination of the true redshift distributions in tomographic bins is critical for cosmological constraints from photometric surveys. The proposed redshift self-calibration method, which utilizes the photometric galaxy clustering alone, is highly convenient and avoids the challenges from incomplete or unrepresentative spectroscopic samples in external calibration. However, the imperfection of the theoretical approximation on broad bins as well as the flaw of the algorithm in previous work [1] risk the accuracy and application of the method. In this paper, we propose the improved self-calibration algorithm that incorporates novel update rules, which effectively accounts for heteroskedastic weights and noisy data with negative values. The improved algorithm greatly expands the application range of self-calibration method and accurately reconstructs the redshift distributions for various mock data. Using the luminous red galaxy (LRG) sample of the Dark Energy Spectroscopic Instrument (DESI) survey, we find that the reconstructed results are comparable to the state-of-the-art external calibration. This suggests the exciting prospect of using photometric galaxy clustering to reconstruct redshift distributions in the cosmological analysis of survey data.

Pengaruh Carbon Emission Disclosure dan Good Corporate Governance terhadap Nilai Perusahaan: Studi Empiris pada Perusahaan Sektor Transportasi dan Logistik yang Terdaftar di Bursa Efek Indonesia Periode 2017-2022

Purpose: This study aims to identify the impact of carbon emission disclosure, independent commissioners, and institutional ownership on firm value. Research methodology: The population used in this study consists of transportation and logistics sector business entities registered on the Indonesia Stock Exchange during the period of 2017-2022, quatitative methods, and SPSS applications. Result: This study concludes that carbon emission disclosure has an effect on firm value. Meanwhile, independent commissioners have no effect on firm value and institutional ownership has an effect on firm value. Limitations: This study identified limitations, specifically in the examination where several business entities were not included in the research sample, resulting in an incomplete sample. Contribution: This study provides empirical evidence that contributes to the literature on the impact of carbon emission disclosures, independent commissioners, and institutional ownership on firm value. Additionally, this study is expected to inform policies related to environmental responsibility in compliance with the Republic of Indonesia Presidential Regulation Number 98 of 2021 on greenhouse gas emission control in national development and OJK Regulation No. 30/SEOJK.04/2016 on corporate obligations to include environmental disclosures in annual reports.

Multi-view neural 3D reconstruction of micro- and nanostructures with atomic force microscopy

Atomic Force Microscopy (AFM) is a widely employed tool for micro- and nanoscale topographic imaging. However, conventional AFM scanning struggles to reconstruct complex 3D micro- and nanostructures precisely due to limitations such as incomplete sample topography capturing and tip-sample convolution artifacts. Here, we propose a multi-view neural-network-based framework with AFM, named MVN-AFM, which accurately reconstructs surface models of intricate micro- and nanostructures. Unlike previous 3D-AFM approaches, MVN-AFM does not depend on any specially shaped probes or costly modifications to the AFM system. To achieve this, MVN-AFM employs an iterative method to align multi-view data and eliminate AFM artifacts simultaneously. Furthermore, we apply the neural implicit surface reconstruction technique in nanotechnology and achieve improved results. Additional extensive experiments show that MVN-AFM effectively eliminates artifacts present in raw AFM images and reconstructs various micro- and nanostructures, including complex geometrical microstructures printed via two-photon lithography and nanoparticles such as poly(methyl methacrylate) (PMMA) nanospheres and zeolitic imidazolate framework-67 (ZIF-67) nanocrystals. This work presents a cost-effective tool for micro- and nanoscale 3D analysis.

Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to in situ measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions.

