This study investigates the effectiveness of two Finite Impulse Response (FIR) filter designs based on the Hanning and Blackman-Harris windows for preprocessing electroencephalography (EEG) signals collected from both neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). EEG signals were recorded using a 16-channel setup and band-pass filtered between 0.5 and 40 Hz to isolate relevant neural activity. Subsequently, the signals were processed independently using each FIR filter type. Performance evaluation was conducted using four quantitative metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Power Spectral Density (PSD). The Hanning window filter showed MAE values ranging from 0.079 to 0.325, MSE from 0.026 to 0.177, SNR between 7.56 and 15.86 dB, and PSD values from 5.3 to 9.08 × 10⁻³. These results demonstrate good noise attenuation while preserving signal morphology. In contrast, the Blackman-Harris window produced higher MAE (0.061–0.318) and MSE (0.019–0.172) but achieved significantly greater SNR improvements (7.77–17.4 dB) and tighter control over PSD (4.904 – 8.442 × 10⁻³), indicating superior noise suppression and reduced spectral leakage. A paired t-test confirmed that differences in all four performance metrics were statistically significant (p < 0.05) across both neurotypical and ASD subject groups. Despite the Hanning filter's computational simplicity, the Blackman-Harris filter demonstrated more robust performance, making it a more suitable choice for high-fidelity EEG signal analysis in clinical diagnostics and neuroscience research.

This study aimed to conduct a systematic review (SR) and meta-analysis (MA) of the author’s empirical studies published between 2021 and 2025 on Arabic–English transliteration of personal names on Facebook and public signages (shop names and linguistic landscapes). It aimed to synthesize evidence on orthographic anomalies, error patterns, and variation in English to Arabic and Arabic to English transliteration across social media, shop names, and linguistic landscapes. The fourteen studies share a unified methodological framework and provide quantitative data (percentages, frequencies, and error rates) that allow for statistical aggregation. The fourteen studies were categorized into 3 clusters: Shop names and linguistic landscapes, personal names, borrowed nouns, and ai generated transliteration. Results of the SR/MA revealed consistent patterns of inaccuracy and variation across human Arabic–English transliteration. In public signage, recurrent issues include vowel omission, inconsistent representation of consonants with no direct English equivalents, semantic and syntactic anomalies in compound names, and wide divergence from standard spellings. Personal names show similarly unstable patterns, with multiple transliterations for the same name, inconsistent rendering of the glottal stop and pharyngeal fricatives, variable spelling of the definite article /al /, and frequent gemination errors. Borrowed English nouns display phonological adaptation patterns shaped by Arabic orthography, especially in the representation of /g/ and other non native phonemes by Artificial Intelligence. Meta analytic pooling across studies shows high overall error rates, cross context variation, and tendencies toward under representation of vowels and over regularization of consonants. Subgroup analyses indicate that transliteration accuracy varies by domain, with signage showing the highest error density and personal names the greatest internal variability. Together, the findings demonstrate that human transliteration is shaped by sociolinguistic preference, orthographic habit, and contextual constraints rather than by standardized rules, establishing a coherent empirical profile of real world Arabic–English transliteration behavior. These results offer the first coherent map of human transliteration behavior and lay the groundwork for future research.

Seven-Year Clinical Outcomes and Optical Quality of Implantable Collamer Lens Implantation Versus KLEx for Myopia Correction.

Effectiveness and Safety of Cross-Linking in Keratoconus Patients With Corneal Thickness <400 µm: A Systematic Review and Meta-analysis.

The paper analyzes the influence of phase noise on the operation parameters of cognitive radio network synchronization systems against the background of instability of the power supply voltage of reference generators. In the first part of the study, the influence of the errors of the phase synchronization device and the clock synchronization device on the reliability of the received message was assessed. The dependences of the probability of error of coherent reception of opposite binary signals in the presence of phase and clock synchronization errors were obtained by numerical methods. In the second part, a simulation model was developed to assess the influence of phase noise on the parameters of the synchronization systems of cognitive radio networks against the background of instability of the supply voltage of the reference generators. The basis was taken as a linear model of the phase loop (PLL) with a PI filter (proportional-integral filter). The novelty of the study lies in the development of a simulation model of the phase loop with a PI filter, which allows to assess the influence of phase noise and instability of the supply voltage of the reference generators on the synchronization parameters. The dependences of the spectral density of the phase error and the transfer function from the supply noise to the phase error were obtained, which allows to quantitatively assess the mechanism of penetration of the supply instability into the frequency characteristics of the PLL. A numerical comparison of the influence of PI filter parameters (Tᵢ, Kf) on the quality of PLL operation and the system's resistance to power supply noise was carried out, which deepens the methodology for optimizing synchronizer parameters. Practical value: the developed model allows predicting the stability of synchronization systems of cognitive radio networks in real conditions, when reference oscillators are affected by power supply instability. The results obtained can be used in the design of PLL structures for cognitive receivers, where it is critically important to maintain reception coherence under the influence of internal noise and interference. The determined criteria (spectral density, RMS phase error) can be used as reliability and quality metrics for comparing different architectures of synchronization systems. In the third part, a methodology for assessing the influence of voltage stabilizer accuracy on the phase stability of the synchronization system is developed. Scientific novelty of the developed methodology: a new approach to quantitatively assessing the influence of power supply stabilizer instability on the phase stability of synchronization systems is proposed; A MATLAB model has been developed that allows for virtual experiments and the determination of acceptable limits for power supply instability, taking into account specific PLL and VCO parameters.

