Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultra-Precise High-Speed Untethered Manipulation of Magnetic Scanning Microprobe in Aqueous Solutions

This article describes the design and development of an integrated system that renders ultra-precise and high-speed, untethered manipulation of a single magnetic scanning microprobe in aqueous solutions under a microscope. The system uses a six-input-three-output hexapole electromagnetic actuator to control the magnetic gradient force exerted on the probe and a three-dimensional vision-based motion tracking system to enable feedback control. The control system has three core functions that enables superior manipulation of the magnetic microprobe. The six-input-six-output closed-loop magnetic flux control significantly suppresses the effect of magnetic hysteresis and greatly increases the bandwidth of magnetic flux generation. The optimal flux allocation solves four issues in multipole magnetic force generation: redundancy; coupling; nonlinearity; and position-dependency. The position-dependent discrete-time motion control law determines the required force for stabilization and tracking. These three core functions have been implemented using a high-speed field programmable gate array (FPGA) system. Experiments have verified that the use of digital control technology, high-speed electronics, mathematical modeling, and real-time computation has achieved superior manipulation of a single magnetic microprobe in aqueous solutions.
Enhancing mechanical properties of 3D printed thermoplastic polymers by annealing in moulds

Five series of specimens with two different print orientations (−45/45 and 0/90) and two print layer thicknesses (0.1 and 0.2 mm) were made. In total 60 specimens with 100% filament infill were made. One specimen series (20 pieces) was isolated as a reference or thermally untreated. Before the thermal treatment (annealing), two specimen moulding methods were used: NaCl powder (granulation 63 mm: 20 pieces) and Calcium Sulphate (Gypsum: 20 pieces). During the annealing, specimens immersed in NaCl powder were heated in a drying oven to the filament melting point (for PLA: 200°C, with a duration interval of 30 min), while the treatment of the heated specimens in gypsum was performed at a temperature of around 190°C, with duration interval of 3 h with the observed temperature inside the mould of about 100°C. An ultrasonic bath and a drying oven were used in the gypsum treatment. Temperature measurement and control during both annealing treatments were performed using a thermal imaging camera, while the temperature control inside the drying oven was performed using a digital thermometer. After treatment, the specimens in the moulds were cooled at room temperature, and the dimensions of annealing and untreated specimens were controlled. Surface morphology was characterised using scanning electron microscopy (SEM). The SEM analysis reveals improved internal structure after heat treatment of the PLA specimens. These results show that the investigated specimens after heat treatments had better structural properties than the referent specimens. Tensile testing on a universal testing machine in compliance with the ASTM D638 standard was also performed. The referent PLA specimen with −45/45 and layer thickness of 0.1 mm had the highest tensile stress value (64.08 MPa) while the specimen with minimal tensile stress value before fracture was 0/90, 0.2 mm (54.81 MPa). Heat treatment in gypsum showed the most significant increase in strength with −45/45 (0.1 mm) being the strongest (71.66 MPa) while the strongest specimen treated in sodium chloride was −45/45 (0.1 mm) with maximum tensile stress of 70.08 MPa. The mechanical characteristics of the PLA were characterised using the Vickers microhardness tester. The PLA microhardness value was calculated according to standards ASTM E384 and ISO 6507. The referent PLA specimen with −45/45 (0.2 mm) orientation shows the maximal microhardness value (125 MPa), and the minimal microhardness value was observed for the 0/90 (0.1 mm) orientation specimens (108 MPa). The heat treatment specimens in gypsum have a better hardness (185 MPa) than those treated in gypsum (165 MPa), with microhardness increasing by about 12%. The essence of the work is reflected in the additional filament processing to achieve a better structural and mechanical performance of the materials and reduce the anisotropy that is characteristic of 3D printing.
The Factors that Affecting Compliance of Taxpayer in Submitting The Annual Notice (Case Study at Jambi City Primary Tax Service Office)

