Raw Data Research Articles

Initial Study of Adaptive Threshold Cycle Slip Detection on BDS/GPS Kinematic Precise Point Positioning during Geomagnetic Storms

Global navigation satellite system (GNSS) provides users with all-weather, continuous, high-precision positioning, navigation, and timing (PNT) services. In the operation and use of GNSS, the influence of the space environment is a factor that must be considered. For example, during geomagnetic storms, a series of changes in the Earth’s magnetosphere, ionosphere, and upper atmosphere affect GNSS’s positioning performance. To investigate the positioning performance of global satellite navigation systems during geomagnetic storms, this study selected three geomagnetic storm events that occurred from September to December 2023. Utilizing the global positioning system (GPS)/Beidou navigation satellite system (BDS) dual-system, kinematic precise point positioning (PPP) experiments were conducted, and the raw observational data from 100 stations worldwide was analyzed. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry Free (GF) cycle-slip detection threshold during geomagnetic storms, which means that the GF is no longer applicable to high-precision positioning services. Meanwhile, there is no significant change in the satellite signal strengths received at the stations during the period of the decrease in positioning accuracy. Analyzing the cycle-slip rates for stations where abnormal accuracy occurred, it was observed that stations experiencing a significant decline in positioning accuracy exhibited serious cycle-slip misjudgments. To improve the kinematic PPP accuracy during magnetic storms, this paper proposes an adaptive threshold for cycle-slip detection and designs five experimental strategies. After using the GF adaptive threshold, the station positioning accuracy improved significantly. It achieved the accuracy level of the quiet period, while the cycle-slip incidence reached the average level. During magnetic storms, the ionosphere changes rapidly, and the use of the traditional GF constant threshold will cause serious cycle-slip misjudgments, which makes the dynamic accuracy in high latitude areas and some mid-latitude areas uncommon, while the use of the GF adaptive threshold can alleviate this phenomenon and improve the positioning accuracy in the high-latitude regions and some of the affected mid-latitude areas during the magnetic storms.

Regulatory safeguards needed if preimplantation genetic testing for polygenic risk scores (PGT-P) is permitted in Singapore

Singapore, a highly affluent island city-state located in Southeast Asia, has increasingly leveraged new assisted reproductive technologies (ART) to overcome its dismal fertility rates in recent years. A new frontier...

Fine Shear-Wave Velocity Structures of Subsurface beneath the Guangdong–Hong Kong–Macao Greater Bay Area with Dense Seismic Array and SPAC Method

Abstract We apply the spatial autocorrelation (SPAC) method to construct the 3D subsurface shear-wave velocity structure model using the short-period dense seismic array (containing 725 nodal geophones) located at the Guangdong–Hong Kong–Macao Greater Bay area (GBA). We first divided the dense array into numerous subarrays, with each subarray consisting of nine nodal geophones, and obtained 562 subarrays that can provide 1D VS profiles of the same quantity. Then, the SPAC method and genetic algorithm are utilized to extract the dispersion curve of the Rayleigh wave from the raw microtremor data and invert VS structure, respectively. Finally, a 3D VS structure model from the surface to 3.3 km depth is derived by combining all 1D VS structures. Relatively low-velocity anomalies above 700 m are considered unconsolidated shallow sediments as well as relatively high-velocity anomalies beneath 1100 m are attributed to consolidated granite bedrock. Meanwhile, low-velocity anomalies that are identified through the vertical VS profile at a depth of about 900–3000 m can be contributed to the fractured zone, and striped low-velocity anomalies in the horizontal VS maps reveal the location of the deeply buried faults in the study area. The results also mean that the SPAC method combined with the records of short-period dense seismic array can be effectively applied to image subsurface structures in high-populated urban area. The development of this noise-resistance and environment-friendly geophysical technique provides a reliable and effective way to explore the complicated subsurface geological structures, which is of great significance to urban engineering construction and earthquake disaster reduction work in densely populated urban agglomerations.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation

The statewide consumer transportation demand model analyzes consumers’ transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server’s computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.

