Miracle-dz/UniFAR-FTUs huggingface dataset:
Flare-Related Changes of an Active Region Magnetic Field

It was shown recently that current helicity, calculated using the photospheric magnetic field vector measurements, possesses a well-pronounced scaling behavior. The sign singularity of two-dimensional structures of current helicity, hc = Bz ∙ (𝛁 ×)z, can be studied by introducing a signed measure and by calculating the power-law exponent, the cancellation exponent κ. The time variations of the cancellation exponent seem to be related to flare activity of an active region (AR). Periods of enhanced flaring are accompanied by a drop and subsequent rise of the cancellation exponent. Here we focus on the changes in the vortex structure of the photospheric magnetic field during the transition of an active region from low flaring to enhanced flaring state. We analyzed variations of the cancellation exponent, helicity imbalance, and total electric current in four flaring active regions and one quiet one. We show that the transition of an active region from a low flaring state to an enhanced one is always accompanied (in this study, the corresponding time interval is less than 2 hr) by the 30%-45% decrease of the cancellation exponent. In two active regions, a reliable 13%-22% decrease of the total electric current took place, and in three active regions the helicity imbalance changed. This, possibly, implies a reinforcement of the anticoriolis twist of the whole magnetic configuration. For comparison, the decrease of κ in the quiet active region does not exceed 10%. This can be interpreted as a real preflare reorganization of the vortex structure of the photospheric magnetic field: a combination of the exhausting of small-scale eddies along with the reinforcement of the total anticoriolis twist of the magnetic structure.
“You have to believe the patient”: What do people with fibromyalgia find helpful (and hindering) when accessing health care?

ABSTRACT Background Fibromyalgia (FM) is a complex, still poorly understood, and difficult-to-treat chronic pain condition for which many people struggle to find adequate care. Aims This study investigated the research question, “What do people accessing health care services for fibromyalgia perceive as helpful, hindering, and absent but desired? ” with the aim of identifying clear, implementable changes for clinical practice. Materials and methods This study used the enhanced critical incident technique (ECIT), a qualitative research method that focuses on helping, hindering, and desired factors, to explore the health care experiences of 14 individuals (12 women and 2 men) diagnosed with FM. Results Using qualitative data analysis, results identified three categories of health care experiences: (1) systemic navigation, including financial and economic security; accessibility, flexibility, and continuity of care; and diversity of treatment options; (2) clinician–patient alliance, including invalidation and prejudice; therapeutic bond; and clinician–patient alignment on treatment plan; and (3) patient self-management strategies, including information-seeking and education, self-advocacy, social supports, symptom management strategies, and other coping strategies. Participants tended to conceptualize their health care concerns as a multilayered systemic problem. Conclusions Participants described a medical system they perceived as poorly equipped to support their needs and tended to invalidate their health concerns. Helping experiences tended to be the result of unique efforts on the part of individual clinicians. Findings emphasize the importance of recognizing the complexities and psychological impact of pain, trusting clinician–patient relationships, multidisciplinary/interdisciplinary care within a biopsychosocial framework, and improved education and awareness around psychosocial aspects of FM and effective management of chronic pain.
A Monte Carlo modeling of electron interaction with solids including cascade secondary electron production

A new Monte Carlo simulation approach has been developed to describe electron scattering and secondary electron generation processes in solids. This approach is based on the uses of Mott's elastic scattering cross section and Penn's dielectric function. A very good agreement has been found on the energy distribution of backscattered electrons between theoretical calculations and accurate experimental measurement recently made by Goto et al. (1994). This fact confirms that the present Monte Carlo model is very useful for more comprehensive understanding of basic phenomena in electron spec-troscopy and microscopy, particularly in the sub-keV energy region where cascade secondary electrons play a dominant role. In this paper the details of the Monte Carlo procedure are described and further application to the mechanism of secondary electron generation is presented.
In Vitro Investigation of Material Combinations for Meso- and Suprastructures in a Biomimetic Approach to Restore One-Piece Zirconia Implants

