Statistical Characteristics Research Articles

DIFFERENTIAL TREATMENT EFFECTS OF STANDARD AND HYPOFRACTIONATED RADIATION REGIMENS IN GLIOBLASTOMA PATIENTS.

The identification of the subgroups with differential treatment effects (DTE) is important for decisionmaking in personalized treatment. The DTE analysis assists in identifying patients who are more likely to benefit from a particular treatment regimen. The aim of the study was to analyze DTE in terms of the survival of glioblastoma (GBM) patients in the groups of standard radiotherapy (SRT) and hypofractionated radiotherapy (HRT) by the multicluster modeling of homogenous groups while retaining the statistical characteristics of the overall primary study cohort. The cohort of 159 patients with newly diagnosed GBM stratified according to the radiotherapy regimen (HRT group (n = 110/69.2%); SRT group (n = 49/30.8%)) was evaluated retrospectively. Forty-eight subgroups (multiclusters) were created by enumerating all possible combinations of 5 significant covariates (age, sex, the radicality of the surgical resection, chemotherapy, and Karnofsky performance status) of the Cox model. The DTE for the cancerspecific survival (CSS) within 48 modeled multiclusters were studied by comparing the interpolated Weibull CSS curves according to the Kolmogorov - Smirnov test. The findings showed that the SRT group was superior to the HRT group by CSS only in 3 of the modeled clusters presenting clinical scenarios with a non-radical tumor resection, no chemotherapy, and low Karnofsky functional status (≤ 70 scores) (Cluster 10: male aged < 60; Cluster 21: female aged ≥ 60; Cluster 22: male aged ≥ 60). Most of the studied clinical variants (45 of 48 multiclusters) did not demonstrate a significant difference when comparing the interpolated Weibull curves of the CSS for the SRT and HRT groups according to the Kolmogorov - Smirnov test (p ≥ 0.05). We propose a novel multicluster modeling approach that addresses DTE in relatively small samples of GBM patients receiving SRT or HRT. This original analytical method can be taken into consideration while designing new well-powered prospective trials aimed at the subgroup analysis in GBM patients who will be most beneficial from personalized treatment strategies.

Application of Paired Correlation Algorithms for the Distance Matrices Between DNA Chains

Authors:This paper is a continuation of some previous works by the authors. We consider various algorithms for calculating distances between genomes of similar species (we use primarily mitochondrial DNA, mtDNA) and various distance matrices between the same genomes obtained on the basis of these algorithms. We can say, just to simplify the situation a little, that all our publications on the subject of DNA analysis are associated with various applications of metrics set on such matrices. The paper also has a second subject, i.e., the study of the obtained distance matrices using special statistical characteristics. We consider two matrices obtained for the mtDNA of 32 species of monkeys; the species were selected so that they all belong to different genera. For them, we have obtained 2 matrices of distances between genomes corresponding to the Jaro –Winkler’s and Needleman –Wunsch’s algorithms. Next, we considered all the triangles obtained in these matrices, and for each of them we used a specially calculated badness. It is actually a measure of the deviation of the resulting triangle from some acuteangled isosceles one. For two sequences of such badness, we have considered variants of paired correlation. At the same time, in addition to the two standard pair correlation algorithms (Spearman’s and Kendall’s ones), we also considered a new algorithm proposed by us. The reason for considering this new algorithm is as follows. In the usual way of calculating the correlation, we consider only the set of pairwise values of two random variables, without taking into account the pairs themselves. Vice versa, in both of the mentioned pair correlation algorithms, despite their slight difference, we consider only the order of the elements in these pairs, not paying attention to the values themselves; we specifically note that this also applies to Spearman’s criterion, which is usually written about as being more suitable for measurements made on an ordinal scale. In our proposed algorithm, we tried to take into account both the value of both random variables and their order in pairs. The results obtained are of interest. Thus, the “pole” variants (i.e., the usual correlation formula and standard pair correlation algorithms) show some (though very small) correlation between two sequences of 4960 pairs of triangles: from 0.1 to 0.4, depending on the specific algorithm, on whether preliminary normalization was carried out, etc. And the “intermediate” variant (taking into account both the order of pairs and the values of random variables) showed a complete lack of connection: the absolute value of the correlation coefficient did not exceed 0.006. Even more interesting is another result obtained in the work, which can be called a small connection between two well-known algorithms for determining the distances between genomes, namely, algorithms of Jaro –Winkler and Needleman –Wunsch.