This review provides an updated perspective on the transformative role of computational simulation in the design and discovery of binary alloys for advanced technologies. Unlike traditional trial and error methods, molecular dynamics (MD) and density functional theory (DFT) simulations now deliver atomistic insights into structure property relationships, enabling more predictive materials design. Recent developments demonstrate that hybrid strategies integrating DFT, MD, machine learning (ML), and multiscale modeling are accelerating the discovery of high performance alloys. The article emphasizes the novelty of simulation-driven design frameworks while identifying critical research challenges, including scalability, force-field accuracy, and the integration of simulation with digital twin concepts. Through selected case studies ranging from semiconductors and biocompatible biomedical alloys to energy materials and emerging 2D binary systems this review argues that computational simulation is shifting from a supplementary role to a central driver of innovation in modern materials science

As a dominant phenomenon in Chinese senior high school English teaching, exam-driven input revolves around meeting standardized test requirements. It structures teaching around high-frequency test points and error-prone areas, rooted in the stimulus-response reinforcement of behaviorism. While it may temporarily boost exam scores, it causes a severe disconnect between test-oriented grammar performance and real-world application. For example, students with 140+ in Gaokao English struggle to write error-free emails, and 62% fail to use the subjunctive mood correctly in writing despite specialized training.This study uses a mixed-methods approach: quantitative analysis of 120 Gaokao mock exam writing samples (from high-intensity exam-training urban School C and low-intensity rural School M) and qualitative interviews with 16 teachers and students. Quantitative results show urban-rural differences: School C has lower grammar error density (mostly below 10% in practical writing) but narrower error types and repeated errors; School M has higher error density (often over 10%) with scattered errors. Qualitative interviews confirm exam-driven input limits students to fragmented knowledge, hindering grammar rule internalization.Theoretically, three frameworks explain the negative effects: compared to Krashen’s "comprehensible input" (i + 1, emphasizing meaning/context), exam-driven input is mechanical and decontextualized; Sweller’s Cognitive Load Theory shows exam training overloads working memory with external load (e.g., memorizing test skills), reducing deep semantic processing; de Bot’s Dynamic Systems Theory reveals exam input disrupts synergy between grammar, vocabulary, and pragmatics, causing non-linear grammar stagnation.In conclusion, exam-driven input distorts grammar acquisition, leading to fragmented knowledge and a score-competence gap. Short-term reforms include task-based teaching for contextualized input; long-term reforms involve optimizing assessment (e.g., increasing contextualized discourse tasks in Gaokao) and balancing educational resources to shift from "learning for exams" to "learning for application."

Autism Spectrum Disorder (ASD) diagnosis can greatly benefit from more efficient and accurate tools to enable early intervention and reduce long-term healthcare costs associated with delayed diagnosis. Electroencephalography (EEG) has emerged as a promising non-invasive technique for detecting neural patterns linked to ASD. This research evaluates the effectiveness of three preprocessing techniques, Butterworth, Discrete Wavelet Transform (DWT), and Independent Component Analysis (ICA), in enhancing EEG signal quality for ASD classification. The performance of each method is assessed using Signal-to-Noise Ratio (SNR), Mean Absolute Error (MAE), Mean Squared Error (MSE), Spectral Entropy (SE), and Power Spectral Density (PSD) analysis to explore frequency band distribution. Additionally, Hjorth parameters—activity, mobility, and complexity—are computed to capture neural dynamics associated with ASD. Results showed that ICA achieved the highest SNR values (normal: 86.44, ASD: 78.69), indicating superior denoising capability, while DWT offered the lowest error metrics (MAE: 4785.08, MSE: 309,690 for ASD), demonstrating its robustness in preserving signal characteristics. Butterworth provided moderate results across metrics. Notably, Hjorth parameters revealed that neurotypical EEGs exhibited higher activity and complexity, highlighting distinct neural dynamics compared to ASD. These findings suggest that ICA is optimal for applications prioritizing signal clarity, while DWT offers a balanced approach for feature preservation in ASD EEG analysis. These findings are expected to support the development of more accurate, EEG-based diagnostic tools for ASD that can be integrated into clinical decision support systems and early screening programs.

