Two-dimensional Data Research Articles

A deep learning object detection method to improve cluster analysis of two-dimensional data

A deep learning object detection method to improve cluster analysis of two-dimensional data

Structure Revision of Formyl Phloroglucinol Meroterpenoids: A Unified Approach Using NMR Fingerprinting and DFT NMR and ECD Analyses

NMR fingerprints are valuable tools for analyzing complex natural product mixtures and identifying incorrectly assigned structures in the literature. Our diagnostic NMR fingerprints for formyl phloroglucinol meroterpenoids revealed discrepancies in the structures reported for eucalyprobusal C (1a) and eucalypcamal K (2a). NMR fingerprinting PCA analyses identified 1a as an oxepine-diformyl phloroglucinol and 2a as an oxepine 3-acyl-1-formyl phloroglucinol, contrary to their initial assignments as pyrano-diformyl and pyrano 3-acyl-1-formyl phloroglucinols, respectively. Extensive reinterpretation of their reported one- and two-dimensional NMR data, coupled with GIAO DFT-calculated 1H and 13C NMR chemical shift and DP4+ analyses, supported the unequivocal reassignment of eucalyprobusal C to 1b and eucalypcamal K to 2b. The absolute configurations of the revised oxepine-containing phloroglucinol meroterpenoids were confirmed via the reinterpretation of their reported ROESY and NOESY NMR data, along with comparative TDDFT-calculated and experimental ECD spectra.

Bearing fault diagnosis based on the feature enhancement of improved mean differential SDP images★ ★This work is supported by National Natural Science Foundation of China [No. 61603149]; Shandong Province Natural Science Foundation [No. ZR2023MF102

In the fault diagnosis of mechanical equipment, vibrational signals often contain noise. In order to solve the problem of rolling bearing fault diagnosis under the influence of noise, an image enhancement method based on mean difference images is proposed. Convert one-dimensional time series into two-dimensional image data by using the symmetrized dot pattern method. The data are processed and grouped by variational mode decomposition to obtain a mean image with stable features. A feature enhancement method based on improved mean difference images is used to achieve data mining of fault features. The improved Canberra distance is used as the classification basis to realize the accurate classification of rolling bearing faults. Finally, the classification effect of the method is verified by experiments. The experimental results show that the image enhancement method proposed in this paper can improve fault features in the image and has a good anti-noise ability.

An integrated complex T-spherical fuzzy set and soft set model for quantum computing and energy resource planning

This study introduces the Complex T-Spherical Fuzzy Soft Set (CTSFSS), a novel and practical model for representing two-dimensional ambiguous information. This is achieved by integrating the Complex T-Spherical Fuzzy Set (CTSFS) with the Soft Set (SS). The development of the CTSFSS theory is motivated by the absence of neutral membership in complex q-rung orthopair fuzzy soft sets and the limitations of Traditional Spherical Fuzzy Soft Sets (TSFSS) in capturing two-dimensional data. Data storage and processing in the modern technological landscape necessitate sophisticated algorithmic computations and robust representation of information. To this end, computational systems predominantly rely on a binary system structured on two fundamental states: 0 (off) and 1 (on). Moreover, the significance of energy resources and computer-aided tools is underscored in this domain, especially considering the potential impact of quantum computing and advances in energy resources on the evolution of digital technology. In addressing these complexities, we propose Complex T-Spherical Fuzzy Soft Relations (CTSFSRs), formulated through the Cartesian product of two CTSFSSs. These relations are adept at handling complex and uncertain data in real-world scenarios. We define and categorize various types of CTSFSRs, followed by an in-depth analysis of their outcomes. Additionally, this paper introduces innovative visualization techniques based on CTSFSRs, tailored for decision-making processes involving energy resources and quantum computing applications. The efficacy and applicability of these proposed methodologies are substantiated through comparative analysis with existing models.

Improving Sensitivity and Reproducibility of Surface-Enhanced Raman Scattering Biochips Utilizing Magnetoplasmonic Nanoparticles and Statistical Methods.