Spatiotemporal statistical features of velocity responses to traffic congestions in a local motorway network

Abstract The causal connection between congestions and velocity changes at different locations induces various statistical features, which we identify and measure in detail. We carry out an empirical analysis of large-scale traffic data on a local motorway network around the Breitscheid intersection in the North Rhine-Westphalia, Germany. We put forward a response function which measures the velocity change at a certain location versus time conditioned on a congestion at another location. We use a novel definition of the corresponding congestion indicator to ensure causality. We find that the response of velocities to the congestion exhibits phase changes in time. A negative response at smaller time lags transforms into positive one at larger time lags, implying a certain traffic mechanism. The response decays as a power law with the distance. We also identify a scaling property leading to a collapse of the response functions on one curve.

Inertia Drives Concentration-Wave Turbulence in Swimmer Suspensions.

We discover an instability mechanism in suspensions of self-propelled particles that does not involve active stress. Instead, it is driven by a subtle interplay of inertia, swimmer motility, and concentration fluctuations, through a crucial time lag between the velocity and the concentration field. The resulting time-persistent state seen in our high-resolution numerical simulations consists of self-sustained waves of concentration and orientation, transiting from regular oscillations to wave turbulence. We analyze the statistical features of this active turbulence, including an intriguing connection to the Batchelor spectrum of passive scalars.

Are Late Talkers Just Late? Neighborhood Density and Word Frequency Properties of Late Talkers' Spoken Vocabularies.

Typically developing toddlers extract patterns from their input to add words to their spoken lexicons, yet some evidence suggests that late talkers leverage the statistical regularities of the ambient language differently than do peers. Using the extended statistical learning account, we sought to compare lexical-level statistical features of spoken vocabularies between late talkers and two typically developing comparison groups. MacArthur-Bates Communicative Developmental Inventories American English Words and Sentences (N = 1,636) were extracted from Wordbank, a database of CDIs. Inventories were divided into three groups: (a) a late talker group (n = 202); (b) a typically developing age-matched group (n = 1,238); and (c) a younger, typically developing group (n = 196) matched to the late talkers on expressive language. Neighborhood density and word frequency were calculated for each word produced by each participant and standardized to z scores. Mixed-effects models were used to evaluate group differences. The late talker and younger, language-matched groups' spoken vocabularies consist, on standardized average, of words from denser phonological neighborhoods and words higher in frequency of occurrence in parent-child speech, compared to older, typically developing toddlers. These findings provide support for the extended statistical learning account. Late talkers appear to generally be extracting and using similar patterns from their language input as do younger toddlers with similar levels of expressive vocabulary. This suggests that late talkers may be following a delayed, not deviant, trajectory of expressive language growth.

Observation of Statistical Characteristics and Vertical Structures of Surface Warm Cyclonic Eddies and Cold Anticyclonic Eddies in the North Pacific Subtropical Countercurrent Region

Conventional wisdom about mesoscale eddies is that cyclonic (anticyclonic) eddies are commonly associated with cold(warm) surface cores. Nevertheless, plenties of surface warm cyclonic eddies (WCEs) and cold anticyclonic eddies (CAEs) in the North Pacific Subtropical Countercurrent (STCC) region are observed by a synergistic investigation based on data from satellite altimetry, microwave radiometer, and Argo float profiles in this study. The results indicate that these two types of abnormal eddies (WCEs and CAEs) are prevalent in the STCC region, comprising approximately 30% of all eddies detected via satellite observations. We then analyze their spatial-temporal distribution characteristics and composite vertical structures. A statistical comparison with surface cold cyclonic eddies (CCEs) and warm anticyclonic eddies (WAEs) reveals notable differences between the anomalous and typical eddies. Additionally, we present the composite vertical structures of temperature and salinity anomalies for the anomalous eddies across five delineated subregions within an eddy-coordinate system. Furthermore, the close relationship between these abnormal eddies and subsurface-intensified mesoscale eddies are discussed.

