Frequency Map Research Articles

National estimation of regulated water storage of reservoirs in China

The need for man-made reservoirs has increased with the growing population and changing climate. One of the most intensive human-induced alterations of the hydrological cycle is the regulated water storage in reservoirs, however, its quantification in large-scale reservoirs in China is inadequate. The lack of such information limits the rational management and utilization of water resources in reservoirs. To address this issue, we first mapped the dense time series of high-resolution (10 m) water inundation areas based on Sentinel-2 image scenes from 2017 to 2022 and then composed the annual surface water frequency (WF) maps of Chinese reservoirs. We calculated the water area of each reservoir at different WFs and estimated the corresponding water storage using our previously developed machine learning model. The water storage difference between the maximum water area (WF ≥ 5 %) and year-long (WF ≥ 75 %) water area was assumed as the regulated water storage of reservoirs. Results show that the multiyear-averaged water storage of all Chinese reservoirs at the maximum water area is estimated at 784.60 km3 from 2017 to 2022, and the water storage at the year-long water area is approximately 318.86 km3. The average annual regulated water storage of the reservoirs is estimated at 465.74 km3, accounting for 43.72 % of the total designed water storage of reservoirs in China. Among the basins, the Yangtze River Basin and Pearl River Basin have the highest average annual regulated water storage of 152.52 and 84.94 km3, respectively. The basins with larger irrigation areas and higher populations tend to have higher reservoir regulated water storage. The normalized water storage (NS) shows high spatial variations across reservoirs in different basins, with most basins in the south having higher NS than those in the north.

Novel approach of slow pathway ablation in common atrioventricular nodal reentrant tachycardia by peak frequency map

Abstract Background It is difficult to visualize slow pathway in atrioventricular nodal reentrant tachycardia (AVNRT) even with recent high-density mapping. This could be because the slow pathway potential is buried by the potential of atrial working cells during sinus rhythm and tachycardia. In other words, it is extremely difficult for conventional 3D mapping systems to annotate slow pathway potential even though the histological run of slow pathway is known .[1], [2] Purpose The purpose of this study is to visualize the atrial myocardial junction of rightward inferior extension by the peak frequency map of Ensite X in common AVNRT and to evaluate whether relative high frequency (HF) sites in low voltage area extending from Koch's triangle to tricuspid annulus (TA) is effective targets of slow pathway ablation and the characteristics of junctional rhythm (JR) after radiofrequency application. Methods This study is single-center prospective cohort study. In 37 consecutive common AVNRT patients, novel approach and conventional approach of slow pathway ablation were performed in 17 and 20 patients, respectively. The conventional approach is anatomical and electrophysiological approach. Peak frequency map was performed during sinus rhythm with HD-grid catheter in novel approach patients. A frequency scanning is performed to locate relative HF sites on the tricuspid annulus. Radiofrequencey application is 30 W, 30-60 sec, and the end point was no inducibility or until one echo under load of isoproterenol. Results The success rate was 100 % with two approaches. The relative HF sites in low voltage area were distributed at the TA side from 4 to 5 o'clock in all novel approach patients, mean peak frequency in success site was 419 ± 87 Hz. The distance to His bundle from success site was farther (24.8 ± 4.9 vs. 12.8 ± 4.4 mm, p<0.001), JR was slower (93 ± 14 vs. 118 ± 10 bpm, p=0.013), time to JR occurrence was shorter (3.0 ± 1.3 vs. 7.6 ± 4.6 sec, p=0.003), and elimination rate of jump up (78 % vs. 40 %, p=0.06) was higher in novel approach. Conclusions The relative HF sites in low voltage area at TA side from 4 to 5 o'clock by peak frequency map could be novel target of slow pathway ablation with high success rate and sufficient safety and efficiency.peak frequency map

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.

Efficacy of the peak frequency map for left atrial posterior wall isolation.

Complete isolation of the left atrial posterior wall (LAPW) is challenging owing to overlapping epicardial conduction. Peak frequency (PF) is a novel parameter that focuses on near- and far-field electrogram components. In this study, we aimed to determine whether transmural block completion is related to the ablation site PF. We analyzed 44 consecutive patients undergoing initial LAPW isolation (LAPWI) between June 2023 and February 2024. Pre-LAPWI and PF maps were obtained using an HD-Grid catheter. In 17 of the 44 patients (Group-1), LAPWI was performed conventionally. In the remaining 27 patients (Group-2), PF-guided LAPWI was applied based on Group-1 results. In Group-1, the left atrial (LA) roof and floor line success rates were 58.8% and 76.5%, respectively. The average PF value in the roof lines of patients with a completed block line was significantly higher than that of the remaining patients (286.6 ± 29.3 Hz vs. 236.1 ± 40.9 Hz, p = .012), but the floor lines were similar. In Group-2, relatively high PF values were targeted to achieve complete block of the LA roof and floor lines, following the results obtained in Group-1. The LA roof line success rates (92.6% vs. 58.8%, p = .007), total number of radiofrequency (RF) applications (22.7 ± 5.6 vs. 27.6 ± 8.5, p = .03), and procedural time (32.6 ± 18.3 vs. 47.9 ± 25.6 min, p = .03) differed between Group-1 and Group-2. Using a PF map for determining the optimal ablation line for LAPWI by RF catheter ablation is feasible.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Peak frequency mapping in Brugada Syndrome.

Peak frequency mapping in Brugada Syndrome.

Lightweight Bearing Fault Diagnosis Method Based on Improved Residual Network

A lightweight bearing fault detection approach based on an improved residual network is presented to solve the shortcomings of previous fault diagnostic methods, such as inadequate feature extraction and an excessive computational cost due to high model complexity. First, the raw data are turned into a time–frequency map using the continuous wavelet transform, which captures all of the signal’s time- and frequency-domain properties. Second, an improved residual network model was built, which incorporates the criss-cross attention mechanism and depth-separable convolution into the residual network structure to realize the important distinction of the extracted features and reduce computational resources while ensuring diagnostic accuracy; simultaneously, the Meta-Acon activation function was introduced to improve the network’s self-adaptive characterization ability. The study findings indicate that the suggested approach had a 99.95% accuracy rate and a floating point computational complexity of 0.53 GF. Compared with other networks, it had greater fault detection accuracy and stronger generalization ability, and it could perform high-precision fault diagnostic jobs due to its lower complexity.

Study on the detection of power line operation anomalies in the Hexi Corridor based on multi-scale filtering of BeiDou satellite signals

In the artificial intelligence detection of power line anomalies, the identification effect is determined by the positioning process of two-dimensional map, which is also a difficult point in this field Therefore, this paper proposes a method to detect the abnormal operation of power lines in Hexi Corridor based on multi-scale filtering of Beidou satellite signals. Multi-scale processing is carried out by wavelet transform to obtain multiple wavelet basis functions of different scales. On this basis, a two-dimensional time–frequency diagram is drawn according to the results of continuous wavelet transform. The adaptive multi-time frequency entropy method is selected to divide the two-dimensional time–frequency map into multiple layers and scales, and the adaptive multi-scale time–frequency entropy of the time–frequency plane is calculated. After smoothing the obtained power line position signal by S-G filtering method, the power line position signal of Hexi Corridor is obtained by GPS/ Beidou dual-mode satellite positioning system, and the positioning signal is optimized by using the positioning mechanism of multiple receiving stations. Realize the abnormal operation detection of power lines in Hexi Corridor. The experimental results show that this method can obtain more accurate positioning signals, and the geometric accuracy factor GDOP fluctuates in the range of 0 ∼ 2. The unsmooth signal can be effectively removed; The maximum error result of abnormal position detection is 1.22kn；; At the same time, the abnormal displacement section of the line can be determined.

Reversible data hiding in JPEG images based on improved frequency selection and mapping strategy

JPEG images have become ubiquitous in our daily lives due to their ability to strike a balance between visual quality and file size. Consequently, reversible data hiding (RDH) schemes for JPEG images have emerged. However, existing methods by modifying discrete cosine transform (DCT) coefficients still have room for improvement in terms of distortion reduction and file size preservation. This paper proposes a novel two-dimensional (2D) histogram mapping strategy for RDH in JPEG images. Firstly, the quantized DCT coefficient blocks are sorted based on the count of zero alternating current (AC) coefficients in each block. This enables the estimation of distortion in non-zero AC coefficients at different locations within the block, facilitating the selection of frequency coefficients with minimal distortion for embedding. Additionally, leveraging statistical characteristics, six mapping types with different embedding efficiency are created and the improved 2D histogram mapping method is proposed. Furthermore, The unit distortion-increase ratio (UDIR) is employed as a comprehensive evaluation metric. Extensive experiments are conducted on four representative images and two widely-used image databases to validate the proposed scheme. The results consistently demonstrate superior visual quality, minimal file size increase (FSI), and higher UDIR in comparison to state-of-the-art RDH schemes for JPEG images.

PEMANFAATAN DATA HUJAN BERBASIS SATELIT CHIRPS UNTUK PEMETAAN HUJAN RANCANGAN DI KABUPATEN LOMBOK UTARA

The design rainfall value is a critical parameter in flood analysis and water structure design. In general, the design rainfall calculation uses measured rainfall data. However, sometimes it is difficult to find complete rainfall data from evenly distributed rainfall stations and in an extended measurement range. This study aims to test the use of CHIRPS satellite-based rainfall data as an alternative solution for providing data in areas with difficulty with rainfall data. The analysis includes data accuracy and calibration evaluation, followed by design rainfall analysis with frequency analysis and mapping using spatial data processing software. The accuracy test results obtained an average deviation value of the original CHIRPS that did not meet the average NSE value of 0.346. Furthermore, the data was calibrated using the regression method, and corrected data was obtained with an NSE of 0.446 with an interpretation of the meeting. A rainfall analysis was conducted for corrected CHIRPS with ARR (Automatic Rainfall Recorder) measurement as a control. The results obtained the average percentage deviation for design rainfall with a return period of 2, 5, 10, 25, 50, and 100 years using corrected CHIRPS ranging from 18 - 28% lower than design rainfall using ARR. The design rainfall isohyet map using spatial data processing software for the North Lombok Region shows that high design rainfall occurs in the eastern part of North Lombok, namely around the Santong and Sambik Bangkol stations, while design rainfall with low values is in the western part of North Lombok, namely around the Tanjung and Gunungsari stations.

Anatomical and subcortical invasiveness in diffuse low-grade astrocytomas differ between IDH status and provide prognostic information.

Diffuse astrocytomas preferentially infiltrate eloquent areas affecting the outcome. A preoperative understanding of isocitrate dehydrogenase (IDH) status may offer opportunities for specific targeted therapies impacting treatment management. The aim of this study was to analyze clinical, topographical, radiological in WHO 2 astrocytomas with different IDH status and the long-term patient's outcome. A series of confirmed WHO 2 astrocytoma patients (between 2005 and 2015) were retrospectively analyzed. MRI sequences (FLAIR) were used for tumor volume segmentation and to create a frequency map of their locations into the Montreal Neurological Institute (MNI) space. The Brain-Grid (BG) system (standardized radiological tool of intersected lines according to anatomical landmarks) was used as an overlay for infiltration analysis of each tumor. Long-term follow-up was used to perform a survival analysis. Forty patients with confirmed IDH status (26 IDH-mutant, IDHm/14 IDH-wild type, IDHwt) according to WHO 2021 classification were included with a mean follow-up of 7.8 years. IDHm astrocytomas displayed a lower number of BG-voxels (P < 0.05) and were preferentially located in the anterior insular region. IDHwt group displayed a posterior insular and peritrigonal location. IDHwt group displayed a shorter OS compared with IDHm (P < 0.05), with the infiltration of 7 or more BG-voxels as an independent factor predicting a shorter OS. IDHm and IDHwt astrocytomas differed in preferential location, number of BG-voxels and OS at long follow-up time. The number of BG-voxels affected the OS in IDHwt was possibly reflecting higher tumor invasiveness. We encourage the systematic use of alternative observational tools, such as gradient maps and the Brain-Grid analysis, to better detect differences of tumor invasiveness in diffuse low-grade gliomas subtypes.

Method of Frequency Mapping of a Virus Using the Example of Tobacco Mosaic

Method of Frequency Mapping of a Virus Using the Example of Tobacco Mosaic

Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks

The extensive integration of electric vehicles (EVs) into power grids has raised safety concerns. Meanwhile current EVs charging loads forecasting models mainly focus on single-step predictions, neglect load sparsity and underutilize residuals, limiting their accuracy. To tackle these issues, a multi-step short-term forecasting model, specifically designed to predict charging loads at public EVs charging stations using historical data, is proposed. The model begins with a preprocessing phase to handle missing data and outliers. Subsequently, an Autoformer variant is intruduced, which initially integrates feature extraction block leveraging the Temporal Convolutional Network (TCN) to adeptly capture temporal dynamics. This is further enhanced with a Discrete Cosine Transform (DCT)-based frequency enhancement block to learn sparsity and periodicity features of EVs charging loads by frequency mapping. In addition, an advanced error correction block, comprising Bayesian optimization algorithm, Convolutional Neural Networks, Multi-Head Self-Attention, and Gated Recurrent Units (BCMG), is incorporated to refine initial prediction results. Above all, the TCN-DCT enhanced Autoformer-BCMG (TDformerBCMG) model is proposed. Finally, a comprehensive experimental analysis is conducted on ACN-data for EVs charging load forecasting in 1–3 h steps. It is demonstrated that TDformerBCMG not only is effective and superior in each component, but also achieves a reduction in Mean Absolute Error by 20.94 % to 74.05 % compared to existing models. Moreover, stability is evidenced across 50 repeated trials, with the standard deviation for the Coefficient of Determination varying between 8.5844e-3 and 3.1580e-2. Further discussion on application analysis is provided. Consequently, TDformerBCMG offers a viable methodology and establishes a new benchmark for power system implementation.

Heartbeat-related spectral perturbation of electroencephalogram reflects dynamic interoceptive attention states in the trial-by-trial classification analysis

Attending to heartbeats for interoceptive awareness initiates distinct electrophysiological responses synchronized with the R-peaks of an electrocardiogram (ECG), such as the heartbeat-evoked potential (HEP). Beyond HEP, this study proposes heartbeat-related spectral perturbation (HRSP), a time–frequency map of the R-peak locked electroencephalogram (EEG), and explores its characteristics in identifying interoceptive attention states using a classification approach. HRSPs of EEG brain components specified by independent component analysis (ICA) were used for the offline and online classification of interoceptive states. A convolutional neural network (CNN) designed specifically for HRSP was applied to publicly available data from a binary-state experiment (attending to self-heartbeats and white noise) and data from our four-state classification experiment (attending to self-heartbeats, white noise, time passage, and toe) with diverse input feature conditions of HRSP. From the dynamic state perspective, we evaluated the primary frequency bands of HRSP and the minimal number of averaging epochs required to reflect changing interoceptive attention states without compromising accuracy. We also assessed the utility of group ICA and models for classifying HRSP in new participants. The CNN for trial-by-trial HRSP with actual R-peaks demonstrated significantly higher classification accuracy than HRSP with sham, i.e., randomly positioned, R-peaks. Gradient-weighted class activation mapping highlighted the prominent role of theta and alpha bands between 200–600 ms post-R-peak—features absent in classifications using sham HRSPs. Online classification benefits from employing a group ICA and classification model, ensuring reliable accuracy without individual EEG precollection. These results suggest HRSP’s potential to reflect interoceptive attention states, proposing transformative implications for clinical applications.

Unraveling the Structure-Spectrum Relationship of Yeast Phenylalanine Transfer RNA: Insights from Theoretical Modeling of Infrared Spectroscopy.

Yeast phenylalanine tRNA (tRNAphe) is a paradigmatic model in structural biology. In this work, we combine molecular dynamics simulations and spectroscopy modeling to establish a direct link between its structure, conformational dynamics, and infrared (IR) spectra. Employing recently developed vibrational frequency maps and coupling models, we apply a mixed quantum/classical treatment of the line shape theory to simulate the IR spectra of tRNAphe in the 1600-1800 cm-1 region across its folded and unfolded conformations and under varying concentrations of Mg2+ ions. The predicted IR spectra of folded and unfolded tRNAphe are in good agreement with experimental measurements, validating our theoretical framework. We then elucidate how the characteristic L-shaped tertiary structure of the tRNA and its modulation in response to diverse chemical environments give rise to distinct IR absorption peaks and line shapes. These calculations effectively bridge IR spectroscopy experiments and atomistic molecular simulations, unraveling the molecular origins of the observed IR spectra of tRNAphe. This work presents a robust theoretical protocol for modeling the IR spectroscopy of nucleic acids, which will facilitate its application as a sensitive probe for detecting the fluctuating secondary and tertiary structures of these essential biological macromolecules.

Innervation of thumb carpometacarpal joint: implications for diagnostic block and denervation procedures

IntroductionOsteoarthritis (OA) of the thumb carpometacarpal (CMC) joint is a common disorder that negatively impacts hand function. Denervation of the thumb CMC joint has emerged as a viable treatment option....

Persistence of Multiscale Degenerate Invariant Tori in Reversible Systems with Degenerate Frequency Mapping

Persistence of Multiscale Degenerate Invariant Tori in Reversible Systems with Degenerate Frequency Mapping

Classification of Time–Frequency Maps of Guided Waves Using Foreground Extraction

