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Cylindrical data are bivariate data formed from the combination of circular and linear variables. Identifying outliers is a crucial step in any data analysis work. This paper proposes a new distribution-free procedure to detect outliers in cylindrical data using the Mahalanobis distance concept. The use of Mahalanobis distance incorporates the correlation between the components of the cylindrical distribution, which had not been accounted for in the earlier papers on outlier detection in cylindrical data. The threshold for declaring an observation to be an outlier can be obtained via parametric or non-parametric bootstrap, depending on whether the underlying distribution is known or unknown. The performance of the proposed method is examined via extensive simulations from the Johnson-Wehrly distribution. The proposed method is applied to two real datasets, and the outliers are identified in those datasets. [ABSTRACT FROM AUTHOR]

Detecting bolt looseness in flanged pipes at an early stage is critical for ensuring safety in the oil and gas industry. Failure to identify such looseness can lead to severe incidents such as leaks and explosions. One effective indirect method for detecting looseness is through vibration analysis of the structures connected by bolted joints. Changes in vibration parameters can indicate looseness, which affects the bolted joints' structural stiffness. This study presents a novel parametric modeling algorithm for detecting bolt looseness in flanged pipes. Autoregressive (AR) model parameters serve as the feature vector for a Mahalanobis distance–based indicator, facilitating accurate looseness detection. Validation was conducted using AR and time-varying autoregressive (TAR) models adapted to the stationary and nonstationary vibration signals of flanged pipes, respectively. The structure was excited using white noise (stationary state) and a moving mass inside the pipe (nonstationary state) to ensure practical applicability. The results demonstrate the method's effectiveness in detecting flange looseness at early stages using an output-only approach. Practical Applications: The developed vibration-based method for detecting flange bolt looseness offers significant practical applications, especially in pipeline integrity management. A novel application involves using a pipeline inspection gauge (PIG) as a moving mass, leveraging its movement within pipes for dynamic excitation similar to the study's approach. Integrating this method with pigging operations enables real-time monitoring of pipeline health. Vibration responses induced by the PIG were analyzed using TAR models and Mahalanobis distance–based indicators to detect various structural issues such as flange bolt looseness, weld cracks, and erosion- and corrosion-induced material loss. However, further investigation is needed to integrate the hardware (such as accelerometers) with the PIG itself, and this is currently under study. Practically, this approach offers: • Early detection of structural defects: identifies issues like flange bolt looseness early, facilitating timely maintenance to prevent failures. • Continuous monitoring: utilizes existing pigging routines for cost-effective, ongoing structural health monitoring. • Enhanced pipeline safety: early defect detection enhances overall safety and reliability. • Improved maintenance scheduling: early detection allows for better planning, reducing downtime and costs. • Applicability to various pipeline systems: adaptable to different pipelines, enhancing its utility across the industry. In conclusion, integrating this vibration-based method with pigging operations offers an efficient solution for maintaining pipeline integrity. By providing early defect warnings, it significantly boosts safety, reliability, and operational efficiency. [ABSTRACT FROM AUTHOR]

The environmental background value of groundwater is an important criterion for determining the causes of groundwater exceedance and identifying the impact of anthropogenic activities on the groundwater environment. In Southwest China, rainfall is abundant and uneven in time, and the karst groundwater cycle is rapid under the influence of rainfall, so it is of great significance to investigate the method of obtaining environmental background values of groundwater in karst area, Southwest China under rainfall events. The anomalous data of water chemistry indicators before and after rainfall were identified by the hydrochemistry map method, Grubbs method and hydrochemistry map combined with Grubbs method, respectively. The effects of the above methods in identifying the anomalous data were compared, and the background eigenvalues of each indicator were calculated. The results show that the background eigenvalues of groundwater environment derived from different anomaly identification methods differ to different degrees before and after rainfall events; the hydrochemistry map combined with Grubbs method can minimize the impact of rainfall events; the method identifies 22 groups of anomalous data before rainfall, and the anomalous data identification rate after rainfall is 79.2%(19 groups)with a repetition rate of 70.8%(17 groups); the background eigenvalues derived before and after the rainfall event are better clustered and less different. In summary, rainfall events affect the statistics and characterization of the environmental background values of groundwater in karst areas, and the hydrochemistry map combined with Grubbs method is the optimal method for identifying anomalous data from rainfall events. So, it is recommended that groundwater sampling should avoid rainfall events as much as possible, so as to more accurately obtain the environmental background values of groundwater in karst areas. [ABSTRACT FROM AUTHOR]

