Local Method Research Articles

Automated geometric reconstruction and cable force inference for cable-net structures using 3D point clouds

Laser scanning provides an efficient solution to digital twin construction in civil engineering. The complexity and redundancy of large-scale point clouds substantially prolong the labor-intensive model reconstruction process. This paper presents an automated and high-precision geometric reconstruction approach for cable-net structures with a complete workflow from raw points to the extraction of cable shapes and forces. The strategy involves the extraction of coordinates of key cable regions through the characterization of cable geometry and local registration methods, followed by the computation of cable forces and shapes using segmented catenary theory and nonlinear optimization. The approach is validated with a single-curvature cable-net structure, with the accuracy of cable shapes within ±5 mm and errors in cable forces <5%. The method contributes to health monitoring and rapid reconstruction of cable-net structures. The precise geometry and forces will facilitate the creation of the mechanical models reflecting the physical authenticity of structures.

A prospective study on the usefulness of high-resolution intraoperative infrared thermography in intracranial tumors.

Infrared thermography (IT) is a non-invasive real-time imaging technique with potential application in different areas of neurosurgery. Despite technological advances in the field, intraoperative IT (IIT) has been an underestimated tool with scarce reports on its usefulness during intracranial tumor resection. We aimed to evaluate the usefulness of high-resolution IIT with static and dynamic thermographic maps for transdural lesion localization, and diagnosis, to assess the extent of resection, and the occurrence of perioperative acute ischemia. In a prospective study, 15 patients affected by intracranial tumors (six gliomas, four meningiomas, and five brain metastases) were examined with a high-resolution thermographic camera after craniotomy, after dural opening, and at the end of tumor resection. Tumors were transdurally located with 93.3% sensitivity and 100% specificity (p < 0.00001), as well as cortical arteries and veins. Gliomas were consistently hypothermic, while metastases and meningiomas exhibited highly variable thermographic maps on static (p = 0.055) and dynamic (p = 0.015) imaging. Residual tumors revealed non-specific static but characteristic dynamic thermographic maps. Ischemic injuries were significantly hypothermic (p < 0.001). High-resolution IIT is a non-invasive alternative intraoperative imaging method for lesion localization, diagnosis, assessing the extent of tumor resection, and identifying acute ischemia changes with static and dynamic thermographic maps.

THE ENERGY BALANCE METHOD COUPLED WITH THE GALERKIN METHOD FOR LOCAL FRACTIONAL NONLINEAR OSCILLATION

The method of solving the vibration frequency of local fractional nonlinear oscillators under general initial conditions is studied by coupling the energy balance method with the Galerkin method. One test case of fractional vibration is given to prove the feasibility of this method.

A Probabilistic-based Drift Correction Module for Visual Inertial SLAMs

Abstract. Positioning is a prominent field of study, notably focusing on Visual Inertial Odometry (VIO) and Simultaneous Localization and Mapping (SLAM) methods. Despite their advancements, these methods often encounter dead-reckoning errors that leads to considerable drift in estimated platform motion especially during long traverses. In such cases, the drift error is not negligible and should be rectified. Our proposed approach minimizes the drift error by correcting the estimated motion generated by any SLAM method at each epoch. Our methodology treats positioning measurements rendered by the SLAM solution as random variables formulated jointly in a multivariate distribution. In this setting, The correction of the drift becomes equivalent to finding the mode of this multivariate distribution which jointly maximizes the likelihood of a set of relevant geo-spatial priors about the platform motion and environment. Our method is integrable into any SLAM/VIO method as an correction module. Our experimental results shows the effectiveness of our approach in minimizing the drift error by 10× in long treverses.