An improved wide-lane ambiguity resolution method for kinematic smartphone positioning

Abstract The release of GNSS raw data on Android smartphones provides the potential for high-precision smartphone positioning using multi-constellation and multi-frequency signals. However, severe multipath and low observation quality in kinematic environments make double-differenced uncombined ambiguities difficult to resolve reliably. To address this, the paper proposes an improved wide-lane (WL) integer ambiguity resolution (IAR) method that combines integer rounding and the Least-Square AMBiguity Decorrelation Adjustment (LAMBDA) methods. The proposed method achieved fix rates of 57% to 70% in challenging environments, with an average improvement of 7 · 7% in horizontal positioning accuracy compared to the float solution. The traditional partial integer rounding method only improved accuracy by 1 · 1%.

Generating hyperspectral reference measurements for surface reflectance from the LANDHYPERNET and WATERHYPERNET networks

The LANDHYPERNET and WATERHYPERNET networks (which together make up the HYPERNETS network) consist of a set of autonomous hyperspectral spectroradiometers (HYPSTAR®) acquiring fiducial reference measurements of surface reflectance at various sites covering a wide range of surface types (both land and water) for use in satellite Earth observation validation and remote sensing applications. This paper describes the processing algorithm for the HYPSTAR® data products. The hypernets_processor is a Python software package to process the LANDHYPERNET and WATERHYPERNET in-situ hyperspectral raw data, collected from the measurement network under the standard measurement protocols, to the designated products, through data transmission and conversion, application of calibration, evaluation of reflectance and other variables, and, archiving for distribution to users. In order to achieve fiducial reference measurement quality, uncertainties are propagated through each step of the processing chain, taking into account temporal and spectral error-covariance. Such detailed uncertainty information is unique for any satellite validation network. We also describe the HYPSTAR® products acquired until 2023–04–31, consisting of 12,190 LANDHYPERNET sequences and 55,514 WATERHYPERNET sequences (of which respectively 11,802 and 44,412 were successfully processed to surface reflectance).

FPGA-enhanced IoT methods for disease pre-screening and prediction: An energy optimization approach

The research paper has introduced a system based on the Internet of Things (IoT) that has the potential to contribute to the creation of a Consumer Electronics (CE) product. In the event that any health-related measure deviates from the normal range, this system, which is based on IoT technology, will send a notification to the user. The data collected by this system is sent to an analysis system based on a Field Programmable Gate Array (FPGA) through a mobile application, and then transferred to the cloud for storage. To display this raw data, a wearable IoT device can be utilized, and the system will process the data accordingly. The major innovation of this proposed project lies in the development of a fuzzy classifier that can accurately predict the pathological condition of an illness. By implementing a fuzzy classifier using FPGA, the execution time is greatly reduced i.e. 57.7μs compared to other algorithms. Ans also the accuracy of the proposed model is 98.8 %, which higher then other machine learning model such as 97 %, 96.5 %, 93.2 % respectively for SVM, KNN and DT models.previous models like KNN, DT, SVM, and NB (Naive Bayes).

Music Information Retrieval using Deep Learning Techniques

Music Information Retrieval (MIR) is gaining attention due to the surge in digital music and the need for efficient search and recommendation systems. Traditional MIR methods rely on hand-crafted features and rule-based systems, limiting their adaptability. Deep Learning (DL) shows promise in automatically extracting complex patterns from raw data. This paper offers an extensive overview of MIR tasks like classification, genre recognition, similarity search, and recommendation, along with DL models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), including LSTM and Transformer architectures, tailored for MIR. Challenges such as data scarcity, computational complexity, and interpretability persist, with proposed solutions like data augmentation, transfer learning, and attention mechanisms. Experimental results on benchmark datasets demonstrate DL's superiority in accuracy, scalability, and robustness over traditional methods. Practical examples highlight DL's potential to revolutionize music search, recommendation, and analysis. Emphasizing the importance of large annotated datasets for training high-quality DL models, strategies for data collection, labeling, and preprocessing are outlined. DL offers promising prospects for advancing MIR by addressing its inherent challenges and establishing new performance benchmarks. Further DL development is expected to drive innovation and enhance digital music consumption experiences through MIR systems. Keywords: Deep Learning, Convolution Neural Network (CNNs), Recurrent Neural Network (RNNs), Digital Music Consumption, Genre recognition.