Recognizing the approach of a dramatic expansion of peptide therapeutics reaching the marketplace in recent years, led by GLP-1 receptor agonists such as semaglutide and liraglutide, the Center for Drug Evaluation and Research (CDER) branch of the US Food and Drug Administration (FDA) issued a final guidance in 2021 that was intended to assist generic drug producers in meeting Abbreviated New Drug Application (ANDA) obligations to establish "sameness" of their active peptide drug relative to that produced by innovator companies. Research and a published report by FDA scientists on best practices followed, which promulgated the use of nuclear magnetic resonance (NMR) and principal component analysis (PCA) and established a quantitative standard by which "sameness" of higher order structure for the applicant's peptide drug could be judged. A key requirement is that drug product samples be analyzed directly and non-invasively, a condition which in practice restricts sample modification to the addition of a small amount of deuterium oxide to allow signal lock and spectral data alignment (as required for NMR analysis). In the study described herein, data are presented to illustrate that 1) relatively small differences in sample pH can cause significant shifting of certain proton resonances, 2) that such resonance shifting is readily reversible and due to the degree of protonation of specific amino acid residues (rather than reflecting differences in higher order structure), and 3) that small differences in pH variability between sample cohorts can frequently cause failure to meet the quantitative benchmark established by the agency. Methodology is presented by which drug sample pHs can be aligned with minimal impact, and a recommendation is made that minor sample pH adjustments be allowed in assessing "sameness" of peptide drug higher order structure. [ABSTRACT FROM AUTHOR]

This article reports an unsupervised approach for estimation of the tool condition in precision machining processes. Three campaigns of run-to-failure experiments were conducted on different machines of varying capabilities to develop a generalized solution that is independent of machine intricacies and settings. The proposed approach uses real-time sensory information, such as vibration and spindle power, to infer the tool condition. The proposed approach utilizes distance metrics, Mahalanobis and Euclidean, determined from the sensory information as health indicators of tool wear, which are shown to be strongly correlated with the tool condition. The health indicators have a high correlation coefficient of 0.94 with tool wear measurements, across machines. Unsupervised approaches, such as Jenks Natural Breaks and K-means clustering, use these health indicators to estimate the tool condition. The developed unsupervised approach is also benchmarked using the IEEE PHM 2010 data. A Goodness of Variance Fit of 0.95 and 0.96 is achieved in classifying tool wear across the machining tests conducted in the three campaigns and IEEE PHM 2010 data, respectively. We highlight the explainability of the methods which improve the ease of deployment and engender trust. [ABSTRACT FROM AUTHOR]

ObjectiveIdentify infants with abnormal suckling behavior from simple non-nutritive suckling devices.BackgroundWhile it is well known breastfeeding is beneficial to the health of both mothers and infants, breastfeeding ceases in 75 percent of mother-child dyads by 6 months. The current standard of care lacks objective measurements to screen infant suckling abnormalities within the first few days of life, a critical time to establish milk supply and successful breastfeeding practices.Materials and methodsA non-nutritive suckling vacuum measurement system, previously developed by the authors, is used to gather data from 91 healthy full-term infants under thirty days old. Non-nutritive suckling was recorded for a duration of sixty seconds. We establish normative data for the mean suck vacuum, maximum suck vacuum, suckling frequency, burst duration, sucks per burst, and vacuum signal shape. We then apply computational methods (Mahalanobis distance, KNN) to detect anomalies in the data to identify infants with abnormal suckling. We finally provide case studies of healthy newborn infants and infants diagnosed with ankyloglossia.ResultsIn a series of case evaluations, we demonstrate the ability to detect abnormal suckling behavior using statistical analysis and machine learning. We evaluate cases of ankyloglossia to determine how oral dysfunction and surgical interventions affect non-nutritive suckling measurements.ConclusionsStatistical analysis (Mahalanobis Distance) and machine learning [K nearest neighbor (KNN)] can be viable approaches to rapidly interpret infant suckling measurements. Particularly in practices using the digital suck assessment with a gloved finger, it can provide a more objective, early stage screening method to identify abnormal infant suckling vacuum. This approach for identifying those at risk for breastfeeding complications is crucial to complement complex emerging clinical evaluation technology.Clinical impactBy analyzing non-nutritive suckling using computational methods, we demonstrate the ability to detect abnormal and normal behavior in infant suckling that can inform breastfeeding intervention pathways in clinic.Clinical and Translational Impact Statement: The work serves to shed light on the lack of consensus for determining appropriate intervention pathways for infant oral dysfunction. We demonstrate using statistical analysis and machine learning that normal and abnormal infant suckling can be identified and used in determining if surgical intervention is a necessary solution to resolve infant feeding difficulties.