Size-Dependent Mechanical Properties and Excavation Responses of Basalt with Hidden Cracks at Baihetan Hydropower Station through DFN–FDEM Modeling

Basalt is an important geotechnical material for engineering construction in Southwest China. However, it has complicated structural features due to its special origin, particularly the widespread occurrence of hidden cracks. Such discontinuities significantly affect the mechanical properties and engineering stability of basalt, and related research is lacking and unsystematic. In this work, taking the underground caverns in the Baihetan Hydropower Station as the engineering background, the size-dependent mechanical behaviors and excavation responses of basalt with hidden cracks were systematically explored based on a synthetic rock mass (SRM) model combining the finite-discrete element method (FDEM) and discrete fracture network (DFN) method. The results showed that: (1) The DFN–FDEM model generated based on the statistical characteristics of the geometric parameters of hidden cracks can consider the real structural characteristics of basalt, whereby the mechanical behaviors found in laboratory tests and at the engineering site could be exactly reproduced. (2) The representative elementary volume (REV) size of basalt blocks containing hidden cracks was 0.5 m, and the mechanical properties obtained at this size were considered equivalent continuum properties. With an increase in the sample dimensions, the mechanical properties reflected in the stress–strain curves changed from elastic–brittle to elastic–plastic or ductile, the strength failure criterion changed from linear to nonlinear, and the failure modes changed from fragmentation failure to local structure-controlled failure and then to splitting failure. (3) The surrounding rock mass near the excavation face of underground caverns typically showed a spalling failure mode, mainly affected by the complex structural characteristics and high in situ stresses, i.e., a tensile fracture mechanism characterized by stress–structure coupling. The research findings not only shed new light on the failure mechanisms and size-dependent mechanical behaviors of hard brittle rocks represented by basalt but also further enrich the basic theory and technical methods for multi-scale analyses in geotechnical engineering, which could provide a reference for the design optimization, construction scheme formulation, and disaster prevention of deep engineering projects.

Cloud detection for HY-1C/COCTS over the ocean based on spectral-and-textural-information-guided deep neural network

AbstractGiven that clouds can absorb and scatter radiation signals in the visible and infrared bands, cloud detection is a key preprocessing step for ocean color and sea surface temperature retrievals. In this research, a Spectral-and-Textural-Information-Guided deep neural Network (STIGNet) is designed for cloud detection in global ocean data from the Haiyang-1C (HY-1C)/Chinese Ocean Color and Temperature Scanner (COCTS). Considering the spectral and textural properties of clouds, the model incorporates HY-1C/COCTS spectral data, differences in brightness temperature (BT), local statistical characteristics of BT, and geographical location information–all of which are closely related to cloud features. Notably, an edge learning module is implemented to emphasize edge features during the training process. We construct a HY-1C/COCTS cloud detection dataset to train and test the cloud detection model. In the dataset, labels are generated by combining the Bayesian cloud detection method with a manual mask. Analysis of the resulting cloud detection images indicates that STIGNet exhibits accurate performance across various types of clouds while showing minimal overestimated errors in areas such as ocean fronts or sun glints, where they tend to occur frequently. The ablation experiments performed on physical-based input features and edge learning modules show enhancements in cloud detection accuracy. Evaluation results demonstrate an overall accuracy of 96.64%, with a cloud overestimated error of 1.61% and a cloud missed error of 1.76%. These findings highlight the effectiveness of STIGNet in generating precise cloud masks for HY-1C/COCTS data.

Using high-repeatable radiomic features improves the cross-institutional generalization of prognostic model in esophageal squamous cell cancer receiving definitive chemoradiotherapy

