Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Contextual Filtered Features for Better Smartphone-User Receptivity Prediction

For the successful engagement of users with incoming interventions, the delivery of just-in-time (JIT) (or opportune moment–OM) interventions is of the utmost importance. This can be accomplished with a machine learning (ML) model that utilizes user context data. Smartwatch and smartphone interactions further open the space for further improvement by considering the user’s state and environmental information. In this work, a novel feature engineering approach for predicting smartphone user receptivity to JIT interventions is proposed. Proposed approach utilizes rich information from user–smartphone interactions and smartwatch sensor data to extract contextually filtered features (CFFs). The superiority of the proposed feature engineering method is demonstrated by developing an ML model that predicts a participant’s receptivity prior to administering an experience sampling method (ESM) questionnaire. The proposed approach is evaluated using the KEmoPhone data set, which contains 3334 ESM answers collected over the course of one week from 73 participants. To reduce the bias that may result in selecting specific classifier, multiple classifiers are tested and their average ROC-AUC metrics are compared. The results show that the proposed feature engineering method—CFFs improves the prediction performance, achieving an ROC-AUC of 56.5% as opposed to 54.7% obtained using conventional features (statistical moments of time series over an 80 min window). Such superior performance is consistent across multiple ML classification algorithms. Full research code will be released in Jupyter Notebooks for facilitating the research in the domain at https://github.com/Jumabek/receptivity upon the publication of this research work.
GSGNet-S*: Graph Semantic Guidance Network via Knowledge Distillation for Optical Remote Sensing Image Scene Analysis

In recent years, optical remote sensing image (ORSI) scene analysis has attracted increasing interest. However, existing networks show a trend of bifurcation. Lightweight networks have very high inference speed but poor inference of contextual information in highly complex backgrounds. In contrast, networks with high-performance contextual information reasoning capability require many parameters and are computationally expensive. Since the knowledge distillation (KD) method can greatly lighten the model, we propose a graph semantic guided network (GSGNet) that utilizes knowledge refinement for ORSI scenario analysis, which has a high inference speed while maintaining practical contextual inference capability. Rich semantic and detailed information facilitates the semantic segmentation of ORSIs. We design adjacent dynamic capture (ADC) and local–global map inference modules that can effectively extract low-level spatial details and high-level contextual semantics. To improve the attention map relearning (AR) performance of the distillation method, we designed semantically guided fusion modules (SGFMs) to locate spatial information and refine edge information. We also employed a structural relationship transfer (SRT) distillation method in which the structural relationship knowledge of the teacher model (GSGNet-T) was used to guide the student model (GSGNet-S). We compared the performances of GSGNet-T and the GSGNet-S with KD (GSGNet-S*) with those of several state-of-the-art (SOAT) methods on the Vaihingen and Potsdam datasets. Extensive experiments showed that GSGNet-S* outperformed most advanced methods with only 19.61 M parameters and a computation cost of 2.9 GFLOPs. The experimental results and code of our network can be accessed at the following URL: https://github.com/LYZ00918/GSGNet-KD.
A new Lower Ordovician species of Calathium, and skeletal structure of western Utah calathids

Conical and vase-shaped calathids are found in the Lower Ordovician Fillmore Formation of western Utah associated with intraformational conglomerates and small patch reefs. Calathium yersini n. sp. exhibits patterns of both the inner and outer walls of calathid two-wall construction. The broadly annulate walls are constructed from meroms with fused proximal merom feet forming the inner wall. The reticulate-patterned outer wall is formed by interlocking stellate ribs at the distal end of each merom. Latitudinal (horizontal) ribs interlock adjacent merom ribs side by side at the tips of ribs while meridional (vertical) ribs overlap merom shaft to merom shaft. Inner and outer walls are perforate. Pore-canal casts preserved in silicified molds suggest an exit and entrance current circulation for inner and outer wall pores. Upper ends of the calathids are not preserved, but an open cup is interpreted from infill material and encrustation of the interior cup wall by epibionts. Epibionts commonly encrust and thicken Calathium outer walls, with the cyanobacterium Girvanella as the dominant encrusting organism. Calathium yersini n. sp. , among the earliest of receptaculitids, has a morphology suitable for water circulation consistent with that of filter-feeding organisms.
Demonstration of a High Repetition Rate Solid-State Switched Spiral Generator