Separation of overlapping non-stationary signals and compressive sensing-based reconstruction using instantaneous frequency estimation

Compressive sensing uses an incomplete sample set to consider signal reconstruction with sparse basis representations over a specific transform domain. This work examines the difficulties encountered in various signal processing applications, such as data overlap with the target signal in time and frequency domains. The conventional filtering and windowing processes fail to promote better results in recovering the desired signal. The non-stationary signal with time-frequency representations is highly significant for certain valuable applications. An efficient signal separation and reconstruction method based on instantaneous frequency estimate is proposed to improve the non-stationary signal outcome. The proposed research includes speech signal data acquisition, signal component decomposition, instantaneous frequency estimation, overlapped signal separation, and reconstruction. Two datasets, Dacon Overlapped Speech and LJ Speech Dataset, are utilized to generate a new overlapped signal dataset. Initially, the generated speech signals are collected and decomposed into several monocomponents using Synchrosqueezing Wavelet Transform (SynWav), Renyi's quadratic entropy (RQuad), Narrow bandpass filtering (NBand) and single frequency filtering (SFreq). The instantaneous frequencies are estimated using the Hilbert-Huang transform (HilHuT) to generate a time-frequency representation of multi-component signals. The overlapped signals are separated and reconstructed to a desired signal using a Compressive sensing-based Discrete Cosine transform with amended intrinsic chirp separation (CDcT_AIChirS). The proposed model is simulated using PYTHON to examine the performances. In comparison, the mean square error is calculated to be 2.71, the root mean square error is calculated to be 1.64, and the signal to noise ratio is calculated to be 32dB.

Towards Better Modeling With Missing Data: A Contrastive Learning-Based Visual Analytics Perspective.

Missing data can pose a challenge for machine learning (ML) modeling. To address this, current approaches are categorized into feature imputation and label prediction and are primarily focused on handling missing data to enhance ML performance. These approaches rely on the observed data to estimate the missing values and therefore encounter three main shortcomings in imputation, including the need for different imputation methods for various missing data mechanisms, heavy dependence on the assumption of data distribution, and potential introduction of bias. This study proposes a Contrastive Learning (CL) framework to model observed data with missing values, where the ML model learns the similarity between an incomplete sample and its complete counterpart and the dissimilarity between other samples. Our proposed approach demonstrates the advantages of CL without requiring any imputation. To enhance interpretability, we introduce CIVis, a visual analytics system that incorporates interpretable techniques to visualize the learning process and diagnose the model status. Users can leverage their domain knowledge through interactive sampling to identify negative and positive pairs in CL. The output of CIVis is an optimized model that takes specified features and predicts downstream tasks. We provide two usage scenarios in regression and classification tasks and conduct quantitative experiments, expert interviews, and a qualitative user study to demonstrate the effectiveness of our approach. In short, this study offers a valuable contribution to addressing the challenges associated with ML modeling in the presence of missing data by providing a practical solution that achieves high predictive accuracy and model interpretability.

Premature ventricular complexes after catheter ablation for atrial fibrillation: Accounting for regression to the mean.

Prior studies have indicated that the frequency of premature ventricular complexes (PVC) increases after catheter ablation for atrial fibrillation (AF). However, these studies have primarily focused on patients whose PVC burden increased rather than including the full spectrum of outcomes. We performed a single-center retrospective cohort study of consecutive patients who underwent first-time AF ablation from 1/2018 to 12/2022 for paroxysmal or persistent AF and had both preablation and postablation rhythm monitoring within 6 months of the procedure. Patients were excluded if they had prior AF or PVC ablation or were prescribed a class I or III antiarrhythmic medication. Among 2945 patients who underwent AF ablation during the study period, 130 patients underwent first-time AF ablation and received both pre and post ambulatory monitoring. The median PVC burden before ablation was <1%. Most patients (63%) had no change in PVC burden after AF ablation compared with preablation, and patients who had an increase in PVC burden were offset by those with reductions in PVCs. A paired analysis pre- and post-AF ablation showed no significant change in PVC burden (p = .495). Although an elevated PVC burden after AF ablation may be seen clinically, the majority of patients have no change in burden. Studies that suggest an increase in PVC burden after AF ablation may suffer from incomplete sample selection and thus omit the important effect of regression to the mean.

