Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to the State Explosion Problem: SOPC Case Study

The system on a programmable chip (SOPC) architecture is better than traditional central processing unit (CPU) + field-programmable gate array (FPGA) architecture. It forms an efficient coupling between processor software and programmable logic through an on-chip high-speed bus. The SOPC architecture is resource-rich and highly customizable. At the same time, it combines low power consumption and high performance, making it popular in the field of high reliability and other new industrial fields. The SOPC architecture system is complex and integrates multiple forms of intellectual property (IP). Because of this, the traditional dynamic test and the static test cannot meet the requirements for test depth. To solve the problem of verification depth, we should introduce formal verification. But there are some types of IP forms that formal tools cannot recognize. These include black box IP, encrypted IP, and netlist IP in the SOPC model. Also, the state space explosion caused by the huge scale of the SOPC model cannot be formally verified. In this paper, we propose a modeling method using SOPC architecture. The model solves the problem of formal tools not recognizing multi-form IPs. To compress the state space, we propose reducing SOPC variables and branch relationships based on verification properties. Then, we conduct a property verification experiment on the reduced SOPC model. The experiment result shows that the model can significantly reduce the verification time.
Multiscale Hjorth Descriptor on Epileptic EEG Classification

The electroencephalogram (EEG) examination provides information on the brain’s electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was developed. Automatic epileptic EEG recognition is an area of interest on which much research focuses. The complexity approach to EEG signal analysis is interesting to be used as feature extraction, referring to the nonlinear characteristics of the signal. This study proposed an automatic epileptic EEG classification method based on the multiscale Hjorth descriptor measurement. EEG signals consisting of normal, interictal, and seizure (ictal) were simulated. The signal is scaled into new signals using the coarse-grained procedure on a scale of 1–20. Then, the Hjorth parameter which consists of activity, mobility, and complexity is calculated on the new signal. This process produces a feature vector that is used in the classification stage. Support vector machine (SVM) is used to evaluate the proposed feature extraction method. Simulation results showed that the Hjorth parameter on a scale of 1–15 yields 99.5% accuracy. The proposed method is expected to be applied to digital EEG for seizure detection and prediction.
Sampling Methods for Online Surveys

INTRODUCTION In the context of conducting surveys or collecting data, sampling is the selection of a subset of a larger population to survey. This chapter focuses on sampling methods for web and e-mail surveys, which taken together we call ‘online’ surveys. In our discussion we will frequently compare sampling methods for online surveys to various types of non-online surveys, such as those conducted by postal mail and telephone, which in the aggregate we refer to as ‘traditional’ surveys.
An efficient repeating signal detector to investigate earthquake swarms

Repetitive earthquake swarms have been recognized as key signatures in fluid injection induced seismicity, precursors to volcanic eruptions, and slow slip events preceding megathrust earthquakes. We investigate earthquake swarms by developing a Repeating Signal Detector (RSD), a computationally efficient algorithm utilizing agglomerative clustering to identify similar waveforms buried in years of seismic recordings using a single seismometer. Instead of relying on existing earthquake catalogs of larger earthquakes, RSD identifies characteristic repetitive waveforms by rapidly identifying signals of interest above a low signal‐to‐noise ratio and then grouping based on spectral and time domain characteristics, resulting in dramatically shorter processing time than more exhaustive autocorrelation approaches. We investigate seismicity in four regions using RSD: (1) volcanic seismicity at Mammoth Mountain, California, (2) subduction‐related seismicity in Oaxaca, Mexico, (3) induced seismicity in Central Alberta, Canada, and (4) induced seismicity in Harrison County, Ohio. In each case, RSD detects a similar or larger number of earthquakes than existing catalogs created using more time intensive methods. In Harrison County, RSD identifies 18 seismic sequences that correlate temporally and spatially to separate hydraulic fracturing operations, 15 of which were previously unreported. RSD utilizes a single seismometer for earthquake detection which enables seismicity to be quickly identified in poorly instrumented regions at the expense of relying on another method to locate the new detections. Due to the smaller computation overhead and success at distances up to ~50 km, RSD is well suited for real‐time detection of low‐magnitude earthquake swarms with permanent regional networks.
Enhanced C-V2X Mode-4 With Virtual Cell, Resource Usage Bitmap, and Smart Roaming