The aim of this study was to find a suitable material combination to avoid cement excess in the marginal region of one-piece zirconia implant-supported restorations by means of a hybrid crown consisting of a meso- and a suprastructure. One-piece zirconia implants (n = 120) were embedded in epoxy resin. Microfilled resin composite mesostructures (n = 60), designed as caps, were bonded on the implant abutment with a primer only. A molar crown was constructed and cemented with a resin cement on top of the mesostructure as a suprastructure out of feldspar ceramic (n = 12), lithium-disilicate (n = 24), or zirconia (n = 24). Fracture load (n = 6) and retention force (n = 6) were measured immediately after storage in distilled water at 37 °C for 24 h, as well as after an additional exposure to artificial aging in a chewing simulator and simultaneous thermal cycling. For the measurement of the fracture load, monolithic crowns made of the employed restorative materials and identical in shape to the hybrid crowns served as controls (n = 6 each). Fracture load values for feldspar ceramic and lithium-disilicate hybrid crowns were slightly higher than those for the respective monolithic crowns at baseline and after aging, which was statistically significant only for feldspar crowns after aging. In contrast, fracture load values for zirconia monolithic crowns were higher than those for zirconia hybrid crowns, which was only statistically significant after aging. Artificial aging reduced the fracture load of feldspar and lithium-disilicate crowns both for hybrid and monolithic crowns. The effect was only statistically significant for lithium disilicate hybrid crowns. The fracture load for hybrid and monolithic zirconia crowns was increased by artificial aging without reaching statistical significance. The retention force of lithium-disilicate and zirconia hybrid crowns was not affected by artificial aging. Taking into account retention force and fracture load, lithium-disilicate hybrid crowns showed promising results.
The impact of feature selection techniques on effort-aware defect prediction: An empirical study

Effort ‐ Aware Defect Prediction (EADP) methods sort software modules based on the defect density and guide the testing team to inspect the modules with high defect density first. Previous studies indicated that some feature selection methods could improve the performance of Classification ‐ Based Defect Prediction (CBDP) models, and the Correlation ‐ based feature subset selection method with the Best First strategy (CorBF) performed the best. However, the practical benefits of feature selection methods on EADP performance are still unknown, and blindly employing the best ‐ performing CorBF method in CBDP to pre ‐ process the defect datasets may not improve the performance of EADP models but possibly result in performance degradation. To assess the impact of the feature selection techniques on EADP, a total of 24 feature selection methods with 10 classifiers embedded in a state ‐ of ‐ the ‐ art EADP model (CBS + ) on the 41 PROMISE defect datasets were examined. We employ six evaluation metrics to assess the performance of EADP models comprehensively. The results show that (1) The impact of the feature selection methods varies in classifiers and datasets. (2) The four wrapper ‐ based feature subset selection methods with forwards search, that is, AdaBoost with For-wards Search, Deep Forest with Forwards Search, Random Forest with Forwards Search, and XGBoost with Forwards Search (XGBF) are better than other methods across the studied classifiers and the used datasets. And XGBF with XGBoost as the embedded classifier in CBS + performs the best on the datasets. (3) The best ‐ performing CorBF method in CBDP does not perform well on the EADP task. (4) The selected features vary with different feature selection methods and different datasets, and the features noc (number of children), ic (inheritance coupling), cbo (coupling between object classes), and cbm (coupling between methods) are frequently selected by the four wrapper ‐ based feature subset selection methods with forwards search. (5) Using AdaBoost, deep forest, random forest
Polymeric Interlayer in CdS-Free Electron-Selective Contact for Sb2Se3 Thin-Film Solar Cells

High open-circuit voltage in Sb2Se3 thin-film solar cells is a key challenge in the development of earth-abundant photovoltaic devices. CdS selective layers have been used as the standard electron contact in this technology. Long-term scalability issues due to cadmium toxicity and environmental impact are of great concern. In this study, we propose a ZnO-based buffer layer with a polymer-film-modified top interface to replace CdS in Sb2Se3 photovoltaic devices. The branched polyethylenimine layer at the ZnO and transparent electrode interface enhanced the performance of Sb2Se3 solar cells. An important increase in open-circuit voltage from 243 mV to 344 mV and a maximum efficiency of 2.4% was achieved. This study attempts to establish a relation between the use of conjugated polyelectrolyte thin films in chalcogenide photovoltaics and the resulting device improvements.
Analysis of the Influence of Dynamic Capabilities on Company Performance Mediated By Competitive Advantage