This study aims to prove the benefits of the E-filing system policy and the role of internet understanding on taxpayer compliance. The research Methods sample was 100 respondents who were in the area of Jambi City Primary Tax Service Office. The statistical tool in this study used WarpPLS version 7. The result showed that the E-filing system variable had a significant positive effect on taxpayer compliance and internet understanding also had a significant positive effect on taxpayer compliance.
Outcomes of Democratic Innovations: Horizontalism as Inclusive Participation Spillover, but Fragile under Hierarchical Attacks

Anti-austerity protests revived anti- and post-hegemonic sociopolitical imaginary, around the word. In Spain, 15M movement built the premises for political projects based on direct democracy and horizontal models of social organization, as defining features of the new muncipalists who won 2015 elections in several cities. The case of Madrid has revelatory value in the debate regarding outcomes of participatory innovations, as the alliance which pioneered digital democracy and multi-actor multi-level local governance lost elections of 2019. Although a relatively large body of research investigated the feasibility of democratic innovations by contrasting their designs and implementation processes onto the matrix of their objectives and underlying ideological principles, the literature explaining their outcomes is scarce. Few studies focus on factors modeling the sustainability of participatory democracy as city regime, and the most common approach stresses out neoliberal inhibitors, as structural breaks which limit the depth and quality of participation. The present study aims to contribute to filling this gap by extending the analytical framework to encompass factors related to horizontalism, as enablers of democracy innovations. It is concluded that while horizontal governance works as a spillover of inclusive participation, it is highly vulnerable to attacks designed to vertically restructure its working processes.
Overcoming the Challenges of Digitalisation in Hungarian Manufacturing Companies

The digitalisation has become an imperative for most organisations and their workforces in our world of emergent and continuous changes. Digital technologies are evolving at the speed of light, such as big data analytics, social media, artificial intelligence, internet of things, etc., and the platform built with these technologies. In this context, digitalisation refers to the adoption of these technologies for communications and business activities into digital ones connecting people, products, services and systems. Recently, studies began to emphasise the significant role of integrating human factors into digitalised workplaces and investigate deeply how well prepared they are — workforces and organisations — for the digitalisation challenges in the era of Industry 4.0. The aim of our research is to provide a survey-based result on digital skills of the workforce influenced by digitalisation and to support organisations to fulfil digitalisation challenges. The data used for the analysis come from a primary online questionnaire survey conducted in the spring of 2021. The questionnaire was completed by managers and white collar workers of Hungarian manufacturing companies (n=621). The analysis is based on quantitative methodology, descriptive statistics and relationship analyses were used. The results showed the identified level of experience of workforces related to digital technologies and the level of development of the organisations.
Auxiliary-Task-Based Energy-Efficient Resource Orchestration in Mobile Edge Computing

Advances in edge computing significantly impact the development of mobile networks. As the most important research goal related to edge networks, resource orchestration has been well studied in recent years; however, existing approaches based on deep reinforcement learning share similar bottlenecks in training inefficiency. In this paper, we treat drones, whose available time is significantly limited by their batteries, as the mobile terminals of a target edge network and aim to maximize the energy efficiency. The battery-constrained resource orchestration problem is formulated as a nonconvex optimization problem with consideration of both operating costs and available battery. Owing to the NP-hard nature of mixed-integer programming, the Auxiliary-Task-based dynamic Weighting Resource Orchestration (ATWRO) algorithm is proposed. To improve the sample efficiency, related parameters serving as auxiliary tasks are employed to provide additional gradient information. We further refine the exploration space and apply an alternative replay buffer to develop a customized reinforcement learning approach. Extensive experiments demonstrate the effectiveness of the proposed scheme, as they prove that by employing auxiliary tasks, reinforcement learning agents can be trained with higher efficiency. Moreover, the service time of the whole system can be prolonged, and a higher number of completed tasks can be guaranteed.
A New Arraying Technique for Band-Switchable and Polarization-Reconfigurable Antenna Arrays With Wide Bandwidth