KLSANet: Key local semantic alignment Network for few-shot image classification

Few-shot image classification involves recognizing new classes with a limited number of labeled samples. Current local descriptor-based methods, while leveraging consistent low-level features across visible and invisible classes, face challenges including redundant adjacent information, irrelevant partial representation, and limited interpretability. This paper proposes KLSANet, a few-shot image classification approach based on key local semantic alignment network, which aligns key local semantics for accurate classification. Furthermore, we introduce a key local screening module to mitigate the influence of semantically irrelevant image parts on classification. KLSANet demonstrates superior performance on three benchmark datasets (CUB, Stanford Dogs, Stanford Cars), outperforming state-of-the-art methods in 1-shot and 5-shot settings with average improvements of 3.95% and 2.56% respectively. Visualization experiments demonstrate the interpretability of KLSANet predictions. Code is available at: https://github.com/ZitZhengWang/KLSANet.

Research on Temperature Rise Characteristics Prediction of Main Shaft Dual-Rotor Rolling Bearings in Aircraft Engines

Traditional aero-engine bearings rotate simultaneously with their inner and outer rings, which makes the temperature rise prediction model computationally large with low accuracy, and it cannot be accurately verified due to the means of testing. This paper presents a method for predicting the temperature rise characteristics of aero-engine bearings under composite load conditions. Firstly, the local method is used to calculate the heat generation from heat sources such as bearing spin, lubricant drag, and the differential sliding of steel ball and collar, respectively, then finite element modelling and steady-state thermal analysis are carried out for aero-engine bearings under the simultaneous action of axial and radial external loads, a double-rotor test setup is designed and the predictive model is validated, and finally, the influences of rotational speed and load on the temperature rise characteristics of the bearings are investigated. The study shows that the aero-engine bearing prediction model proposed in this paper has high accuracy; with the increase in the rotational speed of the inner ring of the bearing, the temperatures of both the inner and outer rings of the bearing increase significantly; the temperatures of the inner and outer rings of the bearing increase with the increase in the axial load, and the effect of the radial load on the temperature of the bearing is not obvious.

SLRfinder: A method to detect candidate sex-linked regions with linkage disequilibrium clustering.

Despite their critical roles in genetic sex determination, sex chromosomes remain unknown in many non-model organisms, especially those having recently evolved sex-linked regions (SLRs). These evolutionarily young and labile sex chromosomes are important for understanding early sex chromosome evolution but are difficult to identify due to the lack of Y/W degeneration and SLRs limited to small genomic regions. Here, we present SLRfinder, a method to identify candidate SLRs using linkage disequilibrium (LD) clustering, heterozygosity and genetic divergence. SLRfinder does not rely on specific sequencing methods or a specific type of reference genome (e.g., from the homomorphic sex). In addition, the input of SLRfinder does not require phenotypic sexes, which may be unknown from population sampling, but sex information can be incorporated and is necessary to validate candidate SLRs. We tested SLRfinder using various published datasets and compared it to the local principal component analysis (PCA) method and the depth-based method Sex Assignment Through Coverage (SATC). As expected, the local PCA method could not be used to identify unknown SLRs. SATC works better on conserved sex chromosomes, whereas SLRfinder outperforms SATC in analysing labile sex chromosomes, especially when SLRs harbour inversions. Power analyses showed that SLRfinder worked better when sampling more populations that share the same SLR. If analysing one population, a relatively larger sample size (around 50) is needed for sufficient statistical power to detect significant SLR candidates, although true SLRs are likely always top-ranked. SLRfinder provides a novel and complementary approach for identifying SLRs and uncovering additional sex chromosome diversity in nature.

