Least-squares Algorithm Research Articles

In-situ high-accuracy figure measurement based on stereo deflectometry for the off-axis aspheric mirror.

The misalignment between the geometric and optical axes, combined with rotational asymmetry, poses significant challenges for achieving high-accuracy measurement of the off-axis aspheric mirror during the fabrication and polishing processes. To address this issue, this paper presents a method based on stereo deflectometry for measuring the figure of the off-axis aspheric mirror. In this method, point cloud of the off-axis aspheric mirror is first obtained using stereo deflectometry. Subsequently, the point cloud is transformed into the parent mirror coordinate system using the final-optimized transformation matrix via the nonlinear least-squares algorithm, and the figure of the off-axis aspheric is determined by subtracting the conic formula. To verify the feasibility and high accuracy of the proposed method, measurements are conducted in both simulations and experiments on an off-axis parabolic mirror with a diameter of 142 mm. The simulation results indicate that the difference between the calculated results of the proposed method and the ground truth is 3.01 nm PV (peak-to-vally), and the experimental results are consistent with those obtained using the interferometer, demonstrating the feasibility and high accuracy of the proposed method.

Efficient evaluation of the Jacobian in the damped least-squares method for optical design problems using algorithmic differentiation.

The fast evaluation of the Jacobian is an essential part of the optimization of optical systems using the damped least-squares algorithm. While finite differences provide an intuitive way to approximate derivatives, algorithmic differentiation is a technique to evaluate them exactly. However, applying algorithmic differentiation to a ray tracing routine for optical systems with many parameters is computationally expensive, where the main costs are caused by the determination of the ray-surface intersection. To overcome this disadvantage, we present a mathematical analysis of the ray-surface intersection and its efficient differentiation in both forward and reverse mode algorithmic differentiation. Futhermore the structure of the optimization variables and operands is exploited to derive a method that allows computation of the Jacobian in the same order of computational complexity as the primal ray trace. The method is successfully tested for a rotationally symmetric lens system and a freeform design task.

Data fusion by T3-PCA: A global model for the simultaneous analysis of coupled three-way and two-way real-valued data.

In various areas of science, researchers try to gain insight into important processes by jointly analysing different datasets containing information regarding common aspects of these processes. For example, to explain individual differences in personality, researchers collect, for the same set of persons, data regarding behavioural signatures (i.e., the reaction profile of a person across different situations), on the one hand, and traits or dispositions, on the other hand. To uncover the processes underlying such coupled data, to all N-way -mode data blocks simultaneously a global model is fitted, in which each data block is represented by an -way -mode decomposition model (e.g., principal component analysis [PCA], Parafac, Tucker3) and the parameters underlying the common mode are required to be the same for all data blocks this mode belongs to. To estimate the parameters underlying the common mode, a simultaneous strategy is used that pools the information present in all data blocks (i.e., data fusion). In this paper, we propose the T3-PCA model, which represents three- and two-way data with Tucker3 and PCA respectively. This model is less restrictive than the already proposed LMPCA model in which the three-way data block is decomposed according to a Parafac model. To estimate the T3-PCA model parameters, an alternating least-squares algorithm is proposed. The superior performance of the simultaneous T3-PCA strategy over a sequential strategy (i.e., estimating common parameters using information from the three-way data block only) is demonstrated in an extensive simulation study and an application to empirical coupled anxiety data.

MIRD Pamphlet No. 31: MIRDcell V4-Artificial Intelligence Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy.

Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is unlikely to have the desired therapeutic effect because of nonuniform uptake by the targeted cells. Therefore, multiple radiopharmaceuticals targeting different receptors on a cell is warranted. However, past implementations invivo have not met with convincing results because of the absence of optimization strategies. Here we present artificial intelligence (AI) tools housed in a new version of our software platform, MIRDcell V4, that optimize a cocktail of radiopharmaceuticals by minimizing the total disintegrations needed to achieve a given surviving fraction (SF) of tumor cells. Methods: AI tools are developed within MIRDcell V4 using an optimizer based on the sequential least-squares programming algorithm. The algorithm determines the molar activities for each drug in the cocktail that minimize the total disintegrations required to achieve a specified SF. Tools are provided for populations of cells that do not cross-irradiate (e.g., circulating or disseminated tumor cells) and for multicellular clusters (e.g., micrometastases). The tools were tested using model data, flow cytometry data for suspensions of single cells labeled with fluorochrome-labeled antibodies, and 3-dimensional spatiotemporal kinetics in spheroids for fluorochrome-loaded liposomes. Results: Experimental binding distributions of 4 211At-antibodies were considered for treating suspensions of MDA-MB-231 human breast cancer cells. A 2-drug combination reduced the number of 211At decays required by a factor of 1.6 relative to the best single antibody. In another study, 2 radiopharmaceuticals radiolabeled with 195mPt were each distributed lognormally in a hypothetical multicellular cluster. Here, the 2-drug combination required 1.7-fold fewer decays than did either drug alone. Finally, 2 225Ac-labeled drugs that provide different radial distributions within a spheroid require about one half of the disintegrations required by the best single agent. Conclusion: The MIRDcell AI tools determine optimized drug combinations and corresponding molar activities needed to achieve a given SF. This approach could be used to analyze a sample of cells obtained from cell culture, animal, or patient to predict the best combination of drugs for maximum therapeutic effect with the least total disintegrations.

A Low-Jitter and Compact-Area Fractional-N Digital PLL With Fast Multi-Variable Calibration Using the Recursive Least-Squares Algorithm

A Low-Jitter and Compact-Area Fractional-N Digital PLL With Fast Multi-Variable Calibration Using the Recursive Least-Squares Algorithm

Prediction of building HVAC energy consumption based on least squares support vector machines

Air conditioning, as an essential appliance in daily life, has the function of ensuring comfortable room temperature, but it is also accompanied by a large amount of power consumption. Consequently, the study suggests an energy consumption prediction model based on improved genetic algorithm—least squares support vector machine—to accurately predict the energy consumption of building heating, ventilation, and air conditioning. This model uses the improved genetic algorithm for regularization parameter and kernel parameter optimization to prevent overfitting and underfitting issues. According to the testing results, the least squares support vector machine, an upgraded genetic algorithm, may accomplish convergence faster than other algorithms, taking only 0.2 milliseconds to finish. In addition, the average relative error of the improved genetic algorithm- least squares support vector machine did not exceed 0.6%. In the energy consumption prediction for the whole year of 2022, the average error of the improved genetic algorithm-least squares support vector machine was only 2.0 × 106 kWh, and the prediction accuracy could reach up to 97.2%. The above outcomes revealed that the energy consumption prediction model can accurately predict the air conditioning energy consumption, which provides a strong support for the control and optimization of the air conditioning system.

Research on an aeroengine bearing fault diagnosis method based on WMSST-CNN-BKA-LSSVM

Abstract To effectively extract the fault feature from small sample datasets and improve the accuracy of the model, an aeroengine bearing fault diagnosis method is proposed in this research based on wavelet multi-synchrosqueezed transform-convolutional neural network-black-winged kite algorithm-least squares support vector machine (WMSST-CNN-BKA-LSSVM). WMSST was used to perform modal decomposition on the collected fault data to obtain the time-frequency images. The time-frequency images were input into CNN for fault data feature extraction. The results of the CNN fully connected layer were used as input to the LSSVM, and the BKA was used to optimize the key parameters of the LSSVM to classify aeroengine bearing faults. The method was verified by aeroengine bearing test data from the Harbin Institute of Technology. The research showed that the accuracy is 99.86% when the faulty test samples are 70%, and the accuracy is 93.90% when the faulty samples are 30%. The accuracy showed the applicability of this method in small sample fault diagnosis. By comparing the method with a one-dimensional convolutional neural network (1D-CNN) and the convolutional neural network - long short-term memory (CNN-LSTM), it is found that the accuracy has been dramatically improved.