Durations between events of interest such as intra-day transactions of assets can reflect the volatility of asset prices in financial markets. The diverse dynamics of these intervals, which we refer to as financial durations, offer valuable insights into market behavior for investors. Inspection of streaming price data for structural breaks and timely and accurate detection of transitions between different duration patterns within a trading day enables practitioners to update parameters of suitable duration models. In this article, an innovative Ensemble Penalized Estimating Function (E-PEF) approach is proposed to effectively detect change points in the logarithmic autoregressive conditional duration models for financial durations. As a quasi-score-based online detection approach, this methodology leverages Mahalanobis distances and the block bootstrap sampling method to compute critical values for finite sample time series. The online structural break detection rule is informed by comparing observed quasi-scores in the monitoring period with pre-calculated critical values from training data in an ensemble manner. Extensive simulations demonstrate that the E-PEF method has fast structural break detection performance, while effectively controlling the probability of false detection. In the real data application, we applied our method to identify structural breaks for four assets, explored their relationships with summarized changes in volatility patterns, and noted several considerations for practitioners in the financial market. [ABSTRACT FROM AUTHOR]

[Objective]Apple Alternaria leaf spot can easily lead to premature defoliation of apple tree leaves, thereby affecting the quality and yield of apples. Consequently, accurately detecting of the disease has become a critical issue in the precise prevention and control of apple tree diseases. Due to factors such as backlighting, traditional image segmentation-based methods for detecting disease spots struggle to accurately identify the boundaries of diseased areas against complex backgrounds. There is an urgent need to develop new methods for detecting apple Alternaria leaf spot, which can assist in the precise prevention and control of apple tree diseases.[Methods]A novel detection method named Deep Semi-Non-negative Matrix Factorization-based Mahalanobis Distance Anomaly Detection (DSNMFMAD) was proposed, which combines Deep Semi-Non-negative Matrix Factorization (DSNMF) with Mahalanobis distance for robust anomaly detection in complex image backgrounds. The proposed method began by utilizing DSNMF to extract low-rank background components and sparse anomaly features from the apple Alternaria leaf spot images. This enabled effective separation of the background and anomalies, mitigating interference from complex background noise while preserving the non-negativity constraints inherent in the data. Subsequently, Mahalanobis distance was employed, based on the Singular Value Decomposition (SVD) feature subspace, to construct a lesion detector. The detector identified lesions by calculating the anomaly degree of each pixel in the anomalous regions. The apple tree leaf disease dataset used was provided by PaddlePaddle AI-Studio. Each image in the dataset has a resolution of 512×512 pixels, in RGB color format, and was in JPEG format. The dataset was captured in both laboratory and natural environments. Under laboratory conditions, 190 images of apple leaves with spot-induced leaf drop were used, while 237 images were collected under natural conditions. Furthermore, the dataset was augmented with geometric transformations and random changes in brightness, contrast, and hue, resulting in 1 145 images under laboratory conditions and 1 419 images under natural conditions. These images reflect various real-world scenarios, capturing apple leaves at different stages of maturity, in diverse lighting conditions, angles, and noise environments. This diversed dataset ensured that the proposed method could be tested under a wide range of practical conditions, providing a comprehensive evaluation of its effectiveness in detecting apple Alternaria leaf spot.[Results and Discussions]DSNMFMAD demonstrated outstanding performance under both laboratory and natural conditions. A comparative analysis was conducted with several other detection methods, including GRX (Reed-Xiaoli detector), LRX (Local Reed-Xiaoli detector), CRD (Collaborative-Representation-Based Detector), LSMAD (LRaSMD-Based Mahalanobis Distance Detector), and the deep learning model Unet. The results demonstrated that DSNMFMAD exhibited superior performance in the laboratory environment. The results demonstrated that DSNMFMAD attained a recognition accuracy of 99.8% and a detection speed of 0.087 2 s/image. The accuracy of DSNMFMAD was found to exceed that of GRX, LRX, CRD, LSMAD, and Unet by 0.2%, 37.9%, 10.3%, 0.4%, and 24.5%, respectively. Additionally, the DSNMFMAD exhibited a substantially superior detection speed in comparison to LRX, CRD, LSMAD, and Unet, with an improvement of 8.864, 107.185, 0.309, and 1.565 s, respectively. In a natural environment, where a dataset of 1 419 images of apple Alternaria leaf spot was analysed, DSNMFMAD demonstrated an 87.8% recognition accuracy, with an average detection speed of 0.091 0 s per image. In this case, its accuracy outperformed that of GRX, LRX, CRD, LSMAD, and Unet by 2.5%, 32.7%, 5%, 14.8%, and 3.5%, respectively. Furthermore, the detection speed was faster than that of LRX, CRD, LSMAD, and Unet by 2.898, 132.017, 0.224, and 1.825 s, respectively.[Conclusions]The DSNMFMAD proposed in this study was capable of effectively extracting anomalous parts of an image through DSNMF and accurately detecting the location of apple Alternaria leaf spot using a constructed lesion detector. This method achieved higher detection accuracy compared to the benchmark methods, even under complex background conditions, demonstrating excellent performance in lesion detection. This advancement could provide a valuable technical reference for the detection and prevention of apple Alternaria leaf spot.