ObjectivesRepeatability is crucial for ensuring the generalizability and clinical utility of radiomics-based prognostic models. This study aims to investigate the repeatability of radiomic feature (RF) and its impact on the cross-institutional generalizability of the prognostic model for predicting local recurrence-free survival (LRFS) and overall survival (OS) in esophageal squamous cell cancer (ESCC) receiving definitive (chemo) radiotherapy (dCRT).MethodsNine hundred and twelve patients from two hospitals were included as training and external validation sets, respectively. Image perturbations were applied to contrast-enhanced computed tomography to generate perturbed images. Six thousand five hundred ten RFs from different feature types, bin widths, and filters were extracted from the original and perturbed images separately to evaluate RF repeatability by intraclass correlation coefficient (ICC). The high-repeatable and low-repeatable RF groups grouped by the median ICC were further analyzed separately by feature selection and multivariate Cox proportional hazards regression model for predicting LRFS and OS.ResultsFirst-order statistical features were more repeatable than texture features (median ICC: 0.70 vs 0.42–0.62). RFs from LoG had better repeatability than that of wavelet (median ICC: 0.70–0.84 vs 0.14–0.64). Features with smaller bin widths had higher repeatability (median ICC of 8–128: 0.65–0.47). For both LRFS and OS, the performance of the models based on high- and low-repeatable RFs remained stable in the training set with similar C-index (LRFS: 0.65 vs 0.67, p = 0.958; OS: 0.64 vs 0.65, p = 0.651), while the performance of the model based on the low-repeatable group was significantly lower than that based on the high-repeatable group in the external validation set (LRFS: 0.61 vs 0.67, p = 0.013; OS: 0.56 vs 0.63, p = 0.013).ConclusionsApplying high-repeatable RFs in modeling could safeguard the cross-institutional generalizability of the prognostic model in ESCC.Critical relevance statementThe exploration of repeatable RFs in different diseases and different types of imaging is conducive to promoting the proper use of radiomics in clinical research.Key PointsThe repeatability of RFs impacts the generalizability of the radiomic model.The high-repeatable RFs safeguard the cross-institutional generalizability of the model.Smaller bin width helps improve the repeatability of RFs.Graphical

Inflated effect estimates for vitamin D supplementation are driven by common meta-analytical errors.

Han et al. (J Int Soc Sports Nutr 16:55, 2019) sought to quantify the effects of vitamin D supplementation on strength outcomes among athletes in a meta-analysis. The authors reported a pooled effect size (standardized mean difference; SMD) of -0.75 (95% CI: -1.82 to 0.32, p = 0.17) in favor of supplementation, but the analytical approach was not appropriate for a pooled analysis of randomized controlled trials and the effect sizes were calculated incorrectly. This letter discusses how these issues impact the results and interpretation of the paper, then provides an update on the estimated average effect of vitamin D on strength outcomes in athletes. Identified errors included the use of within-group rather than between-group effect size metrics, the use of standard error values in place of standard deviations, and failure to account for correlated observations within the model. The data were reanalyzed after correcting for these common meta-analytic errors. The results of this reanalysis reflect a dramatically smaller and statistically nonsignificant pooled effect estimate of SMD = 0.16 (-0.24 to 0.56, p = 0.43) in favor of supplementation. Further, the model from this reanalysis has more favorable statistical characteristics than the original analysis, as evidenced by a fairly symmetrical funnel plot and a nonsignificant result for Cochrane's Q test (Q = 5.02, p = 0.41). In order to disseminate robust information to sports nutrition practitioners and researchers, it is critically important for meta-analyses to produce valid effect estimates that are appropriate for the underlying study designs and calculated without error. This letter highlights common errors to inform the calculation and interpretation of future meta-analyses in sports nutrition.

Modeling and analysis of propagation characteristics of WBAN in hospital ward scenarios

AbstractTo explore the feasibility of high‐frequency indoor wireless body area network (WBAN) communications, the propagation characteristics of WBAN in simulated hospital ward scenarios at 11 GHz are studied. Based on the extensive measured data, the impact of the patient's lying angle (LA) on channel characteristics has been analyzed, such as statistical characteristics of path loss (PL), shadowing effect, and root mean square delay spread. Moreover, a PL model with LA factor is presented for the hospital ward scene, and the influence of LA on PL in the presence of height difference is also analyzed. The research results indicate that, the PL caused by LA (abbreviated as PLA) follows a power function with respect to the LA in case of a fixed distance between the transmitter and receiver. When the LA is fixed, PLA exhibits a cubic function relationship with the distance between the transmitter and receiver. In addition, the presence of patient's LA can cause more significant multipath effect. However, the impact of LA gradually weakens as the height difference increases when there is a height difference. The research results provide a theoretical basis and practical basis for utilizing the 11 GHz frequency band in indoor WBAN in the future.

Harnessing spectrophotometry resolution power for determining ternary mixture for respiratory disorders treatment in their pharmaceutical formulation.