Low-rank quaternion tensor completion for color video inpainting via a novel factorization strategy

Recently, a quaternion tensor product named Qt-product was proposed, and then the singular value decomposition and the rank of a third-order quaternion tensor were given. From a more applicable perspective, we extend the Qt-product and propose a novel multiplication principle for third-order quaternion tensor named gQt-product. With the gQt-product, we introduce a brand-new singular value decomposition for third-order quaternion tensors named gQt-SVD and then define gQt-rank and multi-gQt-rank. We prove that the optimal low-rank approximation of a third-order quaternion tensor exists and some numerical experiments demonstrate the low-rankness of color videos. So, we apply the low-rank quaternion tensor completion to color video inpainting problems and present alternating least-square algorithms to solve the proposed low gQt-rank and multi-gQt-rank quaternion tensor completion models. The convergence analyses of the proposed algorithms are established and some numerical experiments on various color video datasets show the high recovery accuracy and computational efficiency of our methods.

An augmented complex-valued gradient-descent total least-squares algorithm for noncircular signals

In this paper, we propose a novel augmented complex-valued gradient-descent total least-squares (ACGDTLS) adaptive filter for processing noisy input and output noncircular complex-valued signals. First, a Rayleigh quotient cost function is formulated by incorporating augmented complex-valued statistics and the output-to-input-noise-ratio within the widely linear error-in-variable model, whereby the ACGDTLS is developed using the gradient-descent approach. Next, rigorous analysis is conducted to establish a conservative step-size bound guaranteeing mean convergence, a closed-form expression for the steady-state mean-squared deviation, and the algorithm’s computational complexity. Finally, through simulations conducted in system identification, wind/speech prediction, and stereophonic acoustic echo cancellation, the analytical findings are validated, and the proposed ACGDTLS filter demonstrates superior estimation accuracy compared to the augmented complex-valued least-mean-square algorithm and two state-of-the-art bias-compensated methods. Remarkably, this performance advantage persists across a wide range of step-sizes, input noise variances, and output noise variances.

Generating more constrained seeds for seismic horizon extraction

Enhancing accuracy remains the main challenge for the current horizon extraction algorithms. Adding more manually interpreted seeds helps to improve the accuracy of horizon extraction. We intend to generate more seeds prior to the 3D horizon extraction based on seeds that are manually interpreted on the coarse grid of the seismic survey. First, we compute four horizons for the same seismic sequence boundary using deep learning and constrained least-squares algorithms under the constraints of manually interpreted horizon seeds. Next, we generate more seeds by checking the consistency among those four horizons for each seismic trace, and this process is implemented in the same way as the tying loops of manual horizon interpretation. Finally, we extract horizons using constrained least-squares algorithms with the newly computed seeds. The applications of two real seismic surveys demonstrate that our method can generate horizons with higher accuracy when compared with the horizons computed with only manually interpreted seeds.

Design a novel algorithm for enhancing UWB positioning accuracy in GPS denied environments

Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.

The evolution of flexibility and function in the Fc domains of IgM, IgY, and IgE.