In this article, we propose a novel classification approach for functional data based on a shrinkage estimate of functional Mahalanobis distance. We first introduce a new shrinkage functional Mahalanobis distance (SFMD), by using this new distance, we transform the functional observations into a set of vector‐valued pseudo‐samples. Furthermore, we adopt some good classification algorithms designed for multivariate data to this pseudo‐samples instead of the original functional data. The new approach has advantage of highly flexible and scalable, that is, it can easily combine with any classification algorithm, such as support vector machine, tree‐based methods, and neural networks. We demonstrate the performance of the proposed functional classifier through both extensive simulation studies and two real data applications. [ABSTRACT FROM AUTHOR]

In the past decade, various exact balancing‐based weighting methods were introduced to the causal inference literature. It eliminates covariate imbalance by imposing balancing constraints in a certain optimization problem, which can nevertheless be infeasible when there is bad overlap between the covariate distributions in the treated and control groups or when the covariates are high dimensional. Recently, approximate balancing was proposed as an alternative balancing framework. It resolves the feasibility issue by using inequality moment constraints instead. However, it can be difficult to select the threshold parameters. Moreover, moment constraints may not fully capture the discrepancy of covariate distributions. In this paper, we propose Mahalanobis balancing to approximately balance covariate distributions from a multivariate perspective. We use a quadratic constraint to control overall imbalance with a single threshold parameter, which can be tuned by a simple selection procedure. We show that the dual problem of Mahalanobis balancing is an ℓ2 norm‐based regularized regression problem, and establish interesting connection to propensity score models. We derive asymptotic properties, discuss the high‐dimensional scenario, and make extensive numerical comparisons with existing balancing methods. [ABSTRACT FROM AUTHOR]

Levonorgestrel – ethinyl estradiol tablets (Levo-EE) are an essential medication provided through global health supply chains for family planning purposes. Ensuring the quality of Levo-EE and other essential medications in a global supply chain is a primary concern. Portable and handheld diffuse reflectance spectrometers have become more approachable from a cost and usability perspective in recent years. A discriminatory screening method with a handheld near infrared (NIR) spectrometer (900 – 1700 nm) differentiating between brands of Levo-EE and placebos has been constructed. Additionally, the handheld spectrometer was used to determine if environmentally stressed tablets could be identified and differentiated in a qualitative screening method. The same samples were scanned on a benchtop diffuse reflectance NIR spectrometer as well (350 – 2500 nm) for comparison. Brands of oral contraceptives were able to be discriminated by applying a Mahalanobis distance-based classification approach to the handheld spectrometer, though be it at a slightly lower level of discernability due to the reduced spectral range collected. Environmentally stressed samples were applied to the classification model, which were flagged as unlike the reference dataset. Spectra from the environmentally stressed tablets saw an increase in absorbance for water associated peaks with the benchtop spectrometer at approximately 1450 nm and 1949 nm for tablets stored at 30, 40, and 50°C–54°C. Data collected by the handheld showed that the spectrometer offers a low-cost screening approach to oral contraceptive tablets containing two low-dose active ingredients. The handheld spectrometer was also able to flag environmentally stressed samples. The total model accuracy for the target product (brand "A"; N = 560) was above 98% for both benchtop and handheld spectrometers. While these results focus on oral contraceptives, a similar approach could be conducted for other solid dosage tablets for more approachable quality compliance screening. [ABSTRACT FROM AUTHOR]

Simulating sediment transfer processes in catchments has contributed significantly to solving environmental problems due to its importance in the silting of rivers and reservoirs and for controlling the pollution of water bodies. Among the methods used to improve data collection and modelling, the "sediment fingerprinting approach" uses tracers reflecting the composition of eroded soils and sediments in multivariate statistical analyses and mathematical models for optimizing equation systems. Based on generalized least squares (GLS) method and Mahalanobis distance, this study sought to present a computational framework to solve over-determined systems applied to sediment tracing, systematize the uncertainty analysis and sample number optimization. Hence, this approach takes into account the influence of collinearity among the chemical variables that compose the tracer set to be evaluated by the presence of the variance-covariance matrix. A dataset from the Arvorezinha experimental catchment in southern Brazil was used to validate the modeling, and our findings confirmed the assumption of increased uncertainty as the number of target samples decreases in the sources or eroded sediment samples. Sharing the code files with the PySASF (Python package for Source Apportionment with Sediment Fingerprinting) contributes to improving the technique as it allows other researchers to systematically improve the definition of the number of samples required based on the uncertainty analysis. [ABSTRACT FROM AUTHOR]

Small and medium sized enterprises (SMEs) form backbone of a nation's economy. Implementation of technologies like Internet of Things (IoT), however, is a challenge for majority of them as the conventional solution requires a lot of investment. Thus, financially restricted SMEs, especially in developing nations, remain aloof from leveraging the benefits of the technology. Resorting to affordable devices such as low-cost sensors, actuators, processors, servers, and network technologies etc., pose challenges like low memory, low computation power, less transmission power, low data transfer rate, and limited network bandwidth. Consequently, there arises a need to develop IoT based solutions that cater to these challenges so that low budget SMEs are also able to benefit from IoT's umpteen advantages. This paper proposes an affordable IoT based framework for health status monitoring of machines in SMEs keeping the limitations imposed by low cost IoT devices as centre of the solution. The scope of the present research is limited to monitoring the health status of the electromechanical rotating machines only. Four types of commonly occurring faults in the machines at different rotating speeds are investigated using acoustic signals generated within the machines. Mahalanobis distance and Gaussian mixture model (GMM) have been employed for the analysis of the acoustic signals for estimating the unique fault dependent signatures. GMM works satisfactorily with smaller datasets and requires lesser amount of computational power in comparison to machine learning based algorithms. The investigations have showed that GMM may be effectively used in resource constrained SMEs deploying affordable IoT devices for predictive maintenance. [ABSTRACT FROM AUTHOR]