An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance

An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance

Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning

Targeted mass spectrometry (MS) holds promise for precise protein and protein‐representative peptide identification and quantification, enhancing disease diagnosis. However, its clinical application is hindered by complex data analysis and expert review requirements. It is hypothesized that machine learning (ML) models can automate data analysis to accelerate the clinical application of MS. The approach involves an ML‐driven pipeline that extracts statistical and morphological features from an MS target region and feeds these features into ML algorithms to generate and assess predictive models. The findings demonstrate ML prediction models exhibit superior performance when trained on extracted features versus raw spectra intensity data and that random forest models exhibit robust classification performance in both internal and external validation datasets. These models remain effective across varying training dataset sizes and positive sample rates and are enhanced by a nested active learning approach. This approach can thus revolutionize clinical MS applications.

Wave-CapNet: A Wavelet Neuron-based Wi-Fi Sensing Model for Human Identification

Gait is regarded as a unique feature for identifying people, and gait recognition is the basis of various customized services of the IoT. Unlike traditional techniques for identifying people, the Wi-Fi-based technique is unconstrained by illumination conditions and such that it eliminates the need for dense, specialized sensors and wearable devices. Although deep learning-based sensing models are conducive to the development of Wi-Fi-based identification, the latter technique relies on a large amount of data and requires a long training time, where this limits the scope of its use for identifying people. In this study, we propose a Wi-Fi sensing model called Wave-CapNet for human identification. We use data processing to eliminate errors in the raw data so that the model can extract the characteristics in channel state information (CSI). We also design a dedicated adaptive wavelet neural network to extract representative features from Wi-Fi signals with only a few epochs of training and a small number of parameters. Experiments show that it can identify human gait with an average accuracy of 99%. Moreover, it can achieve an average accuracy of 95% by using only 10% of the data and fewer than five epochs and outperforms state-of-the-art (SOTA) methods.

Rapid detection of endogenous impurities in walnuts using near-infrared hyperspectral imaging technology

Impurities in walnut-based products pose a significant risk to human health, necessitating the development of rapid and efficient methods for detecting endogenous impurities in walnut shell-breaking materials. To address this issue, this study aims to investigate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) in conjunction with conventional chemometric methods and deep learning models. First, biochemical experiments demonstrated differences in the total phenolic and flavonoid contents of different walnut kernels. An improved support vector machine model based on the butterfly optimization algorithm was constructed for classification using an NIR dataset of endogenous impurities in walnuts, achieving an accuracy of 96.12 %. In addition, this study proposed the WT-NIRSNet model, a deep neural network with an additional attention mechanism, and compared it to a traditional chemometric approach. The study demonstrated an accuracy of 99.03 % on the test set using WT-NIRSNet to solve the fast classification problem of endogenous impurities in walnuts. It can achieve desirable classification results using only raw spectral data without complex preprocessing operations and is superior to traditional chemometric models. Therefore, the NIR-HSI technique combined with deep neural networks has a significant potential for detecting endogenous impurities in walnuts.

The Effectiveness of Direct Instruction Model in Teaching Solfeggio for Beginners

The background of this study was rooting down from the students music learning circumstances, of which they have low skill and knowledge of solfeggio, low ability of transferring solfeggio automatically into a new context although they have learned it, unavailability of learning material, syllabus, and lesson plan, standardized method and media of learning. Based on the problems stated above, it is very important to design a research dealing with learning solfeggio which lasting and producing a complete, practical, reliable and valid curricula of solfeggio. The basis model of development is using ADDIE theory. This research belongs to R & D research. The subject of the research is the students of art and music department, FBS Padang State University. The instrumentation of data gathering was using interview, questionnaire, tryout, performance-test. To analyze the raw data to see the level of reliability and practicality the researcher used the formula of Rho Spearman Brown, and the result is that r-obtain 1.2 exceeds r-table within significance level of 95% (0.05) it is 0.349 or by (0,01) it is 0.449. To prove the effectiveness of the product of this research ( the model of learning solfeggio ), the writer distinguish the data of pre-test to post-test by accumulating the scores the students obtained by using match t-test, and the result is that t-obtain 9.311 exceeds t-table 1.697. It means that, the model of learning solfeggio as the product of this research is much more effective significantly since the result of the match t-test is so high exceeding the number of the t-tab. It can be concluded that this model is very useful for the lecturers and the students as well in learning solfeggio at the music department of FBS Padang State University.