Successful exchanges of cooperative awareness messages (CAMs) among neighboring vehicles are essential in the intelligent transportation system (ITS) to ensure safe autonomous driving. Especially in cellular vehicle-to-everything (C-V2X) mode-4, where the resource allocation process in the vehicles is autonomous without centralized control, efficient resource selection to minimize collisions is necessary to enhance the CAM delivery ratio (CDR). In this paper, we propose an enhanced operation of LTE C-V2X mode-4 by using virtual cells (VCs) with orthogonal resource allocation, extended master information block (EMIB) with a resource usage bitmap (RuBMP), and smart roaming in the virtual cell boundary. We propose a virtual cell manager (VCM) that monitors resource usage via CAM broadcasts from vehicles, configures the RuBMP to show the overall resource block usage, includes it in EMIB messages, and broadcasts the EMIB every 10ms. The periodic broadcast of the EMIB with the RuBMP enables vehicles to avoid selection of resources currently used by other vehicles, and minimizes collisions during CAM broadcasts. Smart roaming enables seamless, successful CAM broadcasts even at the boundary of virtual cells where resources (subchannels and subframes) must be re-selected based on the state of the next virtual cell. The analytical model of the proposed scheme (including successful CDR) is provided, and the performances are analyzed with NS-3 simulations for overlapped virtual cells in metropolitan areas with high densities of vehicles. The proposed scheme shows a 99.2% (or more) successful CAM delivery ratio for overlapped cells with up to 400 vehicles per virtual cell using four LTE sidelink subchannels.
Influence of Permanent Magnetic Properties on Laser-Assisted Heating Magnetization for Magnetic MEMS

When magnetic microelectromechanical systems (MEMS) devices utilize thin- or thick-film permanent magnets, the uniform magnetization of these magnets, owing to their flat-shape, generate a strong antimagnetic field and weak surface magnetic flux density. This study proposes a laser-assisted micro multi-pole magnetization method that reduces the antimagnetic field and generates a high surface magnetic flux density. The proposed method involves locally reducing the coercivity through laser-assisted heating and an external reverse magnetic field to selectively reverse the magnetization in heated areas, thereby generating fine and different magnetic patterns on permanent magnets. This study experimentally investigates the relationship between the magnetic properties of twelve NdFeB magnet samples (residual flux density (<inline-formula> <tex-math notation="LaTeX">$\text{B}_{\mathrm {r}}$ </tex-math></inline-formula>):1.20-1.39 T and intrinsic coercivity (<inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {cj}}$ </tex-math></inline-formula>):955-2388 kA/m) magnetized using the proposed method and the resulting surface magnetic flux densities. Here, the magnetization ratio is introduced as the ratio of the measured to the analyzed surface magnetic flux density. The experimental results reveal that the magnetization ratio increases with an increase in <inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {cj}}$ </tex-math></inline-formula>. The highest achieved surface magnetic flux density was 391 mTp-p with a <inline-formula> <tex-math notation="LaTeX">$\text{B}_{\mathrm {r}}$ </tex-math></inline-formula> of 1.3 T and <inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {cj}}$ </tex-math></inline-formula> of 2388 kA/m. The magnetization ratio was 78.5%. Hence, a higher <inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {cj}}$ </tex-math></inline-formula> suppressed the demagnetization. The magnetization ratio peaks around <inline-formula> <tex-math notation="LaTeX">$\text{B}_{\mathrm {r}}=1.29$ </tex-math></inline-formula> T. A higher <inline-formula> <tex-math notation="LaTeX">$\text{B}_{\mathrm {r}}$ </tex-math></inline-formula> promotes magnetization reversal, thereby making demagnetization easier. To increase the magnetization ratio and surface magnetic flux density, a permanent magnet with the highest possible <inline-formula> <tex-math notation="LaTeX">$\text{H}_{\mathrm {cj}}$ </tex-math></inline-formula> and optimal <inline-formula> <tex-math notation="LaTeX">$\text{B}_{\mathrm {r}}$ </tex-math></inline-formula> for the magnetization state must be selected. This study has great potential for the development of magnetic MEMS devices.
Robust Superhydrophobicity through Surface Defects from Laser Powder Bed Fusion Additive Manufacturing