Modern antenna arrays desire antenna apertures which can support a wide frequency range and reconfigurable polarization states. It is challenging to realize such antenna apertures with high isolation among different antenna elements when they are closely packed, particularly for wide-band (>2:1 frequency ratio) operations. In this paper, we demonstrated switchable-band antenna arrays which can be reconfigured from S to C band with an instantaneous bandwidth close to 2:1. The major novelty in this antenna array is that a new arraying technique is adopted to reduce the number of feeding ports by 50%. As a result, the isolation among antenna elements can be significantly improved, thereby increasing the overall radiation efficiency. It should be noted that vertical, horizontal and circular polarizations are supported by this antenna array when all ports are provided with appropriate phases. Herein two arrays are demonstrated. Array I is based on a fractal-shaped slot-ring structure. Each antenna element is fed by a microstrip along the diagonal direction. The common antenna aperture can be reconfigured to operate at 2.5-4.0 GHz or 4.2-7.2 GH, using PIN diode switches. Array II is extended from Array I but with an Artificial Magnetic Conductor (AMC) ground to enhance the instantaneous bandwidth to 2:1. The length of the microstrip feeding lines are reduced to enable modular design. In addition, PIN diode switches are embedded in the feeding lines to further improve the isolation and radiation efficiency. The return losses, port isolations, radiation patterns, gains, radiation efficiencies and IIP3 are simulated and measured.
Coupling measurement of the Higgs boson and search for heavy Higgs like bosons via the decay channel \hwwlnln with the ATLAS experiment

Diese Arbeit pr\"asentiert zwei Analysen des Zerfallskanals \hwwlnln mit den Daten des ATLAS experiments am LHC. Die analysierten Daten wurden im Jahr 2011 bzw. 2012 bei einer Schwerpunktsenergie von $\sqrt{s} = 7 \TeV$ bzw. $8 \TeV$ aufgezeichnet und es wurde eine integrierte Luminosit\"at von $25\,\textrm{fb}^{-1}$ erreicht. Die beiden Analysen unterscheiden sich im analysierten Phasenraum, der von der Massen $m_{\rm H}$ des Higgs boson Signals abh\"angt. Die Analyse f\"ur Massen $m_{\rm H} < 200 \GeV$ wurde \"uber die letzten Jahre optimiert, um in der Lage zu sein, eine Pr\"azisions Messung der Kopplungen einer Resonanz bei $m_{\rm H} \approx 125 \GeV$ durchzuf\"uhren. Dabei wird ein Likelihood Fit der transversalen Masse $\mT = \sqrt{ (E_{T}^{\ell \ell} + P_{T}^{\nu \nu})^2 - | \vec{P_{T}^{\ell \ell}} + \vec{P_{T}^{\nu \nu}|^2} }$ angewendet. Mit einer statistischen Signifikanz von $6.1 \sigma$ konnte ein \hwwlnln Signal bei einer Masse $m_{\rm H} = 125.36 \pm 0.41 \GeV$ beobachtet werden. Die Messung der Signalst\"arke, dem Verh\"altniss von experimentell bestimmtem Produktionswirkungsquerschnitt mal Verzweigungsverh\"altnis zur theoretischen Prognose, ergab folgenden Wert: \begin{align*} \mu &= 1.08\,^{+0.16}_{+0.15} \textrm{(stat.)} \,^{+0.16}_{-0.14} \textrm{(syst.)}, \end{align*} was im Einklang mit der Standard-Modell-Vorhersage steht. Die Skalierung der Kopplungen des Higgs bosons an Fermionen und Bosonen wurden bestimmt zu: \begin{align*} \kappa_{F} &= 0.92\,^{+0.30}_{-0.23}\\ \kappa_{V} &= 1.04\,^{+0.10}_{-0.11}. \end{align*} Zur Suche nach schweren, Higgs boson artigen Teilchen wurde die Analyse des \\ \hwwlnln Zerfallskanals f\"ur Massen $m_{\rm H} > 200 \GeV$ optimiert. Auch im hohen Massenbereich wird ein Likelihood Fit an der Verteilung der transversalen Masse \mT durchgef\"uhrt. Es wurden obere Grenzen auf Produktionswirkungsquerschnitt mal Verzweigungsverh\"altnis f\"ur drei Szenarien bestimmt: Standard-Model-Higgs-Boson im Massenbereich $200 \leq m_{\rm H} \leq 1 \TeV$, Higgs boson artige Resonanz mit einer Zerfallsbreite von $1 \GeV$ im Massenbereich $200 \leq m_{\rm H} \leq 2 \TeV$ und das elektroschwache Singlet Szenario im Massenbereich $200 \leq m_{\rm H} \leq 1 \TeV$, bei dem die Zerfallsbreite zus\"atzlich zur Masse variiert wird. Es konnte in keinem getesteten Szenario ein statistisch signifikanter Daten\"uberschuss beobachtet werden und dar\"uberhinaus konnte ein Standard Modell artiges Higgs Boson bis zu einer Masse von $m_{\rm H} = 661 \GeV$ ausgeschlossen werden.
Pre-service teachers’ self-concept and views toward using ICT for teaching science