A Smart Application Frame of Remote Sensing in Non-grain Production Data Governance

Abstract. This study addresses the intricate challenges encountered in the data governance process of Non-grain Production (NGP) on Arable land. This involves managing data from diverse sources, with varying accuracies and formats, and utilizing multiple specialized software tools. An object-oriented approach is adopted to encapsulate experiential knowledge related to the data and associated processing methods, thus creating an Application Knowledge Body Model (AKBM). This model acts as a conduit between users and computational resources, encompassing various types of data and their corresponding processing and analysis methods. Moreover, by employing model inference techniques to devise methods for transitioning from raw data models to target models, a foundation is laid for the accumulation, sharing, and intelligent application of expertise on data, methods, models, and knowledge.The application examples demonstrate that users can directly construct new solutions containing relevant data and associated processing methods, rather than grappling with a multitude of data files and complex specialized software when encountering novel challenges. This promotes collaborative development in data governance on geospatial big data platforms, significantly enhancing governance efficiency, improving the quality of information support in NGP cultivation management, advancing current technological capabilities, and fostering the progression of related technologies.

Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics.

Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network's learning dynamics and hinder the model's ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.

HiPSC-CM Electrophysiology: Impact of Temporal Changes and Study Parameters on Experimental Reproducibility.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are frequently used for preclinical cardiotoxicity testing and remain an important tool for confirming model-based predictions of drug effects in accordance with Comprehensive in Vitro Proarrhythmia Assay (CiPA). Despite the considerable benefits hiPSC-CMs provide, concerns surrounding experimental reproducibility have emerged. We investigated the effects of temporal changes and experimental parameters on hiPSC-CM electrophysiology. iCell cardiomyocytes2 were cultured and biosignals were acquired using a microelectrode array (MEA) system (1-14 days). Continuous recordings revealed a 22.6% increase in the beating rate and 7.7% decrease in the field potential duration (FPD) during a 20-minute equilibration period. Location specific differences across a multiwell plate were also observed, with iCell cardiomyocytes2 in the outer rows beating 8.8 beats per minute (BPM) faster than the inner rows. Cardiac endpoints were also impacted by cell culture duration; from 2-14 days the beating rate decreased (-12.7 BPM), FPD lengthened (+257 ms), and spike amplitude increased (+3.3 mV). Cell culture duration (4-10 days) also impacted cardiomyocyte drug responsiveness (E-4031, nifedipine, isoproterenol). qRT-PCR results suggest that daily variations in cardiac metrics may be linked to the continued maturation of hiPSC-CMs in culture (2-30 days). Daily experiments were also repeated using a second cell line (Cor.4U). Collectively, our study highlights multiple sources of variability to consider and address when performing hiPSC-CM MEA studies. To improve reproducibility and data interpretation, MEA-based studies should establish a standardized protocol and report key experimental conditions (e.g., cell line, culture time, equilibration time, electrical stimulation settings, raw data values).

High-speed light-sheet fluorescent microscopy with an adaptive three-dimensional region-of-signal identification method

Light-sheet fluorescent microscopy (LSFM) is a crucial 3D imaging technique for tissues with advantages in high 3D spatial resolution, effective optical sectioning, and quick imaging. Prior to imaging, the sample's imaging depth, imaging region, and region of interest (ROI) division should all be established in accordance with the sample's outermost dimensions. However, because biological samples are inherently irregular a traditional imaging flow that images ROI by ROI, slice by slice, would waste a great deal of time and data storage space by additionally recording non-signal regions. In order to solve this problem, we create a high-speed LSFM with an adaptive three-dimensional region-of-signal identification method (RSI). We immediately perform crude imaging for each ROI before the software analyzes the longitudinal distribution and, ultimately, uses dimensionality reduction, binarization, filtering, and redundancy expansion to acquire the signal region result. By imaging two mouse spinal cord segments with a total of 170 ROIs and 180 ROIs on two channels, respectively, the high-speed imaging capability is validated. Our method reduced the overall imaging time and raw data size by more than treble and 14 times, respectively, without losing signal. With a programmable imaging range based on the real signal region, this method offers an efficient tool for flexible light-sheet microscopy. It can also be applied to other microscopy techniques to speed up imaging.