Spiral generators can be used to generate high-voltage pulses on the order of singles to hundreds of nanoseconds with a single switch. They act as the energy storage, voltage multiplication, and sometimes as an RF source all in one compact package. This compact design makes them attractive for high voltage pulse generation in volume and weight sensitive applications. Previous work has primarily leveraged spark gap devices as an initiation switch for operation. Some limited work has explored using thyristors as a replacement for the spark gaps. This paper explores the use of wide bandgap field effect transistors as an initiation switch for the spiral generators to push the pulse repetition frequency (PRF) higher previously demonstrated. Silicon-Carbide (SiC) transistors are evaluated as closing switches for the spiral generator. Electrical characteristics of the transistors are analyzed and compared to traditional spark gaps. Operation of a 10-turn spiral generator is experimentally demonstrated to step up SOO V input voltage to 10 kV at a 100 kHz PRF. Commentary on driving the spiral generator to higher voltage and PRF is presented.
Object-based detection of vehicles in airborne data

Robust detection of vehicles in airborne data is a challenging task since a high variation in the object signatures – depending on data resolution – and often a small contrast between objects and background lead to high false classification rates and missed detections. Despite these facts, many applications require reliable results which can be obtained in a short time. In this paper, an object-based approach for vehicle detection in airborne laser scans (ALS) and photogrammetrically reconstructed 2.5D data is described. The focus of this paper lies on a robust object segmentation algorithm as well as the identification of features for a reliable separation between vehicles and background (all nonevehicle objects) on different scenes. The described method is based on three consecutive steps, namely, object segmentation, feature extraction and supervised classification. In the first step, the 2.5D data is segmented and possible targets are identified. The segmentation progress is based on the morphological top-hat filtering, which leaves areas that are smaller than a given filter size and higher (brighter) than their surroundings. The approach is chosen due to the low computational effort of this filter, which allows a fast computation even for large areas. The next step is feature extraction. Based on the initial segmentation, features for every identified object are extracted. In addition to frequently used features like height above ground, object area, or point distribution, more complex features like object planarity, entropy in the intensity image, and lineness measures are used. The last step contains classification of each object. For this purpose, a random forest classifier (RF) using the normalized features extracted in the previous step is chosen. RFs are suitable for high dimensional and nonlinear problems. In contrast to other approaches (e.g. maximum likelihood classifier), RFs achieves good results even with relatively small training samples.
Analysis and Design of 6.78M WPT Architecture for Configurable Power Applications Based on DCX Energy Synthesis and Functional Execution

Improving the power level of high-frequency wireless power transfer (WPT) is important research. In this process, how to make the system have high-efficiency energy conversion, strong robustness, and power adjustment capabilities is a key technical difficulty. In this letter, a configurable WPT system architecture with multiple fixed energy state (DCX) channels combined with functional execution (FTE) channels is proposed. With the cooperation of the FTE branch, the state of the DCX branch is effectively locked so as to complete the ultimate efficiency optimization. The flexible adjustment of the power level is realized through the configurable combination of DCX branches. Meanwhile, the robustness improvement and power fluctuation of the system are undertaken by the FTE branch, and the constant output is also supported. Furthermore, a 178-W experimental prototype was built, and the overall efficiency reached 93.64%.
Margin-Maximized Hyperspace for Fault Detection and Prediction: A Case Study With an Elevator Door

This study proposes a practical fault detection and prediction method by addressing a margin-maximized hyperspace. The proposed method is effective for a highly imbalanced dataset without any supervision, which is a frequently occurring and challenging problem in real-world applications. The proposed method has three characteristics. First, knowledge-based feature manipulation is executed to provide sufficient information for a neural network. Second, a regulated variational autoencoder transforms distinct input features into a latent space, which ensures high accuracy and robustness. Third, the obtained latent space is confirmed to statistically allocate two extremes of major (normal) and minor (faulty) clusters at an origin and unity, maximizing the sensitivity to classify faults. The effectiveness of the proposed method is demonstrated through field measurements of elevator door-strokes and showed high sensitivity to separate each cluster along with locational constancy compared to other autoencoders. Therefore, the proposed method is effective for real-world applications with scarce fault measurements.
Growth Hormones in Broad Bean (Vicia faba L.) and Radish (Raphanus raphanistrum subsp. sativus L.) Are Associated with Accumulated Concentrations of Perfluoroalkyl Substances