In linear regression models with non-Gaussian errors, transformations of the response variable are widely used in a broad range of applications. Motivated by various genetic association studies, transformation methods for hypothesis testing have received substantial interest. In recent years, the rise of biobank-scale genetic studies, which feature a vast number of participants that could be around half a million, spurred the need for new transformation methods that are both powerful for detecting weak genetic signals and computationally efficient for large-scale data. In this work, we propose a novel transformation method that leverages the information of the error density. This transformation leads to locally most powerful tests and therefore has strong power for detecting weak signals. To make the computation scalable to biobank-scale studies, we harnessed the nature of weak genetic signals and proposed a consistent and computationally efficient estimator of the transformation function. Through extensive simulations and a gene-based analysis of spirometry traits from the UK Biobank, we validate that our approach maintains stringent control over Type I error rates and significantly enhances statistical power over existing methods.

Rescaled three-mode principal component analysis: An approach to subspace recovery.

Modeling the sparsity and density of measurement errors for origin–destination demand estimation

Purpose This study aims to solve the problem of high-precision kinematic calibration of manipulators. Kinematic calibration is an effective means to improve the absolute positioning accuracy of manipulators. However, the calibration accuracy of traditional methods still has limitations under several working conditions. To overcome this problem, a hybrid approach of calibration combining kinematic model and convolutional neural network is proposed in this paper to improve the calibration accuracy of a manipulator. Design/methodology/approach A hybrid approach of calibration combining a kinematic model and a convolutional neural network is proposed in this paper to improve the calibration accuracy of a manipulator. Specifically, as the first step, a sequential quadratic programming-based kinematic calibration process is carried out to primarily identify the geometric parameter errors. On the basis of this identification, a hybrid approach of calibration based on a convolutional neural network (CNN) is proposed. Afterward, the kinematic calibration integrated CNN approach is adopted for comprehensive compensation of both geometric and non-geometric parameter errors. Findings The performance of the proposed method is experimentally verified and compared with nine benchmarked methods, demonstrating a relatively high calibration accuracy. Meanwhile, several key issues are discussed, including the generalization capabilities of our proposed method, the probability density of the position error as well as the influence of the input format of the CNN model. Originality/value A hybrid calibration method combining kinematic modeling and neural networks is proposed, which is capable of fully compensating geometric and non-geometric parameter errors.

This study presents a critical evaluation of image-based 3D reconstruction techniques for small archaeological artifacts, focusing on a quantitative comparison between Neural Radiance Fields (NeRF), its recent Gaussian Splatting (GS) variant, and traditional Structure-from-Motion (SfM) photogrammetry. The research targets artifacts smaller than 5 cm, characterized by complex geometries and reflective surfaces that pose challenges for conventional recording methods. To address the limitations of traditional methods without resorting to the high costs associated with laser scanning, this study explores NeRF and GS as cost-effective and efficient alternatives. A comprehensive experimental framework was established, incorporating ground-truth data obtained using a metrological articulated arm and a rigorous quantitative evaluation based on root mean square (RMS) error, Chamfer distance, and point cloud density. The results indicate that while NeRF outperforms GS in terms of geometric fidelity, both techniques still exhibit lower accuracy compared to SfM, particularly in preserving fine geometric details. Nonetheless, NeRF demonstrates strong potential for rapid, high-quality 3D documentation suitable for visualization and dissemination purposes in cultural heritage. These findings highlight both the current capabilities and limitations of neural rendering techniques for archaeological documentation and suggest promising future research directions combining AI-based models with traditional photogrammetric pipelines.

Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5θ2) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency.

Abstract Inaccurate stellar atmospheric refraction models impair the attainable accuracy of the refraction-based celestial navigation, leading to performance degradation in Strapdown Inertial Navigation System/Refractive Celestial Navigation System (SINS/RCNS) integrated navigation. This paper proposes a new SINS/RCNS integrated navigation algorithm aided by dual-band starlight refraction observations for near-Earth flight vehicles. In an analogy with the dual-frequency error correction in GNSS positioning, this algorithm exploits dual-band starlight measurements to estimate the atmospheric density error. Then, the measurement equation is established between the dual-band refraction angles and both position error and atmospheric density error. Moreover, the Extended Kalman Filter (EKF) is utilized to estimate the atmospheric density error online, which is then used for navigation error compensation. The simulation results indicate that the proposed algorithm can effectively mitigate navigation accuracy degradation by online correction for the atmospheric density error, enhancing integrated navigation performance for near-Earth flight applications.