This research has a purpose to find out how the influence of Dynamic Capabilities, either directly or indirectly on Company Performance through Competitive advantage as a mediating variable. Design / methodology / approach – data collection in the form of distributing questionnaires to collect a sample of 309 manufacturing companies in Indonesia. In any case, to analyze the data, the analytical method used is the Structural Equation Model (SEM) to assign the phenomenon and the tool used is AMOS 22. The result of this research is that Dynamic Capabilities, affect the upgrade in Competitive Advantage significantly and positively. Dynamic Capabilities, affect the positive and significant upgrade in Company Performance. Competitive Advantage affects the Company's performance improvement positively and significantly. The mediating influence of Competitive advantage upgrades the influence of Dynamic Capabilities, on improving Company Performance. Practical implications – this research has shown that Dynamic Capabilities have affected Company Performance through the practice of Competitive Advantage. In any case, This research describes that the company's performance is influenced by different competitive priorities as well as internal capabilities and external capabilities. Originality / value - This research analyzes sustainable innovation predicated on dynamic capabilities in manufacturing companies in Indonesia, by collecting managers' perceptions of modification in the external environment that affect the adaptation and alignment of the company's maneuver, which has an impact on the applied business model.
Likelihood-Based Cointegration Tests in Heterogeneous Panels

This paper presents a likelihood-based panel test of cointegrating rank in heterogeneous panel models based on the mean of the individual rank trace statistics. The existence of the first two moments of the asymptotic distribution of the individual trace statistic is established. Based on this, the asymptotic distribution of the test statistic is shown to be normal. The small-sample size and power properties are investigated using Monte Carlo simulations. An empirical example for a consumption model including consumption, income and inflation is estimated for 23 OECD countries over the period 1960-1994. The results indicate that two cointegrating relations exist in the system: One containing consumption and income and one inflation only.
Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design

Abstract Surveys of consumer intentions to buy are inefficient predictors of purchase rates because they do not provide accurate estimates of mean purchase probability. This is a consequence of the fact that intentions surveys cannot detect movements in mean probability among nonintenders, who account for the bulk of actual purchases and for most of the time-series variance in purchase rates. Comparison of predictions from alternative surveys, one of subjective purchase probabilities and the other of buying intentions, indicates that purchase probabilities explain about twice as much of the cross-section variance in automobile purchase rates as buying intentions. Similar but not quite so conclusive differences are obtained from analysis of selected household durables. The probability variable predicts more accurately than the intentions variable largely because it divides nonintenders, and those who report that they “don't know” about their buying intentions, into subgroups with systematically different p.
XACML for Mobility (XACML4M)—An Access Control Framework for Connected Vehicles

The automotive industry is experiencing a transformation with the rapid integration of software-based systems inside vehicles, which are complex systems with multiple sensors. The use of vehicle sensor data has enabled vehicles to communicate with other entities in the connected vehicle ecosystem, such as the cloud, road infrastructure, other vehicles, pedestrians, and smart grids, using either cellular or wireless networks. This vehicle data are distributed, private, and vulnerable, which can compromise the safety and security of vehicles and their passengers. It is therefore necessary to design an access control mechanism around the vehicle data’s unique attributes and distributed nature. Since connected vehicles operate in a highly dynamic environment, it is important to consider context information such as location, time, and frequency when designing a fine-grained access control mechanism. This leads to our research question: How can Attribute-Based Access Control (ABAC) fulfill connected vehicle requirements of Signal Access Control (SAC), Time-Based Access Control (TBAC), Location-Based Access Control (LBAC), and Frequency-Based Access Control (FBAC)? To address the issue, we propose a data flow model based on Attribute-Based Access Control (ABAC) called eXtensible Access Control Markup Language for Mobility (XACML4M). XACML4M adds additional components to the standard eXtensible Access Control Markup Language (XACML) to satisfy the identified requirements of SAC, TBAC, LBAC, and FBAC in connected vehicles. Specifically, these are: Vehicle Data Environment (VDE) integrated with Policy Enforcement Point (PEP), Time Extensions, GeoLocation Provider, Polling Frequency Provider, and Access Log Service. We implement a prototype based on these four requirements on a Raspberry Pi 4 and present a proof-of-concept for a real-world use case. We then perform a functional evaluation based on the authorization policies to validate the XACML4M data flow model. Finally, we conclude that our proposed XACML4M data flow model can fulfill all four of our identified requirements for connected vehicles.
The classification of simply connected biquotients of dimension at most 7 and 3 new examples of almost positively curved manifolds

We classify all compact 1-connected manifolds $M^n$ for $2 \leq n leq 7$ which are diffeomorphic to biquotients. Further, given that $M$ is diffeomorphic to a biquotient, we classify the biquotients it is diffeomorphic to. Finally, we show the homogeneous space $Sp(3)\Sp(1) \tines Sp(1)$ and two of its quotients $Sp(3)\Sp(1) \times Sp(1) \times S^1$ and $\delta S^1 \backslash Sp(3)/Sp(1)\times Sp(1)$ admit metrics of almost positive curvature. iv Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Mathematics First Advisor Wolfgang Ziller Subject Categories Geometry and Topology This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/311 THE CLASSIFICATION OF SIMPLY CONNECTED BIQUOTIENTS OF DIMENSION AT MOST 7 AND 3 NEW EXAMPLES OF ALMOST POSITIVELY CURVED MANIFOLDS
Image library approach to evaluating parametric uncertainty in metrology of isolated feature width