Surface-enhanced Raman scattering (SERS) technology has been widely recognized for its remarkable sensitivity in biochip development. This study presents a novel sandwich immunoassay that synergizes SERS with magnetoplasmonic nanoparticles (MPNs) to improve sensitivity. By taking advantage of the unique magnetism of these nanoparticles, we further enhance the detection sensitivity of SERS biochips through the applied magnetic field. Despite the high sensitivity, practical applications of SERS biochips are often limited by the issues of stability and reproducibility. In this study, we introduced a straightforward statistical method known as "Gaussian binning", which involves initially binning the two-dimensional Raman mapping data and subsequently applying Gaussian fitting. This approach enables a more consistent and reliable interpretation of data by reducing the variability inherent in Raman signal measurements. Based on our method, the biochip, targeting for C-reactive protein (CRP), achieves an impressive detection limit of 5.96 fg/mL, and with the application of a 3700 G magnetic field, it further enhances the detection limit by 5.7 times, reaching 1.05 fg/mL. Furthermore, this highly sensitive and magnetically tunable SERS biochip is easily designed for versatile adaptability, enabling the detection of other proteins. We believe that this innovation holds promise in enhancing the clinical applicability of SERS biochips.

Identifying stress scores from gait biometrics captured using a camera: A cross-sectional study

BackgroundStress is a critical risk factor for various health issues, but an objective, non-intrusive and effective measurement approach for stress has not yet been established. Gait, the pattern of movements in human locomotion, has been proven to be a valid behavioral indicator for recognizing various mental states in a convenient manner. Research QuestionThis study aims to identify the severity of stress by assessing human gait recorded through an objective, non-intrusive measurement approach. MethodsOne hundred and fifty-two participants with an average age of 23 years old (SD = 1.07) were recruited. The Chinese version of the Perceived Stress Scale with 10 items (PSS-10) was used to assess participants’ stress levels. The participants were then required to walk naturally while being recorded with a regular camera. A total of 1320 time-domain and 1152 frequency-domain gait features were extracted from the videos. The top 40 contributing features, confirmed by dimensionality reduction, were input into models consisting of four machine-learning regression algorithms (i.e., Gaussian Process Regressor, Linear Regression, Random Forest Regressor, and Support Vector regression), to assess stress levels. ResultsThe models that combined time- and frequency-domain features performed best, with the lowest RMSE (4.972) and highest validation (r = 0.533). The Gaussian Process Regressor and Linear Regression outperformed the others. The greatest contribution to model performance was derived from gait features of the waist, hands, and legs. SignificanceThe severity of stress can be accurately detected by machine learning models using two-dimensional (2D) video-based gait data. The machine learning models used for assessing perceived stress were reliable. Waist, hand, and leg movements were found to be critical indicator in detecting stress.

Implementasi Convolutional Neural Network Pada Robot Tikus Micromouse Untuk Navigasi Labirin Dengan Pendeteksi Garis Menggunakan Raspberry Pi

Micromouse is a science and technology project competition that began in the late 1970s. Simply put, the micromouse competition is an event where robotic mice can navigate a 16x16 maze without human assistance. Convolutional Neural Network, also known as CNN, is an extension of the Artificial Neural Network (ANN) designed to process two-dimensional data and is often used for classification, recognition, and prediction tasks. In the conducted research, a robotic mouse was designed using Raspberry Pi to control servo motor movements and utilize a camera to detect maze obstacles, such as intersections, through image scanning. The robotic mouse was tested using a CNN architecture model on TensorFlow, assisted by a line detection algorithm. The testing results indicated that the CNN model architecture showed signs of overfitting (the model learned features too well from the training data) with an accuracy of 96.53%, and the model accuracy evaluation result was 97.97% on the designed dataset. As part of the testing, a maze size of 5x5 was used. The testing of the Convolutional Neural Network model and line detection algorithm, when applied to the robotic mouse, demonstrated that the mouse could navigate according to the path. However, the recorded data was influenced by external light reflecting off the surface of the path, causing the line detection algorithm to perceive it as an obstacle, leading the robot to turn back..

Target Detection Method for High-Frequency Surface Wave Radar RD Spectrum Based on (VI)CFAR-CNN and Dual-Detection Maps Fusion Compensation

This paper proposes a method for the intelligent detection of high-frequency surface wave radar (HFSWR) targets. This method cascades the adaptive constant false alarm (CFAR) detector variability index (VI) with the convolutional neural network (CNN) to form a cascade detector (VI)CFAR-CNN. First, the (VI)CFAR algorithm is used for the first-level detection of the range–Doppler (RD) spectrum; based on this result, the two-dimensional window slice data are extracted using the window with the position of the target on the RD spectrum as the center, and input into the CNN model to carry out further target and clutter identification. When the detection rate of the detector reaches a certain level and cannot be further improved due to the convergence of the CNN model, this paper uses a dual-detection maps fusion method to compensate for the loss of detection performance. First, the optimized parameters are used to perform the weighted fusion of the dual-detection maps, and then, the connected components in the fused detection map are further processed to achieve an independent (VI)CFAR to compensate for the (VI)CFAR-CNN detection results. Due to the difficulty in obtaining HFSWR data that include comprehensive and accurate target truth values, this paper adopts a method of embedding targets into the measured background to construct the RD spectrum dataset for HFSWR. At the same time, the proposed method is compared with various other methods to demonstrate its superiority. Additionally, a small amount of automatic identification system (AIS) and radar correlation data are used to verify the effectiveness and feasibility of this method on completely measured HFSWR data.

Identification of composite power quality disturbances based on relative position matrix

With the integration of large-scale nonlinear loads and distributed power sources into the grid, composite power quality disturbances (PQDs) events are becoming increasingly common, which significantly degrade the quality of power supply. Therefore, this paper focuses on studying the accurate classification of composite PQDs to mitigate the risk of power quality deterioration. However, traditional classification methods perform barely satisfactory in terms of accuracy and robustness in the classification of PQDs. To address these issues, this paper proposes a method for recognizing composite PQDs based on relative position matrix (RPM). Initially, utilizing the RPM method, the initial one-dimensional PQD time series data is transformed into two-dimensional image data while preserving its high-frequency characteristics. This process results in the creation of an informative and feature-rich image training set. Subsequently, an end-to-end framework for PQDs classification was developed. The framework utilizes convolutional neural networks to automatically extract multi-scale spatial and temporal features from image data. This design aims to automate the classification of composite PQDs, eliminating the need for labor-intensive manual signal processing and feature extraction. This integration ensures a more accurate and robust classification. Finally, the proposed method is tested on a case involving 30 types of PQDs at varying noise levels and compared with existing power quality disturbance classification methods, and results show that the proposed method has better performance than the previously established methods.

A Review of 3D Printing and Surgical Planning

Three-Dimensional Printing (3DP), also known as rapid prototyping or additive manufacturing, has revolutionized the field of medicine, particularly in preoperative planning. By transforming intricate Two-Dimensional (2D) medical imaging data into tangible, patient-specific Three-Dimensional (3D) models, 3DP enhances surgical precision and patient care. This technology facilitates detailed preoperative visualization, enabling surgeons to interact with accurate anatomical models, plan complex procedures, and anticipate potential challenges. The evolution of 3DP, driven by advances in Fused Deposition Modeling (FDM), Selective Laser Sintering (SLS), and Stereolithography (SLA), has democratized access to affordable and sophisticated 3D printers, spurring innovation in medical applications. In this study, a comprehensive review of the literature on the integration of 3DP technology into preoperative planning is conducted. Applications across various surgical disciplines, including cardiovascular, orthopedic, craniofacial, neurosurgical, oncological, transplant, and pediatric surgeries are examined. Benefits of 3DP in providing tactile feedback, creating patient-specific surgical guides and implants, and enhancing surgical simulation and training are evaluated. Additionally, challenges associated with the high costs, time-intensive processes, and material limitations of 3DP are assessed. Findings demonstrate that 3DP significantly reduces operative times, improves surgical outcomes, and lowers complication rates. The study highlights the transformative impact of 3DP on surgical practice and underscores its potential for future advancements. With ongoing research and innovation in materials science, bioprinting, artificial intelligence, and augmented reality, 3DP is poised to further revolutionize preoperative planning, offering more personalized, precise, and patient-centered healthcare solutions.

Differential Dynamics of Transit Use Resilience During the COVID-19 Pandemic Using Multivariate Two-Dimensional Functional Data Analysis

Differential Dynamics of Transit Use Resilience During the COVID-19 Pandemic Using Multivariate Two-Dimensional Functional Data Analysis

Transformer-Based Spatio-Temporal Analysis for Classification of Aortic Stenosis Severity From Echocardiography Cine Series.

Aortic stenosis (AS) is characterized by restricted motion and calcification of the aortic valve and is the deadliest valvular cardiac disease. Assessment of AS severity is typically done by expert cardiologists using Doppler measurements of valvular flow from echocardiography. However, this limits the assessment of AS to hospitals staffed with experts to provide comprehensive echocardiography service. As accurate Doppler acquisition requires significant clinical training, in this paper, we present a deep learning framework to determine the feasibility of AS detection and severity classification based only on two-dimensional echocardiographic data. We demonstrate that our proposed spatio-temporal architecture effectively and efficiently combines both anatomical features and motion of the aortic valve for AS severity classification. Our model can process cardiac echo cine series of varying length and can identify, without explicit supervision, the frames that are most informative towards the AS diagnosis. We present an empirical study on how the model learns phases of the heart cycle without any supervision and frame-level annotations. Our architecture outperforms state-of-the-art results on a private and a public dataset, achieving 95.2% and 91.5% in AS detection, and 78.1% and 83.8% in AS severity classification on the private and public datasets, respectively. Notably, due to the lack of a large public video dataset for AS, we made slight adjustments to our architecture for the public dataset. Furthermore, our method addresses common problems in training deep networks with clinical ultrasound data, such as a low signal-to-noise ratio and frequently uninformative frames. Our source code is available at: https://github.com/neda77aa/FTC.git.

Epicardial pulsed-field ablation-impact of electric field and heat distribution induced by coronary metallic stents.

Pulsed-field ablation (PFA) technique is a nonthermal ablation technique. No study has yet evaluated the effect of the positional relationship between the ablation electrode (AE) and the coronary metal stent (CMS) on the electric field distribution and temperature distribution in epicardial ablation. Our study aimed to evaluate the effect of the CMS on the electric field as well as the temperature distribution in different models. Multi-angle modeling of the CMS and AE was performed. The PFA ablation region was evaluated with a field strength contour of 1,000 V/cm, which was used to assess the validity of the two-dimensional (2D) model simulation data as well as the distribution of the multi-angle electric field and temperature in the three-dimensional (3D) model. The presence of the CMS had little effect on the width of the ablation area (0.2 mm). In the 3D model, the temperature of the ablation area was highest when the angle between the AE and the CMS was in the 90° position (43.4°C, 41.3°C); a change in the distance between the AE and the CMS affected the temperature of the ablation area (maximum 2.1°C) and the width of the ablation (maximum 0.32 mm). The presence of the CMS distorts the distribution of the electric field, but does not produce a change in the extent of the ablation damage, nor does it bring thermal damage to the ablation region. Different simulation models give similar results in PFA calculations, and this study effectively reduces the complexity of modeling simulation.

Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.

Octanol/water partition coefficients estimated using retention times in reverse-phase liquid chromatography and calculated in silico as one of the determinant factors for pharmacokinetic parameter estimations of general chemical substances.

The octanol/water partition coefficient P (logP) is a hydrophobicity index and is one of the determining factors for the pharmacokinetics of orally administered substances because it influences membrane permeability. To illustrate the wide-ranging variety of compounds in the chemical space, a two-dimensional data plot consisting of 25 blocks was previously proposed based on a substance's in silico chemical descriptors. The logP values of approximately 200 diverse chemicals (test plus reference compounds covering all 25 blocks of the chemical space) were estimated experimentally using retention times in reverse-phase liquid chromatography; these values were compared with those of authentic reference compounds with established logP values (available for 17 of 60 reference substances in the Organization for Economic Co-operation and Development Test Guideline 117). The logP values of 140 of 165 chemicals successfully estimated using four different mobile phase conditions (pH 2, 4, 7, and 10 for molecular forms) correlated significantly with those calculated using the in silico packages ChemDraw and ACD/Percepta (r > 0.72). Although substances that neighbored authentic compounds in the chemical space had precisely correlated logP values estimated experimentally and in silico, some compounds that were more distant from authentic substances showed lower logP values than those estimated in silico. These results indicate that additional authentic reference materials with wider ranging chemical diversity and their logP values from reverse-phase liquid chromatography should be included in the international test guidance to promote simple and reliable estimation of octanol/water partition coefficients, which are important determinant factors for the pharmacokinetics of general chemicals.

The Landscape of Compressibility Measures for Two-Dimensional Data

The Landscape of Compressibility Measures for Two-Dimensional Data

Evaluasi Perbandingan Kinerja Algoritma Cheng and Church Biclustering Terhadap Algoritma Clustering Klasik K-Means untuk Mengidentifikasi Pola Distribusi Barang Ekspor Indonesia

Clustering is a process of grouping data into several groups (clusters) so that data in one cluster has a homogeneous level of similarity and data between clusters has heterogeneous similarity. A common example of a clustering algorithm is K-Means Clustering. Compared with classical clustering algorithms, the biclustering algorithm is a two-dimensional data grouping process. The biclustering algorithm functions to find data submatrices, namely row subgroups and column subgroups that have high correlation. One example of a biclustering algorithm is Cheng and Church Biclustering (CC Biclustering). The aim of this research is to evaluate the performance of the biclustering algorithm against classical clustering algorithms. Analysis applied to CC Biclustering and K-Means Clustering to identify distribution patterns of Indonesian export goods in the period 2013 to 2022. Based on research results, the optimal scenario for the K-Means algorithm is scenario 2, that is the application of the 7 cluster K-Means algorithm with pre- processing data scaling. Meanwhile, the optimal scenario for the CC Biclustering algorithm is scenario 1, that is the application of the CC Biclustering algorithm with a tolerance value of 0.10 with data scaling pre-processing. However, from these two scenarios, based on the MSR/Volume value, it was concluded that the best scenario is scenario 1 in the application of the CC Biclustering algorithm which has an MSR/Volume value of 0.077.

Integration Method of Monitoring Video and Geospatial Data Based on 3D Modeling

The fusion technology of videos has been increasingly applied in various fields such as smart cities and smart agriculture, playing a significant role in enabling real-time scene monitoring and decision-making for surveillance personnel. Currently, conventional methods for integrating geographic data with monitoring videos involve mapping two-dimensional geographic information data onto surveillance videos. However, this method exhibits significant mapping errors in scenarios with large terrain variations and 360 degrees multi-angle real-time previews. To address this issue, this paper proposes a fusion method of monitoring videos and geospatial data based on three-dimensional modeling. This method constructs a virtual scene using digital elevation models and vector geographic data and overlays the images captured under the camera viewport in the virtual scene with each frame of the video images in the monitoring video stream, thereby enhancing the video scene based on geographic data. In practical application scenarios, a system for integrating monitoring videos with geospatial data is designed and implemented. Experimental results demonstrate that this method effectively addresses issues such as unfamiliarity with the geographical environment, ambiguity of location information, inaccuracies in mapping due to terrain variations and changes in camera intrinsic parameters, thus showing superior applicability.

A Geovisualization Dashboard of Granular Food Security Index Map using GIS for Monitoring the Provincial Level Food Security Status

This study aims to build a web-based interactive geovisualization dashboard from a granular food security index map using satellite imagery and other geospatial big data. The map dashboard is built using a two-dimensional (2D) data visualization approach. Making a two-dimensional map using QuantumGIS (QGIS) tools, displayed in the form of WebGIS with the plugin used "Qgis2web" based on javascript leaflets. Once included in WebGIS, interactive visualizations are displayed on websites with interfaces based on hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). The dashboard map is equipped with interactive features such as legend, click grid, zoom, show me where I am, measure distance, and search. Therefore, the dashboard map can be used to monitor the food security index, search for food security index areas, as well as geographical identification of food security index areas which are useful for supporting the analysis of decision-making or policies by the government regarding food security strategies.

Effects of Twin-Block with an expanding device on the upper airway in growing children with skeletal class II malocclusion-a retrospective study based on the consistency of three-dimensional and two-dimensional data.

Skeletal class II malocclusion is one of the most common malocclusions. Among the functional appliances for skeletal class II malocclusion, the Twin-Block appliance with a maxillary expander is effective in repositioning the mandible forward. In this study, we focused our efforts on investigating the effects of Twin-Block appliances with maxillary expanders on the upper airway in growing children with skeletal class II malocclusion by tracing and measuring lateral cephalograms after evaluating the consistency of three-dimensional CBCT data and two-dimensional lateral cephalogram data. A total of 102 patients ranging from 9 to 15years old (11.37 ± 2.80, male/female ratio = 1:1) with skeletal class II malocclusion were selected to evaluate the consistency of CBCT data and lateral cephalogram data. The strongly and moderately correlated segments were then selected to study the effects of Twin-Block with a maxillary expander on the upper airway in 66 growing children with skeletal class II malocclusion (11.31 ± 1.23years old, male/female ratio = 1:1) by lateral cephalograms. The results showed a strong significant correlation in the nasopharynx (r = 0.708) and moderate significant correlations in the overall upper airway (r = 0.641), palatopharynx (r = 0.553), and glossopharynx (r = 0.575) but a weak correlation in the hypopharynx (r = 0.323). The corresponding determination coefficient (R2) was also determined by scatter plot analysis. Moreover, compared with the pretreatment data (T1), the total area of the upper airway and the areas of the nasopharynx, palatopharynx, and glossopharynx after functional treatment (T2) increased statistically and significantly. Lateral cephalograms can reflect the volume of the nasopharynx and oropharynx in skeletal class II children to a certain extent, while Twin-Block appliances with maxillary expanders can widen the volume of the nasopharynx and oropharynx significantly. The lateral cephalogram is reliable for analyzing the nasopharynx, palatopharynx, and glossopharynx in orthodontic clinical practice. Twin-Block appliances with maxillary expanders have a positive effect on skeletal class II patients with airway stenosis.