We propose the Minimum Regularized Covariance Trace (MRCT) estimator, a novel method for robust covariance estimation and functional outlier detection designed primarily for dense functional data. The MRCT estimator employs a subset-based approach that prioritizes subsets exhibiting greater centrality based on the generalization of the Mahalanobis distance, resulting in a fast-MCD type algorithm. Notably, the MRCT estimator handles high-dimensional datasets without the need for preprocessing or dimension reduction techniques, due to the internal smoothening whose amount is determined by the regularization parameter α > 0 . The selection of α is automated. An extensive simulation study demonstrates the efficacy of the MRCT estimator in terms of robust covariance estimation and automated outlier detection, emphasizing the balance between noise exclusion and signal preservation achieved through appropriate selection of α. The method converges fast in practice and performs favorably when compared to other functional outlier detection methods. [ABSTRACT FROM AUTHOR]

Frequently, the serum concentrations of TSH (s-TSH) and free thyroxine (s-FT4) are interpreted together when a physician considers the patient's thyroid status. Then, each measurement is compared with its univariate reference interval. However, a pair of s-TSH and s-FT4 may be more appropriately assessed if compared with a bivariate reference interval. We constructed a bivariate reference interval for s-TSH and s-FT4 from their measurements in 495 healthy blood donors. After Box-Cox transformation, we estimated the Mahalanobis distances from each pair of s-TSH and s-FT4 to the center of the bivariate distribution. The 95 percentile in the distribution of the Mahalanobis distances was defined as the limit of the bivariate reference interval. Univariate reference intervals comprising the central 95% (2.5–97.5 percentile) and 97.5% (1.25–98.75 percentile) of reference values were estimated from the same data. Normal thyroid function was defined as both s-TSH and s-FT4 within their respective univariate reference intervals, or as a Mahalanobis distance within the 95% bivariate reference interval. In 177,514 specimens from adult individuals in out-patient care, 76.6% were classified as bivariate normal. The corresponding figures for the 95% and 97.5% univariate reference intervals were 68.9% and 76.2%, respectively. The kappa statistics for classification agreement between the bivariate 95% reference interval and the 95% and 97.5% univariate reference intervals were 0.790 and 0.881, respectively. We thought the bivariate reference interval to be clinically most accurate but were unable to prove it. [ABSTRACT FROM AUTHOR]

The purpose of this article was to introduce the basic concepts, calculation methods, two examples and the calculation methods using SAS related to the cluster analysis of disordered samples. Basic concepts included the definition of distance between samples, the definition of classes, and the characteristics of classes. The calculation method involved the Mahalanobis distance calculation formula and the intermediate distance calculation formula. The data in the two examples were "survey data reflecting air pollution conditions in 39 cities in the United States" and "measurement results of the percentage content of 16 fatty acids in 24 strains". With the help of SAS software, cluster analysis of disordered samples was performed on the data in the two examples, and the explanation of the SAS output results was given. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning approach that enables participants to train models without sharing raw data, thereby protecting data privacy and facilitating collective information extraction. However, the risk of malicious attacks during client communication in federated learning remains a concern. Model poisoning attacks, where attackers hijack and modify uploaded models, can severely degrade the accuracy of the global model. To address this issue, we propose DWAMA, a federated learning-based method that incorporates outlier detection and a robust aggregation strategy. We use the robust Mahalanobis distance as a metric to measure abnormality, capturing complex correlations between data features. We also dynamically adjust the aggregation weights of malicious clients to ensure a more stable model updating process. Moreover, we adaptively adjust the malicious detection threshold to adapt to the Non-IID scenarios. Through a series of experiments and comparisons, we verify our method's effectiveness and performance advantages, offering a more robust defense against model poisoning attacks in federated learning scenarios. [ABSTRACT FROM AUTHOR]

Normative studies are needed to obtain norms for comparing individuals with the reference population on relevant clinical or educational measures. Norms can be obtained in an efficient way by regressing the test score on relevant predictors, such as age and sex. When several measures are normed with the same sample, a multivariate regression-based approach must be adopted for at least two reasons: (1) to take into account the correlations between the measures of the same subject, in order to test certain scientific hypotheses and to reduce misclassification of subjects in clinical practice, and (2) to reduce the number of significance tests involved in selecting predictors for the purpose of norming, thus preventing the inflation of the type I error rate. A new multivariate regression-based approach is proposed that combines all measures for an individual through the Mahalanobis distance, thus providing an indicator of the individual's overall performance. Furthermore, optimal designs for the normative study are derived under five multivariate polynomial regression models, assuming multivariate normality and homoscedasticity of the residuals, and efficient robust designs are presented in case of uncertainty about the correct model for the analysis of the normative sample. Sample size calculation formulas are provided for the new Mahalanobis distance-based approach. The results are illustrated with data from the Maastricht Aging Study (MAAS). [ABSTRACT FROM AUTHOR]

Abstract A temporally stable functional brain network pattern among coordinated brain regions is fundamental to stimulus selectivity and functional specificity during the critical period of brain development. Brain networks that are recruited in time to process internal states of others’ bodies (like hunger and pain) versus internal mental states (like beliefs, desires, and emotions) of others’ minds allow us to ask whether a quantitative characterization of the stability of these networks carries meaning during early development and constrain cognition in a specific way. Previous research provides critical insight into the early development of the theory-of-mind (ToM) network and its segregation from the Pain network throughout normal development using functional connectivity. However, a quantitative characterization of the temporal stability of ToM networks from early childhood to adulthood remains unexplored. In this work, reusing a large sample of children (n = 122, 3–12 years) and adults (n = 33) dataset that is available on the OpenfMRI database under the accession number ds000228, we addressed this question based on their fMRI data during a short and engaging naturalistic movie-watching task. The movie highlights the characters’ bodily sensations (often pain) and mental states (beliefs, desires, emotions), and is a feasible experiment for young children. Our results tracked the change in temporal stability using an unsupervised characterization of ToM and Pain networks DFC patterns using Angular and Mahalanobis distances between dominant dynamic functional connectivity subspaces. Our findings reveal that both ToM and Pain networks exhibit lower temporal stability as early as 3-years and gradually stabilize by 5-years, which continues till adolescence and late adulthood (often sharing similarity with adult DFC stability patterns). Furthermore, we find that the temporal stability of ToM brain networks is associated with the performance of participants in the false belief task to access mentalization at an early age. Interestingly, higher temporal stability is associated with the pass group, and similarly, moderate and low temporal stability are associated with the inconsistent group and the fail group. Our findings open an avenue for applying the temporal stability of large-scale functional brain networks during cortical development to act as a biomarker for multiple developmental disorders concerning impairment and discontinuity in the neural basis of social cognition.

A temporally stable functional brain network pattern among coordinated brain regions is fundamental to stimulus selectivity and functional specificity during the critical period of brain development. Brain networks that are recruited in time to process internal states of others' bodies (like hunger and pain) versus internal mental states (like beliefs, desires, and emotions) of others' minds allow us to ask whether a quantitative characterization of the stability of these networks carries meaning during early development and constrain cognition in a specific way. Previous research provides critical insight into the early development of the theory-of-mind (ToM) network and its segregation from the Pain network throughout normal development using functional connectivity. However, a quantitative characterization of the temporal stability of ToM networks from early childhood to adulthood remains unexplored. In this work, reusing a large sample of children (n = 122, 3–12 years) and adults (n = 33) dataset that is available on the OpenfMRI database under the accession number ds000228, we addressed this question based on their fMRI data during a short and engaging naturalistic movie-watching task. The movie highlights the characters' bodily sensations (often pain) and mental states (beliefs, desires, emotions), and is a feasible experiment for young children. Our results tracked the change in temporal stability using an unsupervised characterization of ToM and Pain networks DFC patterns using Angular and Mahalanobis distances between dominant dynamic functional connectivity subspaces. Our findings reveal that both ToM and Pain networks exhibit lower temporal stability as early as 3-years and gradually stabilize by 5-years, which continues till adolescence and late adulthood (often sharing similarity with adult DFC stability patterns). Furthermore, we find that the temporal stability of ToM brain networks is associated with the performance of participants in the false belief task to access mentalization at an early age. Interestingly, higher temporal stability is associated with the pass group, and similarly, moderate and low temporal stability are associated with the inconsistent group and the fail group. Our findings open an avenue for applying the temporal stability of large-scale functional brain networks during cortical development to act as a biomarker for multiple developmental disorders concerning impairment and discontinuity in the neural basis of social cognition. [ABSTRACT FROM AUTHOR]

Canopy cover fraction is one of the most important criteria for investigating the crop growth and yield and is one of the input data of most plant models. Canopy cover fraction is an easier measurement than the other methods which id depended on field observations or image processing beyond the visible spectrum. In this study, drone images of the sugar beet field in the cropping season of 2015-2016 and on the four dates from late May to late June at the Lindau center of plant sciences research, Switzerland were used. The research was conducted by six plant discrimination indices and three distinct thresholding algorithms to segment sugar beet vegetation. Then, among the 18 investigated methods, the best 6 methods were evaluated by comparing their values with the ground truth values in 30 different regions of the farm and on four dates from the beginning of the four-leaf stage to the end of the six-leaf stage. Results showed that the ExG, GLI, and RGBVI indices, in combination with the Otsu and RidlerCalvard thresholding algorithms, demonstrate optimal performance in vegetation segmentation. The evaluation statistics of NRMSE and R² for the ExG&Otsu method as the most accurate method were obtained as 5.13 % and 0.96, respectively. Conversely, the RGBVI&RC method exhibits the least accuracy in the initial evaluation, with NRMSE and R² values of 8.18 % and 0.87, respectively. Comparative analysis of statistical indicators showed that the ExG&Otsu and ExG&RC methods with similar performance, displaying the highest correlation with ground truths. Additionally, the GLI&Otsu method consistently demonstrates the lowest error compared to ground truths. [ABSTRACT FROM AUTHOR]

Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index, and the potential energy recovery rates were also determined. Two driving datasets were evaluated in terms of chaotic aspects using the Lyapunov exponent, Kolmogorov–Sinai entropy, and fractal dimension metrics. The developed hybrid models are superior to the sub-methods in anomaly detection. Hybrid Model-2 had 2.92% more successful results in anomaly detection compared to Hybrid Model-1. In terms of potential energy saving, Hybrid Model-1 provided 31.26% superiority, while Hybrid Model-2 provided 31.48%. It was also observed that there is a close relationship between anomaly and chaoticity. In the literature where cyber security and visual sources dominate in anomaly detection, a strategy was developed that provides energy efficiency-based anomaly detection and chaotic analysis from data obtained without additional sensor data. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a global health challenge that requires advanced strategies for its early detection and prevention. This study evaluates the South Korean population using the Korea National Health and Nutrition Examination Survey (KNHANES) dataset from 2015 to 2021, provided by the Korea Disease Control and Prevention Agency (KDCA), focusing on improving diabetes prediction models. Outlier removal was implemented using Mahalanobis distance (MAH), and feature selection was based on multicollinearity (MC) and reliability analysis (RA). The proposed Extreme Gradient Boosting (XGBoost) model demonstrated exceptional performance, achieving an accuracy of 98.04% (95% CI: 97.89~98.59), an F1-score of 98.24%, and an Area Under the Curve (AUC) of 98.71%, outperforming other state-of-the-art models. The study highlights the significance of rigorous outlier detection and feature selection in enhancing the predictive power of diabetes risk models. Notably, a significant increase in diabetes cases was observed during the COVID-19 pandemic, particularly linked to male sex, older age, rural location, hypertension, and obesity, underscoring the need for enhanced public health strategies for early intervention and targeted prevention. [ABSTRACT FROM AUTHOR]

Classical estimators fail to be efficient in practical scenarios when data is riddled with extreme values known as outliers. Robust estimation strategies are insensitive to outliers and may be used in such cases. The current work is focused on developing a novel robust estimation strategy using Huber M-estimation. A new chain-product type estimator for population mean has been suggested utilizing data on two auxiliary variables. A numerical comparison has been carried out between the proposed robust estimator and the corresponding classical estimator using real and simulated data containing outliers. Recommendations have been made for its practical use based on the encouraging results. [ABSTRACT FROM AUTHOR]

The present study focuses on addressing the issue of too frequent triggers of deep convection in climate models, which are primarily based on physics-based classical trigger functions such as convective available potential energy (CAPE) or cloud work function (CWF). To overcome this problem, the study proposes using machine learning (ML) based deep convective triggers as an alternative. The deep convective trigger is formulated as a binary classification problem, where the goal is to predict whether deep convection will occur or not. Two elementary classification algorithms, namely support vector machines and neural networks, are adopted in this study. Additionally, a novel method is proposed to rank the importance of input variables for the classification problem, which may aid in understanding the underlying mechanisms and factors influencing deep convection. The accuracy of the ML-based methods is compared with the widely used convective available potential energy (CAPE)-based and dynamic generation of CAPE (dCAPE) trigger function found in many convective parameterization schemes. Results demonstrate that the elementary machine learning-based algorithms can outperform the classical CAPE-based triggers, indicating the potential effectiveness of ML-based approaches in dealing with this issue. Furthermore, a method based on the Mahalanobis distance is presented for binary classification, which is easy to interpret and implement. The Mahalanobis distance-based approach shows accuracy comparable to other ML-based methods, suggesting its viability as an alternative method for deep convective triggers. By correcting for deep convective triggers using ML-based approaches, the study proposes a possible solution to improve the probability density of rain in the climate model. This improvement may help overcome the issue of excessive drizzle often observed in many climate models. [ABSTRACT FROM AUTHOR]