Single-shot diffusion trace spectroscopic imaging using radial echo planar trajectories.

Demonstrate the feasibility and evaluate the performance of single-shot diffusion trace-weighted radial echo planar spectroscopic imaging (Trace DW-REPSI) for quantifying the trace ADC in phantom and in vivo using a 3T clinical scanner. Trace DW-REPSI datasets were acquired in 10 phantom and 10 healthy volunteers, with a maximum b-value of 1601 s/mm2 and diffusion time of 10.75 ms. The self-navigation properties of radial acquisitions were used for corrections of shot-to-shot phase and frequency shift fluctuations of the raw data. In vivo trace ADCs of total NAA (tNAA), total creatine (tCr), and total choline (tCho) extrapolated to pure gray and white matter fractions were compared, as well as trace ADCs estimated in voxels within white or gray matter-dominant regions. Trace ADCs in phantom show excellent agreement with reported values, and in vivo ADCs agree well with the expected differences between gray and white matter. For tNAA, tCr, and tCho, the trace ADCs extrapolated to pure gray and white matter ranged from 0.18-0.27 and 0.26-0.38 μm2/ms, respectively. In sets of gray and white matter-dominant voxels, the values ranged from 0.21 to 0.27 and 0.24 to 0.31 μm2/ms, respectively. The overestimated trace ADCs from this sequence can be attributed to the short diffusion time. This study presents the first demonstration of the single-shot diffusion trace-weighted spectroscopic imaging sequence using radial echo planar trajectories. The Trace DW-REPSI sequence could provide an estimate of the trace ADC in a much shorter scan time compared to conventional approaches that require three separate measurements.

Implementasi Intelejen Bisnis dengan Visualisasi Data Gaji dan Algoritma Linear Regresion

To understand these employment dynamics, it is important to explore how long Data Scientists typically work in this field and what sectors they are more likely to be involved in. This information is very important for companies looking to recruit Data Scientists or determine the average salary of their employees. With this activity, managers can more clearly know the employee's salary range based on their work experience and area of specialization. This gives managers a more precise tool for assessing compensation appropriate to the level of experience and expertise possessed by employees. Nowadays, the concept of Business Intelligence can be applied by all industrial sectors, as long as the industry has a database system to develop its business. Business Intelligence processes and analyzes large amounts of raw data and then displays it in a business report with interactive visuals, namely a dashboard. This research aims to contribute to the understanding of average salary predictions and provide valuable information for business owners to make informed decisions. The results show that linear regression is accurate and forms a linear regression model with an R-square result of 0.263429. The salary prediction calculation process is carried out by considering influencing factors, and the results are then presented visually using Power BI to provide business owners with more interactive information.

Study on circular scanning for cross-scale micro/nanoscratching machining

Large-scale scanning probe can facilitate fabricating cross-scale micro/nano structures. However, the processing of complex two-dimensional patterns usually encounters challenges including numerous machining feature points and probe jumps, which results in low efficiency, as well as poor machining quality. Therefore, a circular scanning method based on corner point extraction (CSCE) was proposed in this study for programming the probe path and reducing the number of probe jumps. Before the machining, the target structures or images were converted into raw data points through image processing, and then the data was refined by corner point extraction for obtaining the machining points. Subsequently, the machining points were arranged in the order of a circular scanning algorithm to get the probe path. Using CSCE, the probe jump was only 1 time for processing an H-shaped pattern. By comparison with the circular scanning method based on intersection point extraction (CSIE) for the H pattern, the number of machining points in CSCE was reduced from 82 to 12, and the machining time was shortened from 17.15 s to 3.17 s. Consequently, CSCE can enable efficient and high-quality fabrication of cross-scale micro/nanostructures.