Antibody Fc regions harbour the binding sites for receptors that mediate effector functions following antigen engagement by the Fab regions. An extended "hinge" region in IgG allows flexibility between Fab and Fc, but in both the most primitive antibody, IgM, and in the evolutionarily more recent IgE, the hinge is replaced by an additional domain pair in the homodimeric six-domain Fc region. This permits additional flexibility within the Fc region, which has been exploited by nature to modulate antibody effector functions. Thus, in pentameric or hexameric IgM, the Fc regions appear to adopt a planar conformation in solution until antigen binding causes a conformational change and exposes the complement binding sites. In contrast, IgE-Fc principally adopts an acutely bent conformation in solution, but the binding of different receptors is controlled by the degree of bending, and there is allosteric communication between receptor binding sites. We sought to trace the evolution of Fc conformational diversity from IgM to IgE via the intermediate avian IgY by studying the solution conformations of their Fc regions by small-angle X-ray scattering. We compared four extant proteins: human IgM-Fc homodimer, chicken IgY-Fc, platypus IgE-Fc, and human IgE-Fc. These are examples of proteins that first appeared in the jawed fish [425 million years ago (mya)], tetrapod (310 mya), monotreme (166 mya), and hominid (2.5 mya) clades, respectively. We analysed the scattering curves in terms of contributions from a pool of variously bent models chosen by a non-negative linear least-squares algorithm and found that the four proteins form a series in which the proportion of acutely bent material increases: IgM-Fc < IgY-Fc < plIgE-Fc < huIgE-Fc. This follows their order of appearance in evolution. For the huIgM-Fc homodimer, although none are acutely bent, and a significant fraction of the protein is sufficiently bent to expose the C1q-binding site, it predominantly adopts a fully extended conformation. In contrast, huIgE-Fc is found principally to be acutely bent, as expected from earlier studies. IgY-Fc, in this first structural analysis of the complete Fc region, exhibits an ensemble of conformations from acutely bent to fully extended, reflecting IgY's position as an evolutionary intermediate between IgM and IgE.

Self representation based methods for tensor completion problem

Tensor, the higher-order data array, naturally arises in many fields, such as information sciences, seismic data reconstruction, physics, video inpainting and so on. In this paper, we intend to provide a new model to recover a tensor, based on self-representation, for the all-mode unfoldings of the desired tensor, regardless of the tensor rank. The suggested idea generalizes self-representation to tensor and recovers an incomplete tensor by reconstructing one fiber by others in such a way that they all belong to the same subspace. We design least-square and low-rank self-representation algorithms based on the Linearized Alternating Direction Method utilizing this concept. We show that the proposed algorithms converge to the rank-minimization of the incomplete tensor.

Rapid UV-Vis spectrophotometric method aided by firefly-PLS models for the simultaneous quantification of ciprofloxacin, lomefloxacin, and enrofloxacin in their laboratory mixture, dosage forms and water samples: greenness and blueness assessment

Herein, a novel UV spectrophotometric method coupled with chemometric tools was developed for the simultaneous determination of three fluoroquinolone antibiotics: ciprofloxacin, lomefloxacin, and enrofloxacin. Such integration of UV spectroscopy and chemometric analysis proved to be a simple, rapid, and cost-effective approach for the quantification of these clinically important pharmaceutical compounds and aid in their quality control analysis. The method employed firefly algorithm for variable selection and partial least squares (PLS) regression for model calibration. The developed method was validated by independent test set in addition the accuracy, intra and inter-day precision as per ICH guidelines which showed a satisfactory performance with mean recovery ranged between 98.18 and 101.83 with %RSD < 2. Besides, the developed method displayed ultrasensitive levels with LODs (0.0803, 0.1125, 0.1309 µg/mL) and LOQs (0.2434, 0.3409, 0.3968 µg/mL) for ciprofloxacin, lomefloxacin, and enrofloxacin, respectively. The greenness and blueness of the developed method were also evaluated using the recently proposed Analytical GREEnness metric approach (AGREE) and Blue applicability grade index (BAGI) tools, which showed a high AGREE score of 0.79 and a BAGI score of 77.5. These results indicate that the developed method provides an environmentally friendly alternative to the traditionally used chromatographic techniques, while maintaining high analytical practicability. Finally, the application of the developed methodology was demonstrated on real pharmaceutical and tap water samples, and the results were in good agreement with those obtained by the reference HPLC method indicating the reliability and suitability of the proposed spectrophotometric method for routine analysis of fluoroquinolone antibiotics.