The robustness of superhydrophobic objects conflicts with both the inevitable introduction of fragile micro/nanoscale surfaces and three-dimensional (3D) complex structures. The popular metal 3D printing technology can manufacture robust metal 3D complex components, but the hydrophily and mass surface defects restrict its diverse application. Herein, we proposed a strategy that takes the inherent ridges and grooves’ surface defects from laser powder bed fusion additive manufacturing (LPBF-AM), a metal 3D printing process, as storage spaces for hydrophobic silica (HS) nanoparticles to obtain superhydrophobic capacity and superior robustness. The HS nanoparticles stored in the grooves among the laser-melted tracks serve as the hydrophobic guests, while the ridges’ metal network provides the mechanical strength, leading to robust superhydrophobic objects with desired 3D structures. Moreover, HS nanoparticles coated on the LPBF-AM-printed surface can inhibit corrosion behavior caused by surface defects. It was found that LPBF-AM-printed objects with HS nanoparticles retained superior hydrophobicity after 150 abrasion cycles (~12.5 KPa) or 50 cycles (~37.5 KPa). Furthermore, LPBF-AM-printed ships with superhydrophobic coating maintained great water repellency even after 10,000 cycles of seawater swashing, preventing dynamic corrosion upon surfaces. Our proposed strategy, therefore, provides a low-cost, highly efficient, and robust superhydrophobic coating, which is applicable to metal 3D architectures toward corrosion-resistant requirements.
Indicator-based monitoring and forecasting water deficit and surplus in agriculture in Poland

Abstract Indicator-based monitoring and forecasting water deficit and surplus in agriculture in Poland. Since 2012, the Institute of Technology and Life Sciences has been conducting national agrometeorological monitoring in rural areas of Poland. Current and forecasted information on the status and impact of water deficit and surplus in selected, representative agricultural ecosystems and potential reduction of crop yield are being provided annually in the period from April to October. The required meteorological data are provided by a network of automatic stations located in 13 regions on the Polish territory. Weather forecasts, necessary to develop predictions of water shortages or surplus in the next 10 and 20 days, come from the meteorological service of Meteo- Group Poland. Results of the monitoring and forecasts are presented as maps, graphs and tables on the website. Streszczenie Monitoring i prognozowanie niedoboru i nadmiaru wody w rolnictwie w Polsce metodą wskaźnikową. Od 2012 roku Instytut Technologiczno-Przyrodniczy prowadzi monitoring warunków opadowych w Polsce. System dostarcza aktualnych i prognostycznych informacji w okresie od kwietnia do października z sieci automatycznych stacji pomiarowych zlokalizowanych w 13 regionach Polski. Ocenę warunków opadowych dokonuje się z wykorzystaniem standaryzowanego wskaźnika opadów (SPI) w różnych skalach czasowych, od 1 to 48 miesięcy. Prognoza warunków pogody, niezbędna do prognozowania wskaźników niedoborów lub nadmiarów opadów na obszarze Polski w kolejnych 10 i 20 dniach. jest dostarczana przez serwis meteorologiczny MeteoGroup Poland. Wyniki monitoringu i prognozy są prezentowane w postaci map na stronach internetowych.
Enhancing Photocatalytic Properties of TiO2 Photocatalyst and Heterojunctions: A Comprehensive Review of the Impact of Biphasic Systems in Aerogels and Xerogels Synthesis, Methods, and Mechanisms for Environmental Applications

This review provides a detailed exploration of titanium dioxide (TiO2) photocatalysts, emphasizing structural phases, heterophase junctions, and their impact on efficiency. Key points include diverse synthesis methods, with a focus on the sol-gel route and variants like low-temperature hydrothermal synthesis (LTHT). The review delves into the influence of acid-base donors on gelation, dissects crucial drying techniques for TiO2 aerogel or xerogel catalysts, and meticulously examines mechanisms underlying photocatalytic activity. It highlights the role of physicochemical properties in charge diffusion, carrier recombination, and the impact of scavengers in photo-oxidation/reduction. Additionally, TiO2 doping techniques and heterostructures and their potential for enhancing efficiency are briefly discussed, all within the context of environmental applications.
Theoretical Analysis on Soft Soil Foundation Long-term Settlement for Storage Yard Engineering

: This paper analysed the long-term settlement of storage yard engineering located at Tianjin Nanjiang Port. The long-term settlement was calculated based on “Code for Foundation of Port Engineering”, and the predicted result is compared with the in-site monitoring data. The comparison results show that the long-term settlement calculation results based on code should be modified before using for settlement prediction. The modify factor is 1.1 to 1.3 for Tianjin area.
Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Abstract Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2×)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted.
Content enrichment with expressive document modelling to leverage the understanding of unstructured data

Most information in an enterprise is in the form of unstructured data which is usually managed using a document database. One of the key challenges is to define a generalized data model for this unstructured data and any information extracted from it using content enrichment algorithms. It is more challenging to incorporate provenance and temporal capabilities to such data models. Semantic databases use ontologies such as PROV-O to represent their provenance information expressively, and relational databases use for example Slowly Changing Dimensions (SCDs) concepts to represent temporal information. In this paper, we present a document model which has features inspired from Dublin core, PROV-O and temporal methodologies to generalize information extracted from unstructured data using content enrichment algorithms. Provenance information enables comparison of enrichment models, allows reproducibility and facilitates complex filtering on the enriched data. Temporal metadata helps in versioning the document and enables point-intime and history queries conveniently.
Full‐Field Numerical Simulation of Halite Dynamic Recrystallization From Subgrain Rotation to Grain Boundary Migration

Full‐field numerical modeling is a useful method to gain understanding of rock salt deformation at multiple scales, but it is quite challenging due to the anisotropic and complex plastic behavior of halite, together with dynamic recrystallization processes. This contribution presents novel results of full‐field numerical simulations of coupled dislocation glide and dynamic recrystallization of halite polycrystalline aggregates during simple shear deformation, including both subgrain rotation and grain boundary migration (GBM) recrystallization. The results demonstrate that the numerical approach successfully replicates the evolution of pure halite microstructures from laboratory torsion deformation experiments at 100–300°C. Temperature determines the competition between (a) grain size reduction controlled by dislocation glide and subgrain rotation recrystallization (at low temperature) and (b) grain growth associated with GBM (at higher temperature), while the resulting crystallographic preferred orientations are similar for all cases. The relationship between subgrain misorientation and strain follows a power law relationship with a universal exponent of 2/3 at low strain. However, dynamic recrystallization causes a progressive deviation from this relationship when strain increases, as revealed by the skewness of the subgrain misorientation distribution. A systematic investigation of the subgrain misorientation evolution shows that strain or temperature prediction from microstructures requires careful calibration.
A New Deep Learning Algorithm for Detecting Spinal Metastases on Computed Tomography Images

Study Design. Retrospective diagnostic study. Objective. To automatically detect osteolytic bone metastasis lesions in the thoracolumbar region using conventional computed tomography (CT) scans, we developed a new deep learning (DL)-based computer-aided detection model. Summary of Background Data. Radiographic detection of bone metastasis is often difficult, even for orthopedic surgeons and diagnostic radiologists, with a consequent risk for pathologic fracture or spinal cord injury. If we can improve detection rates, we will be able to prevent the deterioration of patients’ quality of life at the end stage of cancer. Materials and Methods. This study included CT scans acquired at Tokyo Medical and Dental University (TMDU) Hospital between 2016 and 2022. A total of 263 positive CT scans that included at least one osteolytic bone metastasis lesion in the thoracolumbar spine and 172 negative CT scans without bone metastasis were collected for the datasets to train and validate the DL algorithm. As a test data set, 20 positive and 20 negative CT scans were separately collected from the training and validation datasets. To evaluate the performance of the established artificial intelligence (AI) model, sensitivity, precision, F1-score, and specificity were calculated. The clinical utility of our AI model was also evaluated through observer studies involving six orthopaedic surgeons and six radiologists. Results. Our AI model showed a sensitivity, precision, and F1-score of 0.78, 0.68, and 0.72 (per slice) and 0.75, 0.36, and 0.48 (per lesion), respectively. The observer studies revealed that our AI model had comparable sensitivity to orthopaedic or radiology experts and improved the sensitivity and F1-score of residents. Conclusion. We developed a novel DL-based AI model for detecting osteolytic bone metastases in the thoracolumbar spine. Although further improvement in accuracy is needed, the current AI model may be applied to current clinical practice. Level of Evidence. Level III.
The Dual-Mode Integration of Power Amplifier and Radio Frequency Switch Based on GaN Dual-Gate HEMTs

In this paper, an integrated device which realized the dual-mode integration of power amplifier (PA) and radio frequency (RF) switch based on GaN dual-gate (DG) structure is designed and fabricated. The integrated device provides two working modes and meets the performance requirements of PA and RF switch. In the transmit (Tx) mode, the integrated device is used as PA. At the frequency of 3.6 GHz, compared with the conventional GaN HEMT (high electron mobility transistor) as PA, due to the field modulation effect of the dual-gate structure, the current collapse is effectively suppressed, and the output power density $(P_{out})$ of the integrated device is increased from 6.90 W/mm to 7.85 W/mm, and the power added efficiency (PAE) is increased from 44.3% to 51.1%. In the receive (Rx) mode, the integrated device is used as RF switch. Compared with conventional RF switch, the isolation of integrated devices is significantly improved. At the frequency of 3.6GHz, the isolation is improved from 19.4 dB to 41.0 dB; At the frequency of 40 GHz, the isolation is improved from 3.0 dB to 29.7 dB. The integrated device provides a novel function integration scheme for RF frontends, with great potential for improving the integration and dimension of future communication systems.