Error Measures Research Articles

Validity and Reliability of the Spanish Version of the Athlete Psychological Strain Questionnaire.

This study aims to provide data on the reliability and validity of an adapted Spanish version of the Athlete Psychological Strain Questionnaire (APSQ) when administered to Spanish athletes. A cross-sectional study was developed. Data were collected from 128 athletes (males n = 71; females n = 57). The tools used in this study were the APSQ; Kessler Psychological Distress Scale; and Depression, Anxiety, and Stress Scales. The intraclass correlation coefficient was used to determine relative reliability, whereas standard error measurement and minimal detectable change were used to evaluate absolute reliability. Bland-Altman plots were employed to assess test-retest agreement and potential systematic bias. Data indicate that the Spanish version of the APSQ demonstrated satisfactory internal consistency (α: .77-.85) and generally fair to good test-retest reliability (intraclass correlation coefficient: .702; 95% CI, .602-.780). Significant associations were found between the APSQ Spanish version and the Depression, Anxiety, and Stress Scales (ρ = .64-.82), with fair to good agreement, whereas fair to moderate levels of agreement were established between the APSQ Spanish version and the Kessler Psychological Distress Scale (ρ = .53-.75). These results provide preliminary evidence supporting the use of the APSQ Spanish version for mental health screening among Spanish athletes. However, the lack of absolute reliability raises concerns about its utility for assessing the effects of interventions aimed at improving mental well-being.

Micro-Assembly Error Control of Specialized MEMS Friction Sensor

A skin friction sensor is a three-dimensional MEMS sensor specially designed for measuring the skin friction of hypersonic vehicle models. The accuracy of skin friction measurement under hypersonic laminar flow conditions is closely related to the fabrication and micro-assembly accuracy of MEMS skin friction sensors. In order to achieve accurate skin friction measurement, high-precision linear laser scanning ranging, multi-axis precision drive, and 3D reconstruction algorithms are investigated; a MEMS skin friction sensor micro-assembly height error measurement system is developed; and the MEMS skin friction sensor micro-assembly height error control method is carried out. The results show that the micro-assembly height error measurement of MEMS skin friction sensors achieves an accuracy of up to 2 μm. The height errors of the MEMS skin friction sensor were controlled within −8 μm to +10 μm after error control. The angular errors were controlled within the range of 0.05–0.25°, significantly improving micro-assembly accuracy in the height direction of the MEMS skin friction sensor. The results of hypersonic wind tunnel tests indicate that the deviation in the accuracy of the MEMS skin friction sensors after applying height error control is about 5%, and the deviation from the theoretical value is 8.51%, which indicates that height error control lays the foundation for improving the accuracy of skin friction measurement under hypersonic conditions.

Quantifying Tinnitus Perception Improvement: Deriving the Minimal Clinically Important Difference of the Minimum Masking Level.

Tools that can reliably measure changes in the perception of tinnitus following interventions are lacking. The minimum masking level, defined as the lowest level at which tinnitus is completely masked, is a candidate for quantifying changes in tinnitus perception. In this study, we aimed to determine minimal clinically important differences for minimum masking level. A 3-month tinnitus intervention combining counseling and sound therapy was conducted in 74 participants with chronic tinnitus. Minimum masking levels were measured at baseline and 3 months. The clinical global impression was evaluated at 3 months to measure changes in participants' self-perception of tinnitus. The minimal clinically important difference of the minimum masking level was calculated using anchored-based, effect size, standard error measurement, and receiver operating characteristic curve analysis. The minimal clinically important difference analysis of the minimum masking level yielded a -5.5 dB SL from the receiver operating characteristic curve, a -8.1 dB SL from the standard error measurement, a -9.2 dB SL from the effect size, and a -10.3 dB SL from the anchor-based analysis. Of these, the minimal clinically important difference value with optimized sensitivity (.704) and specificity (.957) was a -5.5 dB SL, determined using receiver operating characteristic analysis. The proposed minimal clinically important difference value of the minimum masking level (-5.5 dB SL) provides a good level of sensitivity and specificity. Therefore, the minimum masking level may be an alternative for measuring changes in tinnitus perception. https://doi.org/10.23641/asha.28156229.