Bearings are foundational supporting components in diverse mechanical systems, essential for the reliable operation of these systems through real‐time monitoring and precise health state assessment. However, vibration signals from bearings in practical equipment often contain excessive noise and redundant information, complicating health state assessment. To address this challenge, this paper proposes a neural network‐based method named parallel encoder‐decoder (PED). This method features a parallel architecture that combines the long short‐term memory network and the temporal convolutional network for the encoder, along with a self‐attention module for the decoder. PED is adept at learning the temporal representations hidden in original signals and filtering vibration signals to remove noise and redundant information. Additionally, a multi‐objective loss function is developed to enhance the prediction results. A normalized Mahalanobis distance‐based metric is then employed to compare residual signals during bearing operation with those under normal conditions. The case study evaluates the PED observer's proficiency in accurately predicting vibration signals and assessing the performance of health indicator curves, demonstrating the proposed PED observer's superiority over conventional networks. [ABSTRACT FROM AUTHOR]

The visual analysis method of educational data statistics based on big data mining is studied to improve students' academic performance. Introducing the Mahalanobis distance and covariance matrix into the Fuzzy C-Means (FCM) clustering algorithm improves the FCM clustering algorithm. Through the improvement of the FCM clustering algorithm, the education data is mined from the massive original education data. The mining results are analyzed statistically, and the statistical analysis chart of education data is drawn. By improving the force-guided layout algorithm, the mined educational data points are written into the elastic graph layout to realize the visual layout. The ECharts data visualization analysis component presents the visual layout results of education data points and the statistical analysis charts of education data. Experiments show that this method can effectively mine educational data and draw statistical analysis charts of educational data. Among them, learning analysis data occupy the highest proportion (15%), and privacy protection data occupy the lowest proportion (only 1%). The method can effectively lay out the educational data points and has a better visual effect. This method can effectively present the results of statistical analysis of educational data in visual form, in which learning analysis data is the most important. [ABSTRACT FROM AUTHOR]

Although an autonomous underwater vehicle (AUV) is noted for its good autonomy, concealment and anti-interference ability, errors in its inertial navigation system (INS) inevitably increase over time, leading to positional failure during long-term voyages. Terrain-assisted navigation can help the INS to correct its position. The traditional iterative closest contour point (ICCP) achieves high matching accuracy when the initial position error of the INS is small, but is prone to mismatching when the initial error is large. This study combines ICCP with particle swarm optimization (PSO) to overcome this problem. First, the global optimization ability of PSO is improved by changing the acceleration factor and introducing an artificial bee colony (ABC) onlooker bee greedy search (ABC- ω APSO). Second, the Euclidean distance of ICCP is replaced by the Mahalanobis distance to abate the influence of system error on the matching accuracy. Finally, the initial position error is reduced by rough matching using the ABC- ω APSO, which has global optimization capability. Next, fine matching is performed by ICCP. This two-step process resolves the sensitivity problem of ICCP to the initial position error. The experimental results revealed a good matching effect after the double-matching procedure. When the initial INS errors were 0.55′ to the east and 0.55′ to the north, the matching error was reduced to 89.3 m, suggesting that the approach can realize autonomous passive navigation of AUVs. [ABSTRACT FROM AUTHOR]

Due to the rapid development of internet technology, knowledge acquisition has become more convenient and efficient in network operations. University libraries serve as important resources for readers to acquire knowledge, and online resources and services in libraries have become the main direction for readers to acquire knowledge at present. Research the use of binary K-means clustering algorithm and library reader portrait technology to optimize the design of the reader portrait module and construct a multidimensional and multi perspective reader feature system. Reuse Spark programming language and support vector machine to perform computational processing on reader profile data to ensure accurate segmentation of the dataset. Finally, three datasets were used to test the accuracy and efficiency of the algorithm. The experimental comparison shows that the mining and precision segmentation of parallel SVM on the dataset are 93.20%, 85.16%, and 79.35% on the sample set, respectively, in order to optimize the mining performance of the data. The MD multi view binary K-means algorithm has a total Mahalanobis distance of 3.543, 5.268, and 22.385 on the sample dataset, respectively, to demonstrate its superiority in clustering performance. Therefore, the multi view binary K-means algorithm based on Mahalanobis distance has high advantages in reader portrait technology design, and provides technical support and theoretical reference for library reader portrait technology. [ABSTRACT FROM AUTHOR]

Based on the amplitude attribute analysis of ground penetrating radar (GPR) data, this paper applies GPR to the identification of internal damages and abnormal objects in stone walls of Tibetan ruins. The corresponding relationship between the characteristics of internal damages and abnormal objects and the radar data is explored through the Tibetan stone walls (TSWs) simulation test. The identification of location and size of the damages and abnormal objects in TSWs is realized based on the Mahalanobis distance abnormal data discrimination method. Using the normal distribution and the noncentral chi-square distribution, the identification law of types of the damages and abnormal objects is constructed, which takes root mean square (RMS) amplitude and interface reflection coefficient as the characteristic values. The field detection results of TSWs at three Tibetan sites are taken as an extension and supplement to the simulation results, and a set of identification atlas for the location, size and type of the internal damages and abnormal objects in TSWs is established. The application results of the identification atlas were verified by the field detection and the results show that the atlas has a high accuracy, which can significantly improve the efficiency of the identification, and can provide a basis for the performance research, protection and maintenance of TSWs. [ABSTRACT FROM AUTHOR]