Leakage detection applied to the Da Hedong Reservoir dam (China) based on the flow-field fitting method

Detecting leakage in concrete gravity dams presents a formidable challenge. The flow-field fitting (FFF) method is used to identify leaks in China’s Da Hedong Reservoir dam. We develop vertical- and horizontal-gradient approaches based on the transmitted signal electrical field. A plastic frame is developed to allow the probe to measure horizontal gradients. We determine the exact location of leaks at the reservoir’s bottom and upstream face, respectively, assuming that current leakages are located in hydraulic leakage zones. Numerical experiments and water pressure tests reveal that the conductive silt layer beneath the reservoir can lower the response of the potential values at the leakage inlet, and the effect of the silt layer should be considered and not ignored when interpreting measured data. In addition, with regard to the principle of superposition, the contribution associated with metal pipes in the dam body can be roughly determined by subtracting it from the original potential field data, thereby aiding in locating leaks around the metal pipes. In addition, the FFF measurements from the reservoir’s bottom also confirm the position of existing faults in the substratum at the reservoir site. Last, the results of the FFF method are tested against supporting evidence, such as ground penetrating radar and water pressure tests. In conclusion, these independent data confirm the effectiveness of the FFF method for leakage localization in the concrete gravity dam.

Local fractional Laplace transform iterative method for solving Korteweg–de Vries equation with the local fractional derivative

The local fractional Korteweg–de Vries equations were considered in this paper. Series solutions for the linear and nonlinear case were examined using the local fractional Laplace transform iterative method. This proposed method is the coupling of the local fractional Laplace transform with the new iterative method. The existence and uniqueness of the solutions is considered using the principle of mathematical induction. Furthermore, illustrative examples were considered and the graphs are shown. The results obtained in this study reveal the benefit of this method and provide valuable insight into the behavior of complex phenomena in a precise and efficient manner with less computational work and implementation ease.

Local Modeling by Adapting Source Calibration Models to Analyte Shifted Target Domain Samples Without Reference Values.

Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets.

An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling.

Sea surface temperature (SST), with its complex and dynamic behavior, is a major driver of ocean-atmosphere interactions. The purpose of this study is to investigate the behavior of SST and its prediction using a chaotic approach. Average mutual information (AMI) and Cao methods were used to reconstruct the phase space. The Lyapunov exponent and correlation dimension were used to investigate chaos. The Lyapunov exponent index was used to predict SST with a 5-year average prediction horizon using the local prediction method between 2023 and 2027. The results showed a 3-month delay time for the Pacific and Antarctic Oceans, and a 2-month delay time for the Atlantic, Indian, and Arctic Oceans. The optimal embedding dimension for all oceans is between 6 and 7. Our analysis reveals that the dynamics of SST in all oceans exhibit varying degrees of chaos, as indicated by the correlation dimension. The local prediction method achieves relatively accurate short-term SST predictions due to the clustering of SST points around specific attractors in the phase space. However, in the long term, the accuracy of this method decreases as the points in the phase space of SST can spread randomly. The model performance ranking with a Percent Mean Relative Absolute Error shows that the Indian Ocean has the best performance compared to other oceans, while the Atlantic, Pacific, and Antarctic and Arctic Oceans are in the next ranks. This study contributes to understanding the dynamics of SST and has practical value for use in the development of climate models.

Improved acoustic source localization method for crack identification in structures

Acoustic Emission (AE) monitoring is considered one of the popular non-destructive testing (NDT) methodologies that have been used to predict and identify the location of damage in critical civil infrastructure. In this paper, an improved AE crack localization method is proposed by integrating the empirical mode decomposition (EMD)-based signal decomposition method with a source localization model. Unlike the conventional AE method, the proposed method is free of the use of bulk AE parameters such as counts, rise time, signal strength, and energy. First, EMD is used to minimize the presence of noise in the recorded AE waveforms and extract the key AE components. Then, key AE events are located using the source localization model to localize the crack in concrete structures. The performance of the proposed method is validated experimentally on small and large-scale concrete beams, where the damage is induced using progressive static load testing. In particular, the large-scale beams are designed for flexural and shear mode failure to evaluate the performance of the proposed method under various types of damage. Finally, the results of the proposed method are compared with the traditional method that uses raw AE waveforms and the method that uses bandpass-filtered AE waveforms. The results show higher crack location accuracy of the proposed method than the other methods, which makes it a suitable approach as a crack localization technology.