Acquiring pulse a priori information for chirp transform spectrometer systems through phase error measurements

Abstract With the rapid advancement in deep-space exploration, back-end systems such as Chirp Transform Spectrometers (CTS) must be equipped with larger bandwidths, higher frequency resolutions, and enhanced sensitivities. Phase errors in chirp signals distort pulse compression waveforms, significantly affecting CTS system performance. To correct the distorted pulses, we acquire a priori information about the pulse waveform by measuring the phase errors in the CTS system. This paper measures and analyzes phase errors in the expander and compressor signals of a 1GHz bandwidth CTS system. We discuss the impact of these phase errors on pulse waveforms, system frequency resolution, sensitivity, and other critical performance parameters. Additionally, we plot the in-band phase error curves for the system. By utilizing these phase error curves, we can determine the phase error for any input frequency, thereby providing valuable prior information for pulse compression waveform correction. This approach is crucial for guiding subsequent error optimization and compensation in the CTS system.

Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature

Precise river water temperature (WT) forecasts are essential for monitoring water quality. This study addresses the limited use of signal decomposition in hybrid WT prediction models by proposing three methods: namely ensemble empirical mode decomposition (EEMD) on AdaBoost, long short-term memory (LSTM), and gated recurrent unit (GRU). These models integrate ensemble empirical mode decomposition (EEMD) with machine learning techniques for forecasting WT across multiple time horizons (one, three, and five days). The performance of implemented models were tested on two river stations located on the Clackamas River (USGS 14211010) and Willamette River (USGS 14211720). Some error measures comprising root mean square (RMSE), mean absolute error (MAE), coefficient of determination (R2), uncertainty coefficient in 95% confidence level (U95%), and mean absolute percentage error (MAPE) were applied in assessing the models’ performances. The performance of the proposed merged methods, including EEMD-AdaBoost, EEMD-LSTM, and EEMD-GRU, were compared with their simple forms. The outcomes illustrated that the hybrid models performed better than the relevant individual methods; however, the river WT forecasts of EEMD-LSTM and EEMD-GRU were found to be much closer to the observed data than those of the EEMD-AdaBoost method. The better accuracy of hybrid models compared to their corresponding simple ones can be explained by considering the potential of EEMD in separating intrinsic patterns and reducing the noises, leading to reliable forecasts of river WT time series. A performance comparison of the simple models also denoted the superiority of LSTM and GRU over the AdaBoost. The superior river WT forecasts at both stations during the testing stage were concluded for one day ahead at EEMD-GRU model (USGS 14211010: RMSE = 0.1929 ℃, MAE = 0.1489 ℃, R2 = 0.9988, U95% = 0.3745, MAPE = 1.3608%; USGS 14211720: RMSE = 0.1918 ℃, MAE = 0.1558 ℃, R2 = 0.9990, U95% = 0.3690, MAPE = 1.1790%).

Random Sampling Versus Active Learning Algorithms for Machine Learning Potentials of Quantum Liquid Water.

Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of the system of interest. As the construction of this data is computationally demanding, many schemes for identifying the most important structures have been proposed. Here, we compare the performance of high-dimensional neural network potentials (HDNNPs) for quantum liquid water at ambient conditions trained to data sets constructed using random sampling as well as various flavors of active learning based on query by committee. Contrary to the common understanding of active learning, we find that for a given data set size, random sampling leads to smaller test errors for structures not included in the training process. In our analysis, we show that this can be related to small energy offsets caused by a bias in structures added in active learning, which can be overcome by using instead energy correlations as an error measure that is invariant to such shifts. Still, all HDNNPs yield very similar and accurate structural properties of quantum liquid water, which demonstrates the robustness of the training procedure with respect to the training set construction algorithm even when trained to as few as 200 structures. However, we find that for active learning based on preliminary potentials, a reasonable initial data set is important to avoid an unnecessary extension of the covered configuration space to less relevant regions.

State-of-art, challenges, and outlook on deep hole boring: chatter suppression, tool wear monitoring, and error measurement

State-of-art, challenges, and outlook on deep hole boring: chatter suppression, tool wear monitoring, and error measurement

A Heuristic Strategy Assisted Deep Learning Models for Brain Tumor Classification and Abnormality Segmentation