There is an increased demand to empower pre-service teachers (PSTs) with knowledge and skills regarding the use of information and communication technologies (ICTs) for teaching science. Using the technology acceptance model as theoretical lens, this paper explores the self-concept and views of PSTs in their final year of study regarding their competences to teach science with ICTs. A mixed methods design was used to collect data from 506 PSTs in two phases. In the first phase, all participants responded to a technological pedagogical and content knowledge questionnaire. In the second phase, semi-structured interviews were used to collect data from a subsample of 28 participants. Results showed that PSTs still underestimate their competences to teach science with ICTs and the majority believe that different school situations and lack of use of ICTs by mentor teachers contribute to failure to build on their competences. Findings also revealed that PSTs have the highest perceived knowledge in technological pedagogical knowledge (TPK) and lowest in technological knowledge. We suggest that PSTs have opportunities to develop their technological content knowledge (TCK) through self-directed learning for it to be on par with their TPK. A linear relationship in the development of PSTs ’ TCK and TPK would ensure improved instructional quality in science classrooms. This paper suggests different strategies to build PSTs ’ independency in the use of ICTs to teach sciences.
Sub-Milliarcsecond Binary Star Astrometry

: Long-baseline optical interferometry has revolutionised binary star astronomy. This paper reviews some recent binary star work done using the Mark III Stellar Interferometer. For the first time, many spectroscopic binaries, eclipsing binaries, triple systems, and binaries with X-ray sources have had their visual orbit, diameter of the primary component, and color index determined. Also, accurate stellar masses, distances, radii, and effective temperatures have been derived. A preliminary distance to the Hyades cluster has also been determined. In addition, preliminary observations provide evidence of a third body in the binary star system ( Her, which may resolve an 85-year old discrepancy in astronomy.
Associations between depression, stress, and e-cigarette use among OEF/OIF veterans

ABSTRACT The COVID-19 pandemic has had profound mental and behavioral health implications for the general U.S. population. However, little is known regarding outcomes for U.S. veterans, who represent a population with high rates of depression, stress, and e-cigarette use. One month prior to the pandemic-related closures (February 2020), 1230 OEF/OIF veterans (ages 18–40) completed an online baseline survey. Six months later, participants completed a follow-up survey (83% retention rate). Hierarchical negative binomial regressions were used to examine the relationship between baseline depression and past 30-day e-cigarette use at follow-up and whether baseline stress moderated this relationship. Veterans who screened positive for depression or who endorsed higher stress levels reported greater e-cigarette use at follow-up. Stress also moderated the relationship between depression and e-cigarette use, such that regardless of stress levels, a positive depression screen was associated with greater rates of later e-cigarette use. However, for those with a negative depression screen, higher stress levels were associated with greater e-cigarette use relative to lower stress levels. Veterans with pre-pandemic depression and stress may be at highest risk for e-cigarette use. Ongoing assessment and treatment for depression and promoting stress management skills for veterans in e-cigarette use prevention and intervention programs may be valuable.
Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns

Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP–SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.
Mitigating the risk that peer-initiated task conflict escalates into diminished helping: roles of passion for work and collectivistic orientation

Abstract This study unravels the link between employees’ exposure to peer-initiated task conflict—defined as the extent to which they perceive that coworkers systematically contest and attack their opinions—and their engagement in helping behavior. Beliefs about interpersonal conflict might mediate this link, and two personal resources, passion for work and collectivistic orientation, arguably have moderating roles. To test these predictions, this study relies on survey data from employees who work in the banking sector, which confirm that peer-initiated task conflict diminishes helping behavior, because the focal employees come to believe coworkers are responsible for their emotion-based quarrels. Employees’ passion for work and collectivistic orientation buffer this harmful dynamic. Organizations thus should recognize that exposure to overcritical colleagues can undermine voluntary work behaviors, as well as consider how they might help reduce the force of this negative dynamic by enabling employees to find ways to draw from their supportive personal resources.
Business Analytics and Firm Performance: A Literature Review

Technological advancements and increasingly available intra- and extra-organizational data have attracted firms to invest in business analytics. Despite the growing number of firms that have invested in business analytics, only some of them have been able to turn their investments into tangible business benefits. This has raised questions among researchers and practitioners about the causal ambiguity between business analytics adoption and firm performance outcomes. Consequently, academic research community has recently launched an endeavor to produce empirical evidence on the relationship between business analytics and firm performance, considering different complementary resources, capabilities, and mechanisms that could better explain what firms need to gain business benefits from business analytics (e.g., Brynjolfsson and McElheran, 2016; Gupta and George, 2016).
Ṣukūk or Bond, Which Is More Sustainable during COVID-19? Global Evidence from the Wavelet Coherence Model

Understanding the co-movement and lag–lead relations among indices is integral to financial decision making. These parameters show the reactiveness of the market towards new information. Understanding them helps to minimize risk and facilitates optimal portfolio diversification. By employing the wavelet coherence econometric model, the authors of this study analyzed the intricate relations among the Bond and Ṣukūk indices using global data belonging to the United States (US), the United Kingdom (UK), Middle East and North Africa (MENA), and Gulf Cooperation Council (GCC) countries. The findings indicated the presence of strong but similar implications of the initial shock of COVID-19 deaths on both Islamic and conventional markets’ volatilities, especially in long-term investment bands (64–128 days). The results oppose the general belief that Islamic finance is more sustainable and less volatile to crises than its traditional counterparts. Moreover, the authors of this study report diverse relationships among bond and Ṣukūk indices throughout the sample periods. We consistently found low correlations in short-term investment bands (4–16), leading to optimal diversification opportunities. However, high correlations were reported due to COVID-19 in the long-term investment bands (128–256), leading to low diversification opportunities for long-term investors.
PL-GAN: Path Loss Prediction Using Generative Adversarial Networks

Accurate prediction of path loss is essential for the design and optimization of wireless communication networks. Existing path loss prediction methods typically suffer from the trade-off between accuracy and computational efficiency. In this paper, we present a deep learning based approach with clear advantages over the existing ones. The proposed method is based on the Generative Adversarial Network (GAN) technique to predict path loss map of a target area from the satellite image or the height map of the area. The proposed method produces the path loss map of the entire target area in a single inference, with accuracy close to the one produced by ray tracing simulations. The method is tested at 900MHz transmission frequency; the trained model and source codes are publicly available on a Github page.