Unsupervised spike sorting for multielectrode arrays based on spike shape features and location methods

Unsupervised spike sorting for multielectrode arrays based on spike shape features and location methods

TRANSLATION STRATEGIES IN ENGLISH-UKRAINIAN RENDERING OF DUAL-NUMBER QUANTIFI

The article deals with translation strategies implemented in English-Ukrainian rendering of dualnumber quantifiers ‘both,’ ‘either,’ ‘neither’ that occur in modern fantasy fiction. The study aims to identify local and global translation strategies and methods of their adequate rendering at the post-translation stage of generalizing the translator’s experience. The research objectives are: 1) to compile a complete register of the lexemes under investigation based on J.K. Rowling’s “Harry Potter and the Prisoner of Azkaban” and its authorized Ukrainian translation produced by V. Morozov.; 2) to determine their semantic and functional characteristics and direct translation equivalents; 3) to identify their semantic roles and syntactic functions in the source text; 4) to specify the types of grammatical shifts and methods of their rendering into Ukrainian; 5) to identify and analyze the translation strategies applied by the translator to reach equivalence and adequacy of translation. The research material comprises 96 dual-number quantifiers used in the original text and their translations in the corresponding Ukrainian text fragments. In the course of the study, the following methods were employed: 1) entire sampling to compile a register of the lexemes and accomplish the source and target text levelling; 2) dictionary entries’ analysis to establish the lexical-semantic and functional properties of the quantifiers and their direct Ukrainian translation equivalents; 3) contextual analysis and immediate constituents’ analysis to identify their semantic roles and syntactic functions in the source text; 4) comparative-contrastive translation transformational analysis to specify the translation methods and types of grammatical shifts applied in their rendering; 5) to identify and analyze the translation strategies implemented by the translator; 6) quantity calculations to determine the frequencies of the analyzed phenomena. As a result, based on dictionary entries’ analysis, the main functional-semantic classes of the quantifiers have been found to be the determiner, pronoun, conjunction and pronominal adverb. By means of immediate constituents’ and contextual analysis, 17 pure and blended semantic roles of the lexemes’ referents in ‘state of affairs’ situations have been identified, the most frequent being the agent (36.4%), locative (14.6%) and experiencer (11.4%). By way of sentence parsing, 19 combinations of the semantic roles and syntactic functions have been established, the most frequent one being the agent subject (37.5%), the others showing frequencies around 10%. With the aid of translation transformational analysis, the most common methods of rendering the dual-number quantifiers into Ukrainian have been found to be omission (62.5%), equivalent (literal) translation (21.9%) and lexical substitution (15.6%). Correspondingly, the main translation strategies, including ST-oriented and TToriented ones, have been proved to be 1) communicative translation (68.8%) by omission with emphasis decrease, related and unrelated-word paraphrase, clause/sentence structure change and explicitness decrease, and by equivalent with emphasis change and clause/sentence structure change and/or addition; 2) literal translation (15.6%) including direct translation (4.2%) by pure equivalent and oblique translation (11.4%) by blended equivalent. i.e. with emphasis change and/or clause/sentence change; 3) semantic translation (15.6%) involving synonymy, emphasis change, phrase structure change, modulation, clause/ sentence structure change and explicitness decrease, 4) domestication (100%) incorporated in the above three strategies, all applied to ensure equivalence and adequacy of the translation.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

An EEG channel selection method for motor imagery based on Fisher score and local optimization

Objective. Multi-channel electroencephalogram (EEG) technology in brain–computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness. Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection. Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%. Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.