ABSTRACTBrain tumors are prevalent forms of malignant neoplasms that, depending on their type, location, and grade, can significantly reduce life expectancy due to their invasive nature and potential for rapid progression. Accordingly, brain tumors classification is an essential step that allows doctors to perform appropriate treatment. Many studies have been done in the sector of medical image processing by employing computational methods to effectively segment and classify tumors. However, the larger amount of information collected by healthcare images prohibits the manual segmentation process in a reasonable time frame, reducing error measures in healthcare settings. Therefore, automated and efficient techniques for segmentation are crucial. In addition, various visual information, noisy images, occlusion, uneven image textures, confused objects, and other features may impact the process. Therefore, the implementation of deep learning provides remarkable results in medicinal image processing, particularly in the segmentation and classification process. However, conventional deep learning‐assisted methods struggle with complex structures and dimensional issues. Thus, this paper develops an effective technique for diagnosing brain tumors. The main aspect of the proposed system is to classify the brain tumor types by segmenting the affected regions of the raw images. This novel approach can be applied for various applications like diagnostic centers, decision‐making tools, clinical trials, medical research institutes, disease prognosis, and so on. Initially, the requisite images are collected from standard datasets and further, it is subjected to the segmentation period. In this stage, the Multi‐scale and Dilated TransUNet++ (MDTUNet++) model is employed to segment the abnormalities. Further, the segmented images are given into an Adaptive Dilated Dense Residual Attention Network (ADDRAN) to classify the brain tumor types. Here, to optimize the ADDRAN technique's parameters, an Improved Hermit Crab Optimizer (IHCO) is supported, which increases the accuracy rates of the overall network. Finally, the numerical examination is conducted to guarantee the robustness and usefulness of the designed model by contrasting it with other related techniques. For Dataset 1, the accuracy value attains 93.71 for the proposed work compared to 87.86 for CNN, 90.18 for DenseNet, and 89.56 and 90.96 for RAN and DRAN, respectively. Thus, supremacy has been achieved for the recommended system while detecting the brain tumor types.

Medication errors in pediatric intensive care unit and measures to improve it.

Objective: To ascertain the prevalence of medication mistakes among pediatric patients who are hospitalized to the critical care unit at Fatima Memorial Hospital in Lahore. Study Design: Cross-sectional study. Setting: Pediatric Intensive Care Unit, Fatima Memorial Hospital, Lahore. Period: December 2022 to December 2023. Methods: 131 health care workers and patients were interviewed and medication errors were categorized accordingly. Medication error was labeled as errors arising in prescribing, administration, and transcribing of medicines by the healthcare staff to the patients admitted to PICU. Data was entered and analyzed in SPSS version 25. Results: In this study, the average age of health care workers was 34.26 ± 9.07 years. Out of 131 candidates, 71 (54.2%) were males and 60 (45.8%) were females. Among them, 36 (27.5%) were doctors, 45 (34.4%) were nursing staff and 50 (38.2%) were technicians. The total work experience was 9.60 ± 5.96 years. The average age of children admitted in ICU was 7.31 ± 3.68 years. Out of 131 candidates, 64 (48.9%) were males and 67 (51.1%) were females. Out of 131 patients, medication error was noted in 27 (20.6%) cases. The most common type of medication error was prescription error [12 (9.2%) cases], followed by administration error [8 (6.1%)] and transcription error [7 (5.3%)]. Conclusion: The occurrence of medication error is although less in local setting, but not negligible. So further trials should be done and interventions should be made to lessen don the medication errors.

Optimization of Covariance Matrices of Kalman Filter with Unknown Input Using Modified Directional Bat Algorithm

The proper selection of the model error covariance matrix and the measurement noise covariance matrix of Kalman filter is an optimization problem. Some scholars have studied this, but there is relatively little research on the selection of the two covariance matrices for Kalman filters with an unknown input. Recently, the authors proposed a modified directed bat algorithm (MDBA) which introduces the best historical location of individuals and the elimination strategy to effectively prevent falling into local optimal solution. So, two methods are proposed in this paper to optimize the model error covariance matrix and measurement noise covariance matrix of Kalman filter with unknown inputs (KF-UI) and extended Kalman filter with unknown inputs (EKF-UI) by MDBA, respectively. The objective functions are constructed using the measurement vectors and the corresponding estimated values, and MDBA is adopted to optimize the two covariance matrices of KF-UI and EKF-UI. To validate the effectiveness of proposed methods, two simple structure examples and a benchmark example are adopted. The influence of structural parameter uncertainties on KF-UI is also considered. The result shows that the MDBA-optimized KF-UI has a strong convergence and can take into account the effect of parameter uncertainties. Then, the effectiveness of the proposed MDBA-optimized EKF-UI method is validated by comparing it with EKF-UI with empirically selected covariance values through trial-and-error. The identification results showed that the proposed methods achieved better identification accuracy and enhanced convergence compared to KF-UI and EKF-UI with empirical covariance values.

Research on End-Effector Position Error Compensation of Industrial Robotic Arm Based on ECOA-BP.

Industrial robotic arms are often subject to significant end-effector pose deviations from the target position due to the combined effects of nonlinear deformations such as link flexibility, joint compliance, and end-effector load. To address this issue, a study was conducted on the analysis and compensation of end-position errors in a six-degree-of-freedom robotic arm. The kinematic model of the robotic arm was established using the Denavit-Hartenberg (DH) parameter method, and a rigid-flexible coupled virtual prototype model was developed using ANSYS and ADAMS. Kinematic simulations were performed on the virtual prototype to analyze the variation in end-effector position errors under rigid-flexible coupling conditions. To achieve error compensation, an approach based on an Enhanced Crayfish Optimization Algorithm (ECOA) optimizing a BP neural network was proposed to compensate for position errors. An experimental platform was constructed for error measurement and validation. The experimental results demonstrated that the positioning accuracy after compensation improves by 75.77%, fully validating the effectiveness and reliability of the proposed method for compensating flexible errors.

Increasing the location accuracy of the operating unit of a three-axis metal-cutting machine tool: application of the differential geometric model of volumetric errors

The article is devoted to increasing the volumetric accuracy of multi-axis machine tools. Based on the model of measurement and calculation of the volumetric geometric error of machine tools presented by the authors, a method has been developed which allows to signifi cantly improve the accuracy of the movement of the operating unit of a three-axis metal-cutting machine tool to a given point. The astatic law and the calculation of the coordinate correction according to the differential geometric model of the volumetric error are used to control the movement of the operating unit. The developed method is implemented programmatically and allows to perform the correction in the control program represented in G-code. As a result of experiments on software correction of volumetric errors according to the developed method, conducted at the State Engineering Centre of the Moscow State University of Technology “STANKIN” on a threeaxis CNC machine tool, a signifi cant reduction (up to 90 %) of geometric volumetric errors was shown. The developed method can be used to improve the accuracy of metal-cutting machine tools by creating postprocessors that calculate corrections to the coordinates set in the control system based on the results of interferometric measurements.Keywords: volumetric accuracy, interference measurements, three-axis machine tools, machine tool accuracy, geometric errors, astatic control law, mathematical modelling, differential geometry

Diagnosing Helicobacter pylori using autoencoders and limited annotations through anomalous staining patterns in IHC whole slide images.

This work addresses the detection of Helicobacter pylori (H. pylori) in histological images with immunohistochemical staining. This analysis is a time-demanding task, currently done by an expert pathologist that visually inspects the samples. Given the effort required to localize the pathogen in images, a limited number of annotations might be available in an initial setting. Our goal is to design an approach that, using a limited set of annotations, is capable of obtaining results good enough to be used as a support tool. We propose to use autoencoders to learn the latent patterns of healthy patches and formulate a specific measure of the reconstruction error of the image in HSV space. ROC analysis is used to set the optimal threshold of this measure and the percentage of positive patches in a sample that determines the presence of H. pylori. Our method has been tested on an own database of 245 whole slide images (WSI) having 117 cases without H. pylori and different density of the bacteria in the remaining ones. The database has 1211 annotated patches, with only 163 positive patches. This dataset of positive annotations was used to train a baseline thresholding and an SVM using the features of a pre-trained RedNet-18 and ViT models. A 10-fold cross-validation shows that our method has better performance with 91% accuracy, 86% sensitivity, 96% specificity and 0.97 AUC in the diagnosis of H. pylori . Unlike classification approaches, our shallow autoencoder with threshold adaptation for the detection of anomalous staining is able to achieve competitive results with a limited set of annotated data. This initial approach is good enough to be used as a guide for fast annotation of infected patches.

Translation, Validity, and Reliability of the Indonesian Version of the Shoulder Pain and Disability Index (SPADI).

The Shoulder Pain and Disability Index (SPADI) is a widely used 13-item shoulder-specific patient-reported outcome measure for shoulder pain disorders. The English version of SPADI is easy to use and demonstrates excellent measurement properties for clinical and research settings. To translate and culturally adapt an Indonesian version of SPADI (SPADI-IDN) and then validate its use in Indonesian patients. Cohort study (Diagnosis); Level of evidence, 2. Through a forward and a backward translation process, the validity of the questionnaire was investigated. The study population was 100 patients with shoulder pain who were treated in a hospital in Indonesia. The patients were asked to fill out the SPADI-IDN during their visit. To evaluate the validity of SPADI-IDN, the patients were also asked to fill out the Medical Outcomes Study 12-Item Short-Form Health Survey and the Oxford Shoulder Score. To assess the test-retest reliability, the same patients were asked to fill out the SPADI-IDN questionnaire again 1 week later. The assessment of construct validity, test-retest reliability, internal consistency, floor and ceiling effect, and measurement of error followed the COSMIN (Consensus-based Standards for the selection of health Measurement Instruments) guidelines. The Bland-Altman method was used to explore absolute agreement. Of the 100 eligible patients, all were used to assess construct validity, and 87 patients (87%) were used to assess test-retest reliability. Almost every established hypothesis about the correlations between SPADI-IDN and other questionnaires could be confirmed, implying good construct validity. No floor or ceiling effects were found. The intraclass correlation value was 0.99, indicating strong test-retest reliability. A Cronbach α was 0.95, indicating a good internal consistency. The Bland-Altman analysis did not reveal any bias. The standard error of measurement and the minimal detectable change at the individual and group levels were 2.65, 7.3, and 0.7, respectively. The SPADI-IDN represents a valid and reliable tool for measuring pain and disability in patients with shoulder pain disorders.

A modified method for measuring geometric errors of rotary axes based on sensitivity analysis and error simulation

Complicated measurement for machine tools geometric errors limits the high-accuracy machining processes. This research is aimed at measuring and identifying geometric errors of rotary axes efficiently by designing and optimizing measurement method using double ballbar. First, the conical and tangential measurement patterns are designed to reduce the installation times of the double ballbar. Secondly, the effect of installation errors is reduced by analyzing the coefficient matrix to avoid error accumulation. Thirdly, measurement trajectories affected by position-independent geometric errors are simulated and eliminated. Finally, the error measurement and validation experiments were performed on a dual-turntable five-axis machine tool, and the machining precision was improved by 34.09%-48.89%. The results indicated that the scheme can effectively identify the geometric errors of rotary axes and has the advantages of high efficiency and accuracy.

3D face reconstruction for maxillofacial surgery based on morphable models and neural networks: A preliminary assessment for anthropometry accuracy.

This study aimed to evaluate the anthropometric accuracy of 3D face reconstruction based on neural networks (3DFRBN) using 2D images, including the assessment of global errors and landmarks, as well as linear and angular measurements. Thirty healthy volunteers were recruited in this study. For each volunteer, five standard photos were taken, capturing anterior, 45° to left and right, and 90° to left and right views. These 2D images were then imported into the 3DFRBN program for generating 3D facial models. Accuracy of landmarks on these models was analyzed in comparison with 3dMD-captured models, as well as global error, and linear and angular measurements. The average root mean square error of the 3DFRBN-generated models was 1.52±0.23mm. Euclidean distance for landmark deviation on 3DFRBN-generated models was 1.87±0.13mm, ranging from 1.21mm to 2.17mm. 13 out of 16 measurements had ICCs over 0.90. The average errors for linear and angular measurements were 0.67mm and 3.25°, respectively. This study introduced a novel, convenient, and accurate approach for both prospective and retrospective 3D facial data acquirement. Its great potential for retrospective 3D facial data acquisition can greatly benefit trauma and oncological reconstruction, as well as retrospective studies.

Blind spots in brain imaging: a pictorial essay.

Currently, radiologists must interpret large quantities of images and identify diseases on a daily basis. The minimization of errors is crucial for high-quality diagnostic imaging and optimal patient care. Brain imaging is frequently used in clinical practice; however, radiologists are prone to overlook some regions in brain imaging and make perceptual errors, thus leading to missed diagnoses. These regions, also known as "blind spots", comprise a number of intricate areas, including the posterior fossa, cerebral sulci and pia mater, cranial nerves (CNs), intracranial arteries, dural sinuses, sella and parasellar region, Meckel's cave, skull base, scalp, orbit, and pterygopalatine fossa (PPF). Therefore, the knowledge of normal computed tomography (CT) and magnetic resonance imaging (MRI) manifestations and common lesions in these blind spots is imperative to avoid false-negative results. This article graphically discusses and analyzes these common blind spots of brain imaging using real representative cases. It also provides comprehensive strategies to address missed diagnostic errors in radiology, including enhancing the selection of imaging protocols, implementing a multi-reviewer reporting system, adopting structured reporting templates, employing error measurement or detection strategies, and promoting the use, development, and refinement of artificial intelligence (AI) to improve diagnostic accuracy and efficiency. This article may also increase junior doctors' awareness of these blind spots and assist them in their daily work, and thus has continuing education implications.

Cognitive factors as mediators of the relationship between childhood trauma and depression symptoms: the mediating roles of cognitive overgeneralisation, rumination, and social problem-solving

ABSTRACT Background: Childhood trauma has negative immediate and long-term impacts on depression. Questions remain, however, regarding the cognitive factors influencing this relationship. This study aimed to investigate the role of three cognitive factors – cognitive overgeneralisation, rumination and social problem-solving – as mediating factors in the relationship between childhood trauma and symptoms of depression. Methods: We conducted a cross-sectional study in Iran from March to July 2023. Participants (N = 227; Mean age 32.44 ± 8.95 years) with depression completed measures of childhood trauma, depression, self-overgeneralisation, cognitive errors, memory specificity, rumination and social problem-solving. The conceptual model was assessed using structural equation modelling. Results: Structural equation modelling indicated that childhood trauma had a positive direct effect on depression symptoms. Childhood trauma had a positive indirect effect on depression symptoms through both self-overgeneralisation and rumination and a negative indirect effect on depression through effective social problem-solving strategies. Conclusions: The findings suggest increased exposure to childhood trauma may be associated with elevated depression and self-overgeneralisation, rumination, and effective social problem-solving strategies may play an important role in this relationship. These findings hold potential implications for those working with patients with depression and a history of childhood trauma.

A Parameter-Oriented FFT Signal Processing App

In power systems, some components such as harmonics / interharmonics and noise arise in electrical signals due to varying sources and loads, resulting in changes in the purity of the electrical signal. Therefore, continuous monitoring and accurate analysis of electrical signals become mandatory. One of the common methods in this field is the Fast Fourier Transform (FFT), which subjects electrical signals to continuous analysis through sliding windows at the intervals formed by the standard of IEC-61000-4-7. From this analysis, various parameters are derived and compared with the threshold values specified in other standard, the IEEE-1159. However, due to variational field conditions, as well as factors such as frequencies of sampling, main, and additional components, measurement window, and Signal-to-Noise Ratio (SNR), various measurement errors, occur. These difficulties further complicate the already difficult task of accurate measurement in variable conditions and can lead to errors in preventive measures and control procedures. It is therefore of great importance to gain an understanding of the effects of these parameters and to implement improvements to the methods accordingly. This study emphasizes the significance of parameter selection for FFT and presents an investigation of FFT responses in terms of different parameters. Furthermore, it presents measurement errors according to changes in the signal and presents a basic interface design showing these errors. It was found that even small changes, such as a 1/2000 change in the sampling frequency, a 0.5 Hz shift in the fundamental frequency, or a 1/1000 difference in the measurement window, resulted in significant errors. These findings highlight the necessity of judicious parameter selection for accurate computation and signal monitoring and demonstrate that improvements in the FFT method are needed to adapt to changing conditions.

Engineer CEOs and corporate investment efficiency

Purpose This paper aims to examine whether CEOs with an engineering background increase corporate investment efficiency (CIE). The authors further investigate the role of engineering directors on boards of the above association. Design/methodology/approach Drawing from upper-echelon theory, which suggests that corporate outcomes are a reflection of its top management characteristics, the authors hypothesise a positive association between engineer CEOs and CIE and a positive moderation role of the proportion of engineer directors on boards in the above association. The authors examine this link using a sample of Australian Securities Exchange 200 firms from 2015 to 2022. Engineer CEO data is hand-collected from corporate annual report biographies and investment efficiency is a measure that captures whether the investments are maintained at optimal levels relative to industry-year benchmarks, following the approaches of Biddle et al. (2009), Chen et al. (2011) and the average values of both models. Findings The results indicate support for the hypotheses, revealing that firms managed by engineer CEOs have higher investment efficiency than their counterpart firms. This association is exacerbated in the presence of a higher proportion of engineer directors on boards. The results are robust to year and industry-fixed effects, propensity score matching, alternative measures of investment efficiency and robust standard errors. Our results also remain valid for an industry sub-sample using certain industries in which engineering expertise maybe more desirable (e.g. metals and mining). Research limitations/implications By showing that engineer CEOs are significantly associated with CIE, the authors contribute to upper-echelon literature examining the link between CEO characteristics and corporate outcomes, particularly, investment decision efficiency. The influence of engineering background on corporate outcomes is less examined in the literature; thus, the authors contribute to this thin literature. Originality/value The findings are informative to potential investors in evaluating firms’ investment efficiency before investing in firms. For example, firms with engineer CEOs are likely to maintain efficient investment levels in future years.