In this study, we grew radish (Raphanus raphanistrum subsp. sativus L.) and broad beans (Vicia faba L.) in a greenhouse on soils spiked with a mixture of 15 per- and polyfluoroalkyl substances (PFASs) and investigated the association between accumulated ∑PFAS concentrations, growth, and hormone levels. Short-chained PFASs dominated aboveground tissues, whereas long-chained PFASs were most abundant in the plant roots. Our results showed that the presence or absence of exodermal Casparian strips, as well as the hydrophobicity and anion exchange capacities of PFASs, could explain the translocation of PFASs within plants. Significant associations found between accumulated PFAS concentrations and levels of gibberellins (GA1 and GA15), methionine, and indole-3-acetic acid (IAA) imply potential effects of PFASs on plant development and growth. This study provides the first evidence of associations between PFAS accumulation in plants and growth hormone levels, possibly leading to growth reduction of the apical dome and effects on the cell cycle in pericycle cells and methionine metabolism in plants.
The Role of Migration in Women Entrepreneurship and Empowerment: Evidence from Nigeria

The research examines the relationship between the migration of men and the empowerment of women who remain in the households. The study looks at Nigeria – a Sub-Saharan African country with the highest migration outflows and prevalent gender inequality. The core research question is to examine whether the migration of men affects the entrepreneurship and empowerment of Nigerian women. For the purpose of this study, private entrepreneurship will state the employment status of women from both migrant and non-migrant households while the amount of housework and degree of decision-making power will constitute empowerment. The data is obtained from Nigerian General Household Survey 2018-2019. The sample used in the current analysis consists of 12,199 women, 15 years and older. The Ordinary Least Squares model is applied to assess the changes men’s migration might bring to housework of women who remain in the household. Logit regression addresses the entrepreneurship and decision-making power of Nigerian women. Probit regression serves as a robustness check for Logit, and as a separate econometric model. The findings generally support the pre-experiment expectations: migration of men decreases the amount of housework of Nigerian women, encourages them to run businesses, but reduces their decision-making power.
Kinetic characterization of low-velocity positive collision of double droplets

Droplet positive collision is a complex process involving heat transfer in gas-liquid-solid three-phase flow and droplet collide dynamics. In order to study the kinetic behavior of droplets under positive collision in low velocity (v<2 m/s), this paper focuses on the spreading, vibration and fracture characteristics of double droplets by numerical simulation. First, the accuracy of the model is verified by experimental comparison. The effects of droplet diameters, collision velocities and wall contact angles on the spreading process are analyzed, and the spreading factor curves are plotted. Then, the droplet rebound vibration after collision fusion is equated to a single-degree-of-freedom damped vibration system, and the peak vibration height variation curve of the fused droplet is obtained by nonlinear fitting. Considering the droplet phase change, the influence law of different conditions on the vibration damping factor and vibration time of the fused droplet is studied. Finally, it is found that rebound fracture and spreading fracture occur after the fusion of double droplets under positive collision, and the critical values of collide velocity required for the occurrence of the above phenomenon are found. To provide a reliable theoretical basis for the study of heat and mass transfer processes after multiple droplets collide the wall.
Are polarised elections the hardest to deliver? Explaining global variations in electoral management body performance

ABSTRACT Electoral management, understood as the application and implementation of electoral rules, is a critical part of democratic governance. But there are often concerns about the quality of electoral management and the performance of electoral management bodies around the world. Despite recent advances, there remains a need for new systematic evidence on the quality of electoral management and analysis of the factors that lead to poorly- or well- run elections. This article therefore maps out global variations in the quality of the public management of elections using a new cross-national dataset and measure. It then explains variations by evaluating the relative importance of bureaucratic culture, the autonomy of electoral authorities, political polarisation in the electorate and the capacity of electoral management bodies. The results provide support for the importance of each of these factors. The effect of political polarisation is an important finding as it is a new threat to elections.
Monocular camera-based large field-of-view depth estimation for mobile machining of large aerospace components