A ternary mixture incorporating Hydroxyzine hydrochloride (HYX), Ephedrine hydrochloride (EPH) and Theophylline (THP) frequently prescribed for the treatment of respiratory diseases. Herein, two spectrophotometric methods are designated and applied to resolve these three components in their mixture. Method A is ratio-subtraction combined with derivative spectrophotometry, where THP can be determined directly at its λmax 271 nm (neither HYX or EPH interfere), then for determination of HYX and EPH, the ternary mixture was divided by 22 μg/mL of THP and after subtraction of the plateau region, HYX can be determined directly at its λmax 234.2 nm (absence of EPH intervention). Finally, the third derivative (3D) spectrophotometric approach was utilized to estimate EPH by detecting the peak amplitude at 222 nm with Δλ = 4 and a scaling factor 100. Principal Component Regression (PCR) and Partial Least Squares (PLS), two multivariate calibration approaches, were applied effectively in Method B. This method effectively quantified the mixture under investigation by using the absorption spectra obtained from suitable solutions of the three components in the 210-230 nm region. The calibration models were evaluated using cross-validation with PCR and PLS, producing statistical characteristics that demonstrate the effectiveness of the calibration models. Synthetic and pharmaceutical preparations were also used to conduct external validation. In pharmaceutical formulation, these methods were successfully applied to analyze HYX, EPH, and THP without overlap from formulation's excipients. Moreover, the study's findings were statistically contrasted with those of earlier reported HPLC method. Appraisal approaches were used to determine whether the new spectrophotometric methods had an adverse environmental impact involving the Green Analytical Procedure Index (GAPI) and the AGREE (Analytical Greenness). These evaluations delivered information about the methods' eco-friendliness and sustainability, proving that they are in line with ecologically attributed practices. Furthermore, the Blue Applicability Grade Index (BAGI) was utilized to identify and verify the feasibility and practicality of the suggested approaches.

7847 Radiomic Ultrasound Checkup of Thyroid Nodules Before Radiodiodine Therapy

Abstract Disclosure: A.A. Trukhin: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). A.V. Manaev: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). S.M. Zakharova: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). M.S. Sheremeta: None. E.A. Troshina: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). Background: Over 83 years radioiodine therapy (RIT) of autonomous thyroid nodules still have uncovered pages, according to its complexity and thyroid nodules variety. There are two main criteria to allow RIT – no malignancy and presence of iodine uptake, the last one could be checked by Tc-99m-pertachnetate or I-123, I-131 iodine scintigraphy. Case with thyroid nodules first undergo ultrasound (US) TI-RADS classification, then fine needle aspiration stratification according to the Bethesda system. Publications applied such algorithm shows 75-85% accuracy in malignancy determination, that in 15-25% can lead to improper RIT application. Aim: Method development for thyroid nodules ultrasound checkup before I-131 administration and I-131 24h uptake prognosis. Methods: Study includes 150 nodules (65 benign, 60 malignant, 25 autonomous benign thyroid nodules). Both US longitudinal and transversal thyroid nodule projections were obtained via GE Voluson E8 (36% benign cases / 27% malignant cases) and GE Logiq E (64% benign cases / 73% malignant cases) in cinematic loop regime. Autonomous thyroid nodules underwent I-131 pharmacokinetics examination with 24h uptake determination and RIT. Statistical textural features were obtained through spatial adjacency matrix. Results: Analysis of 16 statistical textural features shows weak correlation between 11 statistical textural features E (Energy), S (Entropy), LU (Local homogeneity), MP (Maximum probability), TR (Matrix trace), CORR (Correlation), IMC (information measure of correlation), DV (Difference variance), DE (Difference entropy), SE (Sum entropy), SA (Average value of the sum). ROC-analysis using 11 statistical textural features shows the best model for benign/malignance determination with AUC = 0.83, Se = 74%, Sp = 73%, PPV = 75%, NPV = 72%, Acc = 74%. I-131 pharmacokinetics examination shows strong correlation SA to I-131 24h uptake. Conclusions: The performed study demonstrates the possibility of using textural statistical features of ultrasound images as instrument for thyroid nodules ultrasound checkup before I-131 administration, for extra FNA prescription and prognosis the I-131 24h important in exact therapeutic I-131 activity calculation. Presentation: 6/2/2024

Automated EEG-based language detection using directed quantum pattern technique

Electroencephalogram (EEG) signals contain complex useful information about brain activities. These EEG signals are noisy, highly varying and nonstationary in nature. Hence, extracting meaningful information from these signals is challenging. The existing machine learning systems struggle to capture the minute changes from the signals and yield high performance.This study introduces a novel quantum-inspired feature extraction technique called Directed Quantum Pattern (DQP), designed to address these challenges by using a lattice structure to capture directional binary features. These directions (paths) are computed using a maximum function providing a dynamic and adaptive feature representation.This paper presents a novel DQP-LangNet developed using DQP for automated classification of two- languages using EEG signals. We have proposed a hybrid approach, combining DQP, statistical features, and multi-level discrete wavelet transform (MDWT) to extract salient features similar to the deep learning approach. The EEG dataset consisting of 14 channels, produces 7 feature vectors per channel, yielding 98 feature vectors. Neighborhood component analysis and Chi-square (Chi2) feature selection approaches generated 196 feature vectors.In addition to the innovative feature extraction a new classification structure called “t” is proposed k-nearest neighbor (tkNN) and support vector machine (tSVM) classifiers are employed. Using the proposed tkNN and tSVM classifiers, 392 (=196×2) classifier-based outcomes are obtained. To further improve classification performance, we applied the iterative majority voting (IMV) technique to automatically select the best result.Our DQP-based model achieved a classification accuracy of 95.68 %using EEG language dataset with leave-one-subject-out (LOSO) cross-validation strategy. Also, an explainable feature engineering (XFE) structure of DQP-LangNet is employed to obtain channel-specific explainable results. Our proposed DQP-LangNet model can be employed for other applications in neuroscience.

The effects of climate change on Pinus tabulaeformis radial growth in the Xiaowutai Mountains, northern China

Clarifying the climate change effects on the radial growth of trees has implications for sustainable forest management, especially under global warming. To investigate tree growth responses to regional climate change of Xiaowutai Mountain, four Chinese pine (Pinus tabulaeformis) ring-width index chronologies were established at different elevations (1290–1600 m). Species growth trends were estimated using climate change projections derived from global climate models. The results show: (1) the four ring-width chronologies exhibited strong statistical characteristics, making them suitable for dendroclimatology studies. Radial growth-climate relationships were highly consistent, showing a negative correlation with previous September temperatures and current May–June temperatures, as well as a positively correlated with precipitation and Palmer Drought Severity Index during the corresponding period; and (2) climate change scenarios revealed that temperature will gradually increase on the Xiaowutai Mountain, and only a slight variation in precipitation is expected. Chinese pine radial growth may show a decline under future climate change.

COMPUTER DIAGNOSTICS OF THE CONDITION OF SHIP ROLLING BEARINGS DURING THEIR OPERATION

The results of statistical processing of vibration signals obtained during diagnostics of ship power plants during the operation process are offered. A distinctive feature of monitoring the technical condition of elements of ship power plants is the uncertainty in the fixation and duration of extreme loads. The peculiarity of computer vibration diagnostics of rotary mechanisms is determined, which is that, in particular, physical measurements of vibration signals require their use in mathematical models of the physical state of the bearing. Calculations of statistical characteristics of vibration signals in the time domain, such as standard deviation (Std), asymmetry (Skewness), kurtosis (Kurtosis), full range of oscillations (Peak2Peak), root mean square value (RMS), cross factor (CrestFactor), form- factor (ShapeFactor), impulse factor (ImpulseFactor), marginal factor (MarginFactor), energy (Energy). The variety of diagnostic signals leads to the need to merge indicators of different dimensions without losing available information into a single generalizing indicator, for which the method of principal components is used in the work. The methodology of statistical processing and practical implementation of diagnostics of vibration signals during the analysis of damage evolution of rolling bearings of turbochargers of marine power plants are described. A new computer information parameter of vibration diagnostics based on the analysis of the first principal component in the time domain and spectral excess in the frequency domain has been identified and experimentally confirmed. The use of new diagnostic parameters makes it possible not only to judge the degree of damage to the rolling bearing, but also to observe the dynamics of the development of the defect and make a relative forecast of the working life of the rolling bearing. A real practical situation is considered - computer diagnostics of vibration signals of ship rolling bearings of a point defect of an external defect of a rolling bearing ring. Determining the applicability of the principal component method to other types of defects showed their potential for vibrodiagnosis of defects located on the inner ring of the bearing and when the ball falls out of the holder.

A robust approach to transformer fault diagnosis: integrating time-current loci analysis with statistical feature extraction

The accurate differentiation of inrush currents and inter-turn faults in power transformers is critical for ensuring the reliability and safety of electrical power systems. Traditional methods often face challenges in distinguishing between these conditions due to their overlapping characteristics, leading to potential misoperations and system instability. This paper presents a novel solution through the introduction of the time-current loci (TIL) method, which effectively addresses this critical issue by providing a robust mechanism for classifying normal operating conditions, inrush currents, and inter-turn faults. The TIL method involves plotting time against current over a single cycle, generating distinct loci patterns that serve as a visual and analytical foundation for classification. By extracting and analyzing key statistical features from these loci, the method enhances the accuracy of fault detection. Specifically, the rate of change in time and current is used to determine the orientation of the TIL curve, with additional features such as the mean orientation and skewness being computed to capture the unique geometric properties associated with each operating condition. This approach simplifies the analysis process, eliminating the need for complex machine learning algorithms and reducing computational demands, making it highly suitable for real-time implementation. Experimental validation was carried out using a 1 kVA, 230 V/230 V transformer under various operating conditions, and the results demonstrated the effectiveness of the TIL method in reliably distinguishing between normal conditions, inrush currents, and inter-turn faults. The visual nature of the loci plots not only aids in accurate classification but also provides an intuitive understanding of transformer behavior, facilitating quick and informed decision-making by operators. This advancement addresses a critical challenge in transformer protection, offering a more reliable and efficient solution compared to traditional techniques.

Semantic Web-Based Prediction Method for Goodwill Impairment in Listed Companies Using Mamba

In the context of globalization and intense market competition, mergers and acquisitions (M&amp;A) have become a common strategy for enterprises to expand and transform. However, goodwill issues in M&amp;A are increasingly concerning. Traditional goodwill impairment prediction models face drawbacks like reliance on precise predictions, large datasets, complexity, poor interpretability, high computational costs, and data acquisition difficulties. This paper proposes a prediction model based on an improved Mamba algorithm. By processing financial data from listed companies in the CSMAR database, the model constructs lagged and rolling statistical features to reflect performance trends. The Mamba algorithm dynamically adjusts input parameters through a Selective State-Space Model (SSM), capturing dependencies in long-sequence data and improving prediction accuracy and timeliness. Experimental results show the Mamba algorithm excels in handling long-sequence data, offering valuable guidance for financial management and risk control.

Network statistics of the whole-brain connectome of Drosophila

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1–3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex (https://codex.flywire.ai) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Proposal of a facet-to-facet multiple flow direction algorithm based on geometrical and mathematical analysis of physical dispersion over triangle facet

Flow direction algorithms have been widely used to extract crucial terrain attributes of great hydrological and geomorphological significance. However, essential distinctions between the empirically-designed strategies of typical algorithms and the natural rules of physical dispersions bring various problems (e.g. parallel channel, artificial dispersion), leading to the low size and extent precisions of estimated results. In this work, geometrical and mathematical analysis is conducted on the inherent characteristics of physical dispersions along slope lines on local terrains. On each 3 × 3 window of digital elevation model (DEM), center pixel is divided into eight non-overlapping sub-facets. Necessary and sufficient (NS) conditions of size relationships between the elevations of adjacent pixels are summarized to directly identify the receiving facets of a sub-facet. Then, strict mathematical relations are derived between slope direction of a sub-facet and flow proportions allocated to receiving facets. A strategy is designed to re-adjust receiving facets and flow proportions for the boundary flow of adjacent facets. Lastly, a multiple-flow-direction algorithm called TFGA is proposed with the NS condition of size relationships, mathematical relation of slope direction with flow proportion, and adjustment strategy of boundary flow. Case studies are conducted for investigating the total contributing areas (TCA) and specific contributing areas (SCA) estimated by TFGA. Results reveal all-side superiorities of TFGA to typical algorithms in spatial patterns, error indicators and statistic characteristics of estimated TCAs and SCAs. Particularly, TFGA improves size precision of estimated results by approximately one order of magnitude. In a conclusion, we highly recommend TFGA for digital elevation analysis.