Data-Driven Modeling and Open-Circuit Voltage Estimation of Lithium-Ion Batteries

This article presents a data-driven methodology for modeling lithium-ion batteries, which includes the estimation of the open-circuit voltage and state of charge. Using the proposed methodology, the dynamics of a battery cell can be captured without the need for explicit theoretical models. This approach only requires the acquisition of two easily measurable variables: the discharge current and the terminal voltage. The acquired data are used to build a linear differential system, which is algebraically manipulated to form a space-state representation of the battery cell. The resulting model was tested and compared against real discharging curves. Preliminary results showed that the battery’s state of charge can be computed with limited precision using a model that considers a constant open-circuit voltage. To improve the accuracy of the identified model, a modified recursive least-squares algorithm is implemented inside the data-driven method to estimate the battery’s open-circuit voltage. These last results showed a very precise tracking of the real battery discharging dynamics, including the terminal voltage and state of charge. The proposed data-driven methodology could simplify the implementation of adaptive control strategies in larger-scale solutions and battery management systems with the interconnection of multiple battery cells.

Development of a proton CT imaging system using scintillator-based range detection.

The accuracy of proton therapy and preclinical proton irradiation experiments is susceptible to proton range uncertainties, which partly stem from the inaccurate conversion between CT numbers and relative stopping power (RSP). Proton computed tomography (PCT) can reduce these uncertainties by directly acquiring RSP maps. This study aims to develop a novel PCT imaging system based on scintillator-based proton range detection for accurate RSP reconstruction. The proposed PCT system consists of a pencil-beam brass collimator with a 1mm aperture, an object stage capable of translation and 360° rotation, a plastic scintillator for dose-to-light conversion, and a complementary metal oxide semiconductor (CMOS) camera for light distribution acquisition. A calibration procedure based on Monte Carlo (MC) simulation was implemented to convert the obtained light ranges into water equivalent ranges. The water equivalent path lengths (WEPLs) of the imaged object were determined by calculating the differences in proton ranges obtained with and without the object in the beam path. To validate the WEPL calculation, measurements of WEPLs for eight tissue-equivalent inserts were conducted. PCT imaging was performed on a custom-designed phantom and a mouse, utilizing both 60 and 360 projections. The filtered back projection (FBP) algorithm was employed to reconstruct the RSP from WEPLs. Image quality was assessed based on the reconstructed RSP maps and compared to reference and simulation-based reconstructions. The differences between the calibrated and reference ranges of 110-150 MeV proton beams were within 0.18mm. The WEPLs of eight tissue-equivalent inserts were measured with accuracies better than 1%. Phantom experiments exhibited good agreement with reference and simulation-based reconstructions, demonstrating average RSP errors of 1.26%, 1.38%, and 0.38% for images reconstructed with 60 projections, 60 projections after penalized weighted least-squares algorithm denoising, and 360 projections, respectively. Mouse experiments provided clear observations of mouse contours and major tissue types. MC simulation estimated an imaging dose of 3.44 cGy for decent RSP reconstruction. The proposed PCT imaging system enables RSP map acquisition with high accuracy and has the potential to improve dose calculation accuracy in proton therapy and preclinical proton irradiation experiments.

Image-based mesh generation for constructing a virtual representation of engineered wood product samples

The complex structure of timber has traditionally been difficult to model as it is a highly heterogeneous material. The density and material properties for structural species such as Pinus radiata (radiata pine) can vary greatly across the growth rings. Numerical simulation methods are becoming more prevalent as a method of predicting moisture migration, stress and strain distributions, and fungal/rot intrusion in engineered wood products (EWPs). All these applications require a computational mesh that captures the growth ring structure to facilitate an accurate assessment of the performance of EWPs. In this work, a low-cost image-based algorithm is developed for generating a virtual representation of a small cross laminated timber panel sample. Specifically, the proposed method results in a virtual description of an EWP sample comprised of a triangular prismatic mesh where the nodes are aligned on the growth rings of each individual timber component of the EWP, with specific wood material properties allocated to each mesh element. Each small component is treated individually and we assume there is no longitudinal variation in the density, pith location, and pith angle within the mesh structure. The initial step involves analysing an image of the end grain pattern of a single clear wood sample to identify the growth rings using a spectral clustering algorithm. Next, the centre of the tree (pith) is located through an iterative constrained least-squares algorithm to determine the pith angle. Image analysis of an anatomical image combined with the pith location allows for a constant density value to be assigned to each mesh element. The capability of this framework is then demonstrated by simulating the moisture migration and heat transfer throughout a CLT sample under atmospheric and saturating boundary conditions. Furthermore, the virtual representation provides the basis for simulating additional physical and biological phenomena, such as moisture-induced swelling, decay and fungal growth.