Effect of iron bearing minerals on iron ore processing

In the current study, elemental analysis and microscopic investigations were conducted to characterize the minerals present in an iron ore sample. The iron content was below 24%, with the dominant iron-bearing minerals being hematite and magnetite. Consequently, gravity and magnetic separation methods were employed to enhance the iron content. However, the processing results less than what was expecting due to incomplete sample characterization. The sample was further analyzed using quantitative X-ray diffraction analysis. This more comprehensive examination revealed additional iron-bearing minerals as goethite, ankerite, and limonite. The behavior of these minerals likely contributed to the unsuccessful separation process which was thoroughly explored in this article. Understanding the mineralogical composition of iron ores is paramount for optimizing processing operations and achieving successful outcomes.

Deep-Autoencoder-Based Radar Source Recognition: Addressing Large-Scale Imbalanced Data and Edge Computing Constraints

Radar radiation source recognition technology is vital in electronic countermeasures, electromagnetic control, and air traffic management. Its primary function is to identify radar signals in real time by computing and inferring the parameters of intercepted signals. With the rapid advancement of AI technology, deep learning algorithms have shown promising results in addressing the challenges of radar radiation source recognition. However, significant obstacles remain: the radar radiation source data often exhibit large-scale, unbalanced sample distribution and incomplete sample labeling, resulting in limited training data resources. Additionally, in practical applications, models must be deployed on outdoor edge computing terminals, where the storage and computing capabilities of lightweight embedded systems are limited. This paper focuses on overcoming the constraints posed by data resources and edge computing capabilities to design and deploy large-scale radar radiation source recognition algorithms. Initially, it addresses the issues related to large-scale radar radiation source samples through data analysis, preprocessing, and feature selection, extracting and forming prior knowledge information. Subsequently, a model named RIR-DA (Radar ID Recognition based on Deep Learning Autoencoder) is developed, integrating this prior knowledge. The RIR-DA model successfully identified 96 radar radiation source targets with an accuracy exceeding 95% in a dataset characterized by a highly imbalanced sample distribution. To tackle the challenges of poor migration effects and low computational efficiency on lightweight edge computing platforms, a parallel acceleration scheme based on the embedded microprocessor T4240 is designed. This approach achieved a nearly eightfold increase in computational speed while maintaining the original training performance. Furthermore, an integrated solution for a radar radiation source intelligent detection system combining PC devices and edge devices is preliminarily designed. Experimental results demonstrate that, compared to existing radar radiation source target recognition algorithms, the proposed method offers superior model performance and greater practical extensibility. This research provides an innovative exploratory solution for the industrial application of deep learning models in radar radiation source recognition.

Particle identification with machine learning from incomplete data in the ALICE experiment

The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors for particles with momentum from about 100 MeV/c up to 20 GeV/c. Traditionally, particles are selected with rectangular cuts. A much better performance can be achieved with machine learning (ML) methods. Our solution uses multiple neural networks (NN) serving as binary classifiers. Moreover, we extended our particle classifier with Feature Set Embedding and attention in order to train on data with incomplete samples. We also present the integration of the ML project with the ALICE analysis software, and we discuss domain adaptation, the ML technique needed to transfer the knowledge between simulated and real experimental data.

Forgiveness and Emotional Well-Being: A Study of Parents of Children with Autism Spectrum Disorders

This study investigates the relationship between forgiveness and subjective well-being among parents of children with autism spectrum disorders (ASD). Using a quantitative correlational approach, the research aims to determine whether forgiveness significantly impacts the subjective well-being of these parents. The study involved 43 participants from SLBN 1 Palangka Raya, who completed questionnaires measuring forgiveness (Heartland Forgiveness Scale) and subjective well-being (SAPAN and SWLS scales). Descriptive statistics revealed high levels of forgiveness and subjective well-being among participants. However, Pearson correlation analysis indicated no significant relationship between the two variables. This suggests that while parents exhibit high forgiveness, it does not necessarily translate to increased life satisfaction or emotional balance. Subjective well-being is influenced by multiple factors, including gratitude, personality, self-esteem, spirituality, and social support. The study highlights the need for further research to explore these additional factors and address limitations such as incomplete samples and participant confusion during data collection. These insights are crucial for supporting the psychological well-being of parents with children who have special needs.