First-line therapy for metastatic uveal melanoma: Experience from a single reference center.

e21514 Background: Metastatic uveal melanoma (mUM) continues to have a poor prognosis with distinct biology from cutaneous melanoma and limited effective treatment options. The main options include immunotherapy (IT, anti-PD-1+/- anti-CTLA-4, tebentafusp), chemotherapy, targeted therapy and local treatment methods for patients with isolated liver metastasis (isolated hepatic perfusion, transarterial chemoembolization, percutaneous hepatic perfusion, radiofrequency ablation). Methods: We conducted a single-center retrospective observation IRB-approved study of first-line therapy for pts age 18 and older with mUM treated at the N.N. Blokhin National Medical Research Center of Oncology from 2020 to 2023. Our objectives were to evaluate clinical outcomes in real-world settings, as well as treatment patterns, disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and adverse events (AEs). Results: A total of 125 pts included. The mean age was 54 yrs (range: 20-74), and the median time to metastasis from primary tumor was 2 years (range: 0-21). Liver metastases were diagnosed in 92% of patients, lung metastases in 20%, bone metastases in 17%, and soft tissue metastases in 9%. Conclusions: Our data suggest that the combination of systemic and local liver-directed treatment methods may be associated with a survival benefit and higher treatment response to IT in patients with mUM. [Table: see text]

UGEE-Net: Uncertainty-guided and edge-enhanced network for image splicing localization

Image splicing, a prevalent method for image tampering, has significantly undermined image authenticity. Existing methods for Image Splicing Localization (ISL) struggle with challenges like limited accuracy and subpar performance when dealing with imperceptible tampering and multiple tampered regions. We introduce an Uncertainty-Guided and Edge-Enhanced Network (UGEE-Net) for ISL to tackle these issues. UGEE-Net consists of two core tasks: uncertainty guidance and edge enhancement. We employ Bayesian learning to model uncertainty maps of tampered regions, directing the model's focus to challenging pixels. Simultaneously, we employ a frequency domain-auxiliary edge enhancement strategy to imbue localization features with global contour information and fine-grained local details. These mechanisms work in parallel, synergistically boosting performance. Additionally, we introduce a cross-level fusion and propagation mechanism that effectively utilizes contextual information for cross-layer feature integration and leverages channel-level correlations for cross-layer feature propagation, gradually enhancing the localization feature's details. Experiment results affirm UGEE-Net's superiority in terms of detection accuracy, robustness, and generalization capabilities. Furthermore, to meet the growing demand for high-quality datasets in image forensics, we present the HTSI12K dataset, which includes 12,000 spliced images with imperceptible tampering traces and diverse categories, rendering it suitable for real-world auxiliary model training.

Improving Acoustic Methods of Pipeline Leak Location with Distributed Sensing

Improving Acoustic Methods of Pipeline Leak Location with Distributed Sensing

Vibration analysis and experimental study of three-dimensional orthogonally coupled plates with through edge cracks

A modeling method for transverse vibration of three-dimensional (3D) orthogonally coupled plates is proposed and the through cracks located at coupling edges are considered. Corresponding experimental tests are performed to validate the effectiveness of this method. The main conflict for modelling 3D orthogonally coupled plates lies in the conflict of local coordinates of neighboring plates at the orthogonally coupled edge. The resolution of this conflict is effectively achieved by employing local collocation points method, this method is used to derive a universal expression for the coupling of plates under any coordinate system. Several cases were analyzed to compare natural frequencies, modal shapes, and dynamic responses against reference and experimental findings. Consequently, the present results are found to closely align with the measured outcomes, confirming the convergence and correctness of the proposed model and its solutions. The effects of crack parameters, such as length and number, on the vibration characteristics of orthogonally coupled plate structures are investigated. It is found that the vibrational characteristics of the coupled plates are more sensitive to the presence and variation of cracks at high frequencies. In the case of this study, as the number of cracks increases, response peaks appear at lower frequencies, with the maximum peak differing by up to twice. These findings are shown to have significant practical implications for understanding and predicting the effects of cracks in real orthogonally coupled plate structures. The effectiveness and convenience of the modeling approach for three-dimensional orthogonally coupled plates with through cracks are demonstrated in this study.