Research on Motion Control and Compensation of UAV Shipborne Autonomous Landing Platform

As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new control compensation method for an autonomous UAV landing platform to address the impact of complex sea conditions on the stability of UAV landing platforms. Firstly, the parallel Stewart platform was introduced as the landing platform, and its structure was analyzed with forward and inverse kinematic calculations conducted in Matlab to verify its accuracy. Secondly, a least-squares recursive AR prediction algorithm was designed to predict the future attitudes of ships under varying sea conditions. Finally, the prediction algorithm was combined with the platform’s control strategy and a dual-sensor system was adopted to ensure the stability of the UAV landing process. The experimental results demonstrate that these innovative improvements enhanced the compensation accuracy by 59.6%, 60.3%, 48.4%, and 47.9% for the rolling angles of 5° and 10° and the pitching angles of 5° and 10°, respectively. Additionally, the compensation accuracy for the roll and pitch in sea states 2 and 5 improved by 51.2%, 59.4%, 58.7%, and 55.9%, respectively, providing technical support for UAV missions such as maritime rescue and exploration.

Robust Direction-of-Arrival Estimation in the Presence of Outliers and Noise Nonuniformity

In direction-of-arrival (DOA) estimation with sensor arrays, the background noise is usually modeled to be uncorrelated uniform white noise, such that the related algorithms can be greatly simplified by making use of the property of the noise covariance matrix being a diagonal matrix with identical diagonal entries. However, this model can be easily violated by the nonuniformity of sensor noise and the presence of outliers that may arise from unexpected impulsive noise. To tackle this problem, we first introduce an exploratory factor analysis (EFA) model for DOA estimation in nonuniform noise. Then, to deal with the outliers, a generalized extreme Studentized deviate (ESD) test is applied for outlier detection and trimming. Based on the trimmed data matrix, a modified EFA model, which belongs to weighted least-squares (WLS) fitting problems, is presented. Furthermore, a monotonic convergent iterative reweighted least-squares (IRLS) algorithm, called the iterative majorization approach, is introduced to solve the WLS problem. Simulation results show that the proposed algorithm offers improved robustness against nonuniform noise and observation outliers over traditional algorithms.

Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire using Confirmatory Factor Analysis.

The Charité Alarm Fatigue Questionnaire (CAFQa) is a 9-item questionnaire that aims to standardize how alarm fatigue in nurses and physicians is measured. We previously hypothesized that it has 2 correlated scales, one on the psychosomatic effects of alarm fatigue and the other on staff's coping strategies in working with alarms. We aimed to validate the hypothesized structure of the CAFQa and thus underpin the instrument's construct validity. We conducted 2 independent studies with nurses and physicians from intensive care units in Germany (study 1: n=265; study 2: n=1212). Responses to the questionnaire were analyzed using confirmatory factor analysis with the unweighted least-squares algorithm based on polychoric covariances. Convergent validity was assessed by participants' estimation of their own alarm fatigue and exposure to false alarms as a percentage. In both studies, the χ2 test reached statistical significance (study 1: χ226=44.9; P=.01; study 2: χ226=92.4; P<.001). Other fit indices suggested a good model fit (in both studies: root mean square error of approximation <0.05, standardized root mean squared residual <0.08, relative noncentrality index >0.95, Tucker-Lewis index >0.95, and comparative fit index >0.995). Participants' mean scores correlated moderately with self-reported alarm fatigue (study 1: r=0.45; study 2: r=0.53) and weakly with self-perceived exposure to false alarms (study 1: r=0.3; study 2: r=0.33). The questionnaire measures the construct of alarm fatigue as proposed in our previous study. Researchers and clinicians can rely on the CAFQa to measure the alarm fatigue of nurses and physicians.