Cross-modal semantic aligning and neighbor-aware completing for robust text–image person retrieval

Most existing text–image person re-identification (TIReID) methods are performed in an ideal environment where both image and text instances are fully intact and identity annotated. However, in real-world open environments, these ideal assumptions often cannot be satisfied. In this study, we are the first to explore how to enhance the robustness of the TIReID model to better adapt to open environments, focusing on two aspects: 1) unlabeled multi-modal data, and 2) incomplete multi-modal data. Here, unlabeled multi-modal data faces two key issues: intra-class variations and cross-modal aligning problems, while incomplete data leads to data semantic inconsistency problems and severe performance degradation. In order to tackle the above issues, we propose a novel Cross-modal Semantic Aligning and Neighbor-aware Completing (CANC) method for robust text–image person re-identification (RTIReID). Specifically, to tackle the intra-class variation problem, we propose intra-view prototype contrastive matching for image and text modalities, which helps the model learn intra-modal discriminative representations while increasing inter-class distance and suppressing intra-class variation. Furthermore, to resolve the cross-modal alignment problem, we propose the cross-view instance projection matching to establish the relationship between different modal representations. Additionally, regarding how to effectively handle incomplete samples, we introduce nearest neighbor consistent completion, which can restore high-quality completion features. We adopt CLIP-ViT and CLIP-Xformer as image and text encoders respectively, and Rank-k metrics (k=1,5,10) as primary evaluation metrics. Extensive experiments on several public datasets demonstrate that our method can effectively address TIReID in open environments, consistently surpassing several SOTA TIReID methods.

Missing Data Imputation with Uncertainty-Driven Network

We study the problem of missing data imputation, which is a fundamental task in the area of data quality that aims to impute the missing data to achieve the completeness of datasets. Though the recent distribution-modeling-based techniques (e.g., distribution generation and distribution matching) can achieve state-of-the-art performance in terms of imputation accuracy, we notice that (1) they deploy a sophisticated deep learning model that tends to be overfitting for missing data imputation; (2) they directly rely on a global data distribution while overlooking the local information. Driven by the inherent variability in both missing data and missing mechanisms, in this paper, we explore the uncertain nature of this task and aim to address the limitations of existing works by proposing an u&lt;u&gt;N&lt;/u&gt;certainty-driven netw&lt;u&gt;O&lt;/u&gt;rk for &lt;u&gt;M&lt;/u&gt;issing data &lt;u&gt;I&lt;/u&gt;mputation, termed NOMI. NOMI has three key components, i.e., the retrieval module, the neural network gaussian process imputator (NNGPI) and the uncertainty-based calibration module. NOMI~ runs these components sequentially and in an iterative manner to achieve a better imputation performance. Specifically, in the retrieval module, NOMI~ retrieves local neighbors of the incomplete data samples based on the pre-defined similarity metric. Subsequently, we design NNGPI~ that merges the advantages of both the Gaussian Process and the universal approximation capacity of neural networks. NNGPI~ models the uncertainty by learning the posterior distribution over the data to impute missing values while alleviating the overfitting issue. Moreover, we further propose an uncertainty-based calibration module that utilizes the uncertainty of the imputator on its prediction to help the retrieval module obtain more reliable local information, thereby further enhancing the imputation performance. We also demonstrate that our NOMI~ can be reformulated as an instance of the well-known Expectation Maximization (EM) algorithm, highlighting the strong theoretical foundation of our proposed methods. Extensive experiments are conducted over 12 real-world datasets. The results demonstrate the excellent performance of NOMI in terms of both accuracy and efficiency.

Monitoring the inverted exponentiated half logistic quantiles under the adaptive progressive type II hybrid censoring scheme

Numerous studies have solved the problem of monitoring statistical processes with complete samples. However, censored or incomplete samples are commonly encountered due to constraints such as time and cost. Adaptive progressive Type II hybrid censoring is a novel method with the advantages of saving time and improving efficiency. On the basis of this scheme, the problem of monitoring a downward shift in the quantiles of the inverted exponentiated half logistic distribution is considered. The conventional Shewhart control chart is insensitive to detect quantile shifts from faulty data processes that exhibit deviations from normality or symmetry. To overcome this limitation, Bootstrap control charts combining with the exponentially weighted moving average method based on the Bayesian estimation and maximum likelihood estimation are proposed, respectively. They are compared with the conventional Shewhart control chart via average run length through Monte-Carlo simulations. Finally, a real dataset related to the tensile strength of carbon fibers is employed to demonstrate the ascendancy of the Bootstrap control charts.

January Effect And Size Effect Market Anomalies On Banking Companies In Indonesia Stock Exchange

In the capital market, each stock price must reflect all available and relevant information or commonly known as the efficient market. In an efficient market, market anomalies should not occur to affect abnormal returns. This study aims to test whether the Indonesia Stock Exchange has January effect and Size Effect anomalies that can affect the capital market to be inefficient. The population of this study were all banking stocks listed on the IDX in the 2018–2022 periods. From this population, incomplete historical data samples will be excluded from the calculation. Therefore, the sample that meets the criteria is 38 banks. The research is divided into two parts: the first to examine the January effect and the second to exam- ine the Size Effect. In the first study, researchers tested whether there was a significant difference between abnormal returns in January and other months. Statistical tests use the Wilcoxon Signed Rank Test. In the second study, researchers tested whether there was a significant difference between the Average Cumulative Abnormal Return (ACAR) of large market capi- talization stocks and the Average Cumulative Abnormal Return (ACAR) of medium and small market capitalization stocks. Statistical tests use the Paired Samples T-test. The conclusion of this study is that there is no effect of the January effect for the 2018–2022 period except for 2021 period and no effect of the Size Effect anomalies on banking stocks listed on the IDX for the 2018–2022 period.

Age Readings and Assessment in Coastal Batoid Elasmobranchs from Small-Scale Size-Selective Fishery: The Importance of Data Comparability in Multi-Specific Assemblages

The large variation in vertebral shape and calcification observed among elasmobranch species prevents using a single method for enhancing growth bands and reading age. Further, estimating age and growth parameters can be difficult or impractical when samples are incomplete due to the bycatch of a size-selective fishery. Using a single and rapid method, age readings were obtained on the vertebrae of four batoid species, namely 53 individuals of Dasyatis pastinaca, 51 of Raja asterias, 15 of Torpedo marmorata, and 55 specimens of Torpedo torpedo, from the local small-scale trammel net fishery in the coastal waters (5–20 m depth) of the Central Tyrrhenian Sea during 2019–2021. Based on these data, a statistical routine was developed to obtain multiple estimates of age and growth parameters for incomplete samples due to size-selective fishing. The acceptable agreement between and within readers (intra and inter-reader disagreement &lt; 5%) and the rate of increase in vertebral size with body size (differently ranked across species) demonstrated the consistency of the enhancing method. The parameters estimated by the Von Bertalanffy and Gompertz growth models matched the data available in the Mediterranean Sea for the species studied, with D. pastinaca, T. torpedo, and R. asterias showing the lowest (k = 0.05–0.12), intermediate (k = 0.112–0.19), and highest (k = 0.18–0.23) growth rates, respectively, in line with the life history traits of these species. Overall, the method proved effective both in delineating band pairs in vertebrae of different species and in reliably estimating the age and growth parameters of problematic samples due to size-selective fishing. The proposed method supports the collection of comparable demographic data from other areas where similar multi-specific assemblages are bycatch of size-selective fisheries impacting potential nursery areas and other essential habitats for elasmobranchs.