Quantile regression has become increasingly popular across various disciplines due to its robustness, offering an alternative to traditional mean-based regression. Unlike traditional linear regression, quantile regression estimates conditional quantiles, capturing the full complexity of the relationships between variables (specifically, the conditional dependence of lifetime on covariates in lifetime analysis). However, inference procedures in quantile regression often involve complex non-parametric methods, as the variance of an estimator typically depends on the unknown error density, making it difficult to estimate. In this paper, we present a bootstrap-type resampling method that simplifies the construction of the inference procedures using the censored median regression estimator originally proposed by Yang (1999). Numerical simulations are performed to validate the proposed procedures.

Not a day goes by without hearing about the impressive feats of large language models (LLMs), and equally, not a day passes without hearing about their challenges. LLMs are notoriously vulnerable to biases in their dataset, leading to issues such as toxicity, harmful responses, and factual inaccuracies. While domain-adaptive training has been employed to mitigate these issues, these techniques often address all model parameters indiscriminately during the repair process, resulting in poor repair quality and reduced model versatility. In this paper, drawing inspiration from fault localization via program slicing, we introduce a novel dynamic slicing-based intent-aware LLM repair strategy, IRepair. This approach selectively targets the most error-prone sections of the model for repair. Specifically, we propose dynamically slicing the model’s most sensitive layers that require immediate attention, concentrating repair efforts on those areas. This method enables more effective repairs with potentially less impact on the model’s overall versatility by altering a smaller portion of the model. Furthermore, dynamic selection allows for a more nuanced and precise model repair compared to a fixed selection strategy. We evaluated our technique on three models from the GPT2 and GPT-Neo families, with parameters ranging from 800M to 1.6B, in a toxicity mitigation setup. Our results show that IRepair repairs errors 43.6% more effectively while causing 46% less disruption to general performance compared to the closest baseline, direct preference optimization. Our empirical analysis also reveals that errors are more concentrated in a smaller section of the model, with the top 20% of layers exhibiting 773% more error density than the remaining 80%. This highlights the need for selective repair. Additionally, we demonstrate that a dynamic selection approach is essential for addressing errors dispersed throughout the model, ensuring a robust and efficient repair.

Our goal is to develop a Bayesian model averaging technique in linear regression models that accommodates heavier tailed error densities than the normal distribution. Motivated by the use of the Huber loss function in the presence of outliers, the Bayesian Huberized lasso with hyperbolic errors has been proposed and recently implemented in the literature. Since the Huberized lasso cannot enforce regression coefficients to be exactly zero, we propose a Bayesian variable selection approach with spike and slab priors to address sparsity more effectively. The shapes of the hyperbolic and the Student-t density functions differ. Furthermore, the tails of a hyperbolic distribution are less heavy compared to those of a Cauchy distribution. Thus, we propose a flexible regression model with an error distribution encompassing both the hyperbolic and the Student-t family of distributions, with an unknown tail heaviness parameter, that is estimated based on the data. It is known that the limiting form of both the hyperbolic and the Student-t distributions is a normal distribution. We develop an efficient Gibbs sampler for posterior computation. Through simulation studies and analyzes of real datasets, we show that our method is competitive with various state-of-the-art methods.

Earth materials are commonly utilized due to their excellent wet properties and environmental friendliness. However, previous research has primarily focused on the impact of additives on the water vapor permeability of earth materials, neglecting the influence of particle size distribution. This has also hindered the quantitative assessment of the water vapor permeability of earth materials. To advance the use of earth materials in building energy conservation, this study develops a mathematical model for the water vapor permeability coefficient of earth materials. This model is derived from experiments that measure the water vapor permeability coefficient of earth materials with varying densities and earth-to-sand ratios, employing both experimental measurements and theoretical analyses. After being adjusted by a quadratic function of error rate and density, the average error rate of the mathematical model decreased from 5.73% to 1.3%, indicating its accuracy. Furthermore, by utilizing this model, the impacts of density, clay, sand, and gravel on the water vapor permeability coefficient of earth materials were quantitatively examined. The results indicate a negative correlation between the water vapor permeability coefficient of earth materials and density. When the clay–sand–gravel ratio was 3.8:5.0:1.2, the vapor permeability of the earth materials was the worst, whereas when the gradation ratio was 4.6:3.4:2.0, the vapor permeability was relatively optimal. The findings of this research can provide a reference for the scientific quantification of the thermo-physical property indices of earth materials in green building design systems.

This article studies the nonparametric estimation and uniform inference for the conditional quantile regression function (CQRF) with covariates exposed to measurement errors. We consider the case that the distribution of the measurement error is unknown and allowed to be either ordinary or super smooth. We estimate the density of the measurement error by the repeated measurements and propose the deconvolution kernel estimator for the CQRF. We derive the uniform Bahadur representation of the proposed estimator and construct the uniform confidence bands for the CQRF, uniformly in the sense for all covariates and a set of quantile indices, and establish the theoretical validity of the proposed inference. A data-driven approach for selecting the tuning parameter is also included. Monte Carlo simulations and a real data application demonstrate the usefulness of the proposed method.