Guided waves propagating in mechanical structures have proved to be an essential technique for applications, such as structural health monitoring. However, it is a well-known problem that when using non-stationary guided wave signals, dispersion, and high-order vibrational modes are excited, it becomes cumbersome to detect and identify relevant information. A typical method for the characterization of these non-stationary signals is based on time–frequency (TF) mapping techniques. This method produces 2D images, allowing the study of specific vibration modes and their evolution over time. However, this approach has low resolution, increases the size of the data, and introduces redundant information, making it difficult to extract relevant features for their accurate identification and classification. This paper presents a method for identifying discontinuities by analyzing the data in the TF maps of Lamb wave signals. Singular Value Decomposition (SVD) for low-rank optimization and then perform foreground feature extraction on the maps were proposed. These foreground features are then analyzed using Principal Component Analysis (PCA). Unlike traditional PCA, which operates on vectorized images, our approach focuses on the correlation between coordinates within the maps. This modification enhances feature detection and enables the classification of discontinuities within the maps. To evaluate unsupervised clustering of the dimensionally reduced data obtained from PCA, we experimentally tested our method using broadband Lamb waves with various vibrational modes interacting with different types of discontinuity patterns in a thin aluminum plate. A Support Vector Machine (SVM) classifier was then implemented for classification. The results of the experimental data yielded good classification effectiveness within reasonably low computational time despite the large matrixes of the TF maps used.

Photoactivated Chromophore for Keratitis-Corneal Cross-linking (PACK-CXL)-A Scoping Review Based on Preclinical Studies.

Photoactivated chromophore for keratitis-corneal cross-linking (PACK-CXL) stabilizes the corneal stroma and eliminates microorganisms. Numerous PACK-CXL protocols, using different energy sources and chromophores, have been applied in preclinical studies, including live animal studies, with various experimental designs and endpoints. So far, a systematic mapping of the applied protocols and consistency across studies seems lacking but is essential to guide future research. The scoping review protocol was in line with the JBI Manual for Evidence Synthesis. Electronic databases were searched (Embase, MEDLINE, Scopus, Web of Science) to identify eligible records, followed by a two-step selection process (title and abstract screening, full text screening) for record inclusion. We extracted information on (1) different PACK-CXL protocol characteristics; (2) infectious pathogens tested; (3) study designs and experimental settings; and (4) endpoints used to determine antimicrobial and tissue stabilizing effects. The information was charted in frequency maps. The searches yielded 3654 unique records, 233 of which met the inclusion criteria. With 103 heterogeneous endpoints, the researchers investigated a wide range of PACK-CXL protocols. The tested microorganisms reflected pathogens commonly associated with infectious keratitis. Bacterial solutions and infectious keratitis rabbit models were the most widely used models to study the antimicrobial effects of PACK-CXL. If preclinical PACK-CXL studies are to guide future translational research, further cross-disciplinary efforts are needed to establish, promote, and facilitate acceptance of common endpoints relevant to PACK-CXL. Systematic mapping of PACK-CXL protocols in preclinical studies guides future translational research.

Improved carbon fibers dispersion in geopolymer composites

Carbon fibers (CFs) reinforced alkali-activated (AA) ground granulated blast furnace slag (GGBFS) geopolymer composites have excellent mechanical and thermoelectrical properties, as well as low energy consumption and low CO2 emissions during their production. However, the dispersion of CFs in the geopolymer matrix is always a technical issue in the preparation of the green composite material. Therefore, this study comparatively investigated various dispersion methods, including, mixing sequence (pre-mixing and after-mixing methods), dispersing agent (nano silicon dioxide [nSiO2]), and ultrasonic treatment time (0, 15, 30, and 45 min), as well as CFs content (0.5, 1.0, and 1.5 wt% of GGBFS). The distribution of CFs was then qualitatively and quantitatively evaluated by a series of tests, such as flowability, electrical resistivity, flexural strength, scanning electron microscope (SEM) analysis, and X-ray computed tomography (CT). The experimental results showed that the proposed pre-mixing method provided excellent dispersion characteristics for CFs compared with the after-mixing method. The introduction of nSiO2 can enhance the dispersion of CFs and the mechanical and electrical properties of AA GGBFS geopolymer composites. At a carbon fiber content of 1.5 %, the pre-mixing method and the addition of nSiO2 dispersant reduced the resistivity of the 28-day geopolymer composites by 17.0 % and 38 %, respectively, compared with the after-mixing method. It is feasible to use X-ray CT scanning with the gray-scale frequency map to analyze the dispersion effect of CFs in AA GGBFS geopolymer composites. However, the results were not intuitive when less agglomeration of CFs occurred for the low content of CFs. The scanning method is more applicable to the sample with over 1.0 wt% CFs. The average pixel areas of uniformly dispersed CF bundles in the 2D images of the pre-mixing method and the addition of nSiO2 dispersant specimens were 3.32 % and 6.55 % higher than those of the after-mixing method, respectively. The 2D and 3D scanning results from the dispersion characteristics of CFs were consistent. 2D scanning could provide a time-consuming option for the measurement of CFs dispersion characteristics.