In the integrated processing of large components in the aerospace field, for the end servo industrial robot positioning accuracy needs, often using binocular vision positioning method. This method can accurately measure the end position but is limited by the restricted field of view and small depth of field. So, a monocular camera is needed for servo-guiding the processing end of the robot arm. Therefore, a monocular depth estimation method based on improved Yolov8 and CNN fusion for mobile machining with large field-of-view is proposed in this paper. Firstly, a dataset construction method based on virtual-real fusion is proposed to solve the problem that the depth information corresponding to the training set images is difficult to measure; secondly, the proposed Yolov8sim-CNN cascade neural network can realize the measurement of fast localization and depth prediction of the target machining workpiece and realize the servo-guidance of the robot arm end. The experimental results show that the proposed Yolov8sim-CNN network can ensure high detection accuracy, and the detection accuracy is substantially improved compared with the method of CNN-only, which indicates that the proposed method has better fitting ability and higher accuracy.
Center Volumes Correlate with Likelihood of Stent Implantation in German Coronary Angiography

Aims Literature on percutaneous coronary intervention (PCI) stated an inverse relationship between hospital volume and mortality, but the effects on other characteristics are unclear. Methods Using German national records, all coronary angiographies with coronary artery disease in 2017 were identified. We applied risk-adjustment to account for differences in population characteristics. Results Of overall 528,188 patients, 55.22% received at least one stent, with on average 1.01 stents implanted in all patients. Based on those patients who received at least one stent, this corresponds to an average number of 1.82 stents. In-hospital mortality across all patients was 2.93%, length of hospital stay was 6.46 days, and mean reimbursement was €5,531. There were comparatively more emergency admissions in low volume centers and more complex cases (3-vessel disease, left main stenosis, and in-stent stenosis) in high volume centers. In multivariable regression analysis, volume and likelihood of stent implantation (p=0.003) as well as number of stents (p=0.020) were positively correlated. No relationship was seen for in-hospital mortality (p=0.105), length of stay (p=0.201), and reimbursement (p=0.108). Nonlinear influence of volume suggests a ceiling effect: In hospitals with ≤100 interventions, likelihood and number of implanted stents are lowest (∼34% and 0.6). After that, both rise steadily until a volume of 500 interventions. Finally, both remain stable in the categories of over 500 interventions (∼60% and 1.1). Conclusion In PCI, lower volume centers contribute to emergency care. Higher volume centers treat more complex cases and show a higher likelihood of stent implantations, with a stable safety.
Stress Triggering of the 2012 Sumatra M w 8.2 Earthquake by the 2012 Sumatra M w 8.6 Earthquake

The Sumatra Mw 8.6 earthquake and the Sumatra Mw 8.2 earthquake occurred near the Sunda trench one after another in April 11, 2012. The two earthquakes are 2 hours and 180 kilometers apart. Based on the focal mechanism of the aftershock and finite fault model of the main earthquake, we inverted the coulomb failure stress change after the main shock. We calculated the coulomb stress changes based on Gavin Hayes and Guangfu Shao’s respectively. Both of the results show that the Mw 8.2 earthquake occurred in the increased area of coulomb stress induced by the Mw 8.6 earthquake. The coulomb failure stress increased about 5.469-13.348bar in the source of the Mw 8.2 earthquake. The coulomb stress change is far more than the stress triggering threshold. Therefore, the Sumatra Mw 8.2 earthquake was probably triggered by the coulomb failure stress change.
Choline and Betaine Levels in Plasma Mirror Choline Intake in Very Preterm Infants

Choline is essential for cell membrane formation and methyl transfer reactions, impacting parenchymal and neurological development. It is therefore enriched via placental transfer, and fetal plasma concentrations are high. In spite of the greater needs of very low birth weight infants (VLBWI), choline content of breast milk after preterm delivery is lower (median (p25–75): 158 mg/L (61–360 mg/L) compared to term delivery (258 mg/L (142–343 mg/L)). Even preterm formula or fortified breast milk currently provide insufficient choline to achieve physiological plasma concentrations. This secondary analysis of a randomized controlled trial comparing growth of VLBWI with different levels of enteral protein supply aimed to investigate whether increased enteral choline intake results in increased plasma choline, betaine and phosphatidylcholine concentrations. We measured total choline content of breast milk from 33 mothers of 34 VLBWI. Enteral choline intake from administered breast milk, formula and fortifier was related to the respective plasma choline, betaine and phosphatidylcholine concentrations. Plasma choline and betaine levels in VLBWI correlated directly with enteral choline intake, but administered choline was insufficient to achieve physiological (fetus-like) concentrations. Hence, optimizing maternal choline status, and the choline content of milk and fortifiers, is suggested to increase plasma concentrations of choline, ameliorate the choline deficit and improve growth and long-term development of VLBWI.
A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms’ role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network’s security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network’s underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security.