When measuring the width of an isolated line or space on a wafer or photomask, only the feature's image is measured, not the object itself. Often the largest contributors to measurement uncertainty are the uncertainties in the parameters which affect the image. Measurement repeatability is often smaller than the combined parametric uncertainty. An isolated feature's edges are far enough away from nearest edges of other features that its image does not change if this distance is increased (about 10 wavelengths in an optical microscope or exposure tool, or several effective-beam-widths in a SEM). When the leading and trailing edges of the same feature are not isolated from each other the metrology process becomes nonlinear. Isolated features may not be amenable to measurement by grating methods (e.g., scatterometry), and there is no hard lower limit to how small an isolated feature can be measured. There are several ways to infer the size of an isolated feature from its image in a microscope (SEM, AFM, optical,.), and they all require image modeling. Image modeling accounts for the influence of all of the parameters which can affect the image, and relates the apparent linewidth (in the image) to the true linewidth (on the object). The values of these parameters, however, have uncertainties and these uncertainties propagate through the model and lead to parametric uncertainty in the linewidth measurement, along with the scale factor uncertainty and the measurement repeatability. The combined measurement uncertainty is required in order to decide if the result is adequate for its intended purpose and to ascertain if it is consistent with other similar results. The parametric uncertainty for optical photomask measurements derived using an edge threshold approach has been described previously [1]; this paper describes an image library approach to this issue and shows results for optical photomask metrology over a linewidth and spacewidth range of 10 nm to 4 μm. The principles will be described, the 1-dimensional image library used and the method of comparing images, along with a simple interpolation method, will be explained, and results presented. This method is easily extended to any kind of imaging microscope and to p dimensions, where p is the number of imaging parameters used. It is more general than the edge threshold method and leads to markedly different results for features smaller than a wavelength.
Agaricus bisporus Wild Mushroom Extract as Lectin Source for Engineering a Lactose Photoelectrochemical Biosensor

Agaricus bisporus mushroom biomass contains a lectin, ABL, with remarkable specificity for lactose biorecognition; in this work, this feature was explored to develop a photoelectrochemical biosensor. The high lectin activity found in saline extracts of this macrofungus (640 HU mL−1), even at critical pH values (4–10) and temperatures (20–100 °C), allowed its direct use as an ABL source. Theoretical and experimental evidence revealed favorable electrostatic and biocompatible conditions to immobilize ABL on a poly(methylene blue)/fluorine-doped tin oxide-coated glass platform, giving rise to the ABL/PMB/FTO biosensor. The conducting polymer added further photoactivity to the device, allowing the identification of lectin–carbohydrate interactions with even greater sensitivity. The dose–response curves studied by electrochemical impedance spectroscopy showed a sigmoidal profile that was well-fitted by Hill’s equation, expanding the working dynamic range (15–540 nmol L−1 lactose; 20.2 pmol L−1 detection limit) and avoiding undesirable sample dilution or preconcentration procedures. Under the optimized photoelectrochemical conditions, the ABL/PMB/FTO biosensor showed remarkable signal stability, accuracy, specificity, and selectivity to analyze lactose in commercial food products. This research raises interest in ABL-based biosensors and the added value of the crude Agaricus bisporus extract toward the development of greener and more sustainable biotechnological approaches.
Impact of Temperature-Induced Oxide Defects on HfxZr1−xO2 Ferroelectric Tunnel Junction Memristor Performance

In this work, we evaluate the importance of the postmetallization annealing (PMA) temperature on the performance of HfxZr1−xO2-based ferroelectric tunnel junctions (FTJs). Our results indicate a significant difference in tunneling electroresistance (TER) ratio and endurance, depending on the PMA temperature despite negligible variations in remanent polarization. We conclude that the minimization of conductive oxide defect states is central to achieve high performance. Through carefully optimized PMA conditions, we demonstrate FTJs with a TER = 3 and low mean cycle-to-cycle variation of < 1.5% combined with at least 16 separable conductance states providing a 4-bit resolution analog FTJ.
Review of Graph-Based Hazardous Event Detection Methods for Autonomous Driving Systems

Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges.