Miracle-dz/UniFAR-FTUs huggingface dataset:
The emergence of technostress in software development work: Technostressors and underlying factors

Software development is a fast-growing market that is heavily tied to information technology (IT) use. Despite the high utilization of IT and high stress levels of software developers, research has largely neglected the effect of IT use on stress experienced by software developers. To address this gap in the research, we employ the concept of technostress. Prior technostress research has found many technostress-creating factors that cause severe negative consequences for both organizations and their employees. Despite these advancements, little is known about how technostress emerges in different organizational contexts and the underlying factors behind the technostress creators in these contexts. We conducted a qualitative study using the critical incident technique to uncover these factors and relevant technostress creators in the context of software development. We utilized a questionnaire with open-ended and closed-ended questions to collect descriptions of technostress experiences from 406 software developers. The current research identifies 21 influencing factors and 10 technostress creators to be relevant in software development, hence contributing to the technostress literature. We also contribute to software development research by explaining how the use of IT contributes to the stress experienced by software developers.
Number theoretic algorithms for elliptic curves

We present new algorithms related to both theoretical and practical questions in the area of elliptic curves and class field theory. The dissertation has two main parts, as described below. Let O be an imaginary quadratic order of discriminant D < 0, and let K = QD . The class polynomial HD of O is the polynomial whose roots are precisely the j-invariants of elliptic curves with complex multiplication by O . Computing this polynomial is useful in constructing elliptic curves suitable for cryptography, as well as in the context of explicit class field theory. In the first part of the dissertation, we present an algorithm to compute HD p-adically where p is a prime inert in K and not dividing D. This involves computing the canonical lift E˜ of a pair (E, f) where E is a supersingular elliptic curve and f is an embedding of O into the endomorphism ring of E. We also present an algorithm to compute HD modulo p for p inert which is used in the Chinese remainder theorem algorithm to compute HD. For an elliptic curve E over any field K, the Weil pairing en is a bilinear map on the points of order n of E. The Weil pairing is a useful tool in both the theory of elliptic curves and the application of elliptic curves to cryptography. However, for K of characteristic p, the classical Weil pairing on the points of order p is trivial. In the second part of the dissertation, we consider E over the dual numbers K[e] and define a non-degenerate “Weil pairing on p-torsion. ” We show that this pairing satisfies many of the same properties of the classical pairing. Moreover, we show that it directly relates to recent attacks on the discrete logarithm problem on the p-torsion subgroup of an elliptic curve over the finite field Fq . We also present a new attack on the discrete logarithm problem on anomalous curves using a lift of E over Fp [e].
Mereological Approach for Formation of "Part-Whole" Relations between Pages of a Semantic Wiki-resource

Wiki technologies are considered as a common means of collaborative development of information resources. One of the most typical problems that arise in the process of semantization of the Wiki resources with a complex heterogeneous structure is the construction of consistent and non-contradictory set of semantic properties that represent the meanings of relations between individual Wiki-pages. We propose to use elements of ontological and mereological analysis that allow us to formalize the characteristics of the used semantic relations and to determine their subtypes. Thus, the interoperability of the use of the names of semantic relations by independent and territorially separated users with different terminological systems is ensured. Mereology analyzes one of the fundamental types of relations between objects, as well as natural and artificial environments – the relations of part and whole. The main subtypes of mereological relations are considered on the examples of relations between the pages of e-VUE – portal version of the Great Ukrainian Encyclopedia implemented on base of semantic Wiki technologies.
The development and implementation of deep learning assisted interoperable retinal image structured report module in PACS

With the increasing incidence of retinal fundus disease, more attention has been paid to the screening and prevention of fundus diseases. However, the low efficiency and interoperability issues hinder the application of retinal disease reporting. In this study, we developed an intelligent classification module for retina based on inception V3 pre-trained model. The top rank of eight classification scores was output as recommendation for image reader reviewing, and the other structured report (SR) module was designed to generate FHIR-compliant retinal diagnostic SRs by customizing existing FHIR DiagnosticReport and Observation resources. The results demonstrated the artificial intelligence (AI) classification pop-up window and the validation of produced SR via a public HAPI FHIR server. The results suggested that the SR was easily shared and could be accurately and integrated with other hospitals or clinical research institutions. In summary, our developed AI assisted SRs module is clinical workflow friendly, and the produced SRs could be easily shared and integrated, which can provide a meaningful use way for further analytic research.
Analysis on mechanisms of idea generation: evidences from fNIRS hyperscanning

This study aimed to determine whether intuitive play in a real environment affects the cerebral blood flow during the interaction between individuals. We implemented a creativity-related task designed by Cheng and Hashimoto. We focused on using fNIRS technology to investigate the differences between two ways for collaborative problem-solving, namely, do-first and think-first approaches. We obtained significant differences between the activities referring to ideas and the others interpreting concepts. We found that the "do-first" approach facilitates conversations on ideas, which reproduced Cheng and Hashimoto's experiment. We also saw a trend of increased cerebral blood flow in the subjects' prefrontal cortex and increased brain activity when performing the task. Our findings indicate that fNIRS technology enables us to study the mechanism of idea generation.
Measurements of beam halo diffusion and population density in the Tevatron and in the Large Hadron Collider

Halo dynamics influences global accelerator performance: beam lifetimes, emittance growth, dynamic aperture, and collimation efficiency. Halo monitoring and control are also critical for the operation of high-power machines. For instance, in the high-luminosity upgrade of the LHC, the energy stored in the beam tails may reach several megajoules. Fast losses can result in superconducting magnet quenches, magnet damage, or even collimator deformation. The need arises to measure the beam halo and to remove it at controllable rates. In the Tevatron and in the LHC, halo population densities and diffusivities were measured with collimator scans by observing the time evolution of losses following small inward or outward collimator steps, under different experimental conditions: with single beams and in collision, and, in the case of the Tevatron, with a hollow electron lens acting on a subset of bunches. After the LHC resumes operations, it is planned to compare measured diffusivities with the known strength of transverse damper excitations. New proposals for nondestructive halo population density measurements are also briefly discussed.
Security Analysis of Authentication and Key Agreement for Internet of Drones

Drones in the Internet of Drones (IoD) have been widely used in various fields, such as military reconnaissance, climate and environmental detection, logistics and transportation, and disaster relief and so on. There are many challenges related to security, privacy and energy consumption when collecting and transferring data between sensors embedded in drones. Recently, Zhang et al. developed a lightweight authentication and key agreement scheme for IoD to address these issues. Their developed scheme realized mutual authentication and key agreement of drones and users by adopting lightweight hash and bitwise XOR operations. The authors demonstrate that their proposed scheme provides better security requirements and more efficient than related schemes. However, this study shows the limitations of Zhang et al.'s scheme, which leads to their scheme may be subject to some potential attacks and cannot satisfy the session key security and untraceability properties.
Explainable Machine Learning Models for Suicidal Behavior Prediction

In the healthcare setting, suicidal behavior prediction plays an important role in clinical decision making due to the suicide rate increasing day by day contributes to a decrease in productivity and increase in national expenditure. Several machine learning models are being developed to generate accurate predictions in a suicide attempt. However, there is a lack of interpretability, explainability and transparency with these predictive models. Therefore, the aim of this study is to improve explanations of machine learning models for predicting suicidal behavior based on clinical data using the Shapley Additive exPlanations (SHAP) approach. The experiment shows that machine learning models with SHAP are able to interpret and understand the nature of an individual's predictions of suicidal behavior.
Using classification and visualization to support clinical texts review in electronic clinical documentation

Electronic health records, as a repository, of patient information, is nowadays the most commonly used technology to record , store and review patient clinical records and perform other clinical tasks. However, the accurate identification and retrieval of relevant information from clinical records is a difficult task due to the unstructured nature of clinical documents, characterized in particular by lack of clear structure. Therefore, medical practice is facing a challenge thanks to rapid growth of health information in electronic health records (EHRs), mostly in narrative text form. As a result, it's becoming important to effectively manage the growing amount of data for a single patient and there is currently a requirement to visualize electronic health records (EHRs) in a way that aids physicians in clinical tasks and medical decision-making. Leveraging text visualization techniques to unstructured clinical narrative texts is a new area of research that aims to provide better information extraction and retrieval to support clinical decision support in scenarios where data generated continues to grow. Clinical datasets in electronic health records (EHR) offer a lot of potential for training accurate statistical models to classify facets of information which can then be used to improve patient care and outcomes. However, in many clinical note datasets, unstructured nature of clinical texts is a common problem. This paper examines to the very issue of getting raw clinical texts and mapping them into meaningful structures that can support healthcare professional utilizes narrative texts. Our work is the result of a collaborative design process that was aided by empirical data collected through formal usability testing.
Dielectric properties of ultrathin metal films around the percolation threshold

Optical reflection measurements of thin Au films at and around the percolation threshold (film thickness 3\char21{}10 nm) are performed in an extremely broad spectral range up to $35\text{ }000\text{ }{\text{cm}}^{\ensuremath{-}1}$. Combining spectroscopic ellipsometry, Fourier-transform infrared spectroscopy, and dc measurements, the dielectric properties of the films can be described over the whole frequency range by Kramers-Kronig consistent effective dielectric functions. The optical conductivity of the films is dominated by two contributions: a Drude component starting at the percolation threshold in the low-frequency range and by plasmons in the near-infrared region, which shift down in frequency with increasing film thickness. The interplay of both components leads to a dielectric anomaly in the infrared region with a maximum of the dielectric constant at the insulator-to-metal transition. The results are compared to predictions from effective-medium approximations and percolation theory.
Do Social Interactions Really Moderate Job Productivity in Coworking Spaces?

Abstract A coworking space is a setup of bringing together independent professionals who do not work for the same company. This study aims to determine the impact of the coworking space environment and of social interactions on the job productivity of coworkers, as well as the influence of social interactions in the relation of coworking space environment and job productivity. A positive significant relationship was found between the coworking space environment and job productivity as well as between social interactions and job productivity. Moreover, the moderating variable “social interactions” strengthens the relationship between coworking environment and job productivity. The findings of the study highlight the importance of coworking spaces as a source of social interactions among freelancers and entrepreneurs. Furthermore, human resource managers and corporate office managers can use this study to help their staff operate in a more flexible and productive setting.
Increasing the Multi-position Signals Noise Immunity of Mobile Communication Systems Based on High-order Phase Modulation

The article presents the results of a study of situations typical for 4G (Long Term Evolution - LTE) mobile communication systems, in which there are conditions for the use of the autocorrelation method for processing multi-position signals, and the noise immunity of demodulators that implement this method is determined. It has been proved using the theory of catastrophes that for modern mobile communication systems it is advisable to use energy methods with high-order phase-difference modulation (PDM), which will provide the system with the property of invariance to a certain class of electromagnetic interference (EMI). A comparative analysis of the application of systems with PDM of the first and second order has been carried out and it has been determined that in the case of autocorrelation reception of signals from PDM-1, it is necessary to ensure sufficiently stringent requirements for the stability of the frequency of the carrier wave. It was found that in the case of using PDM-1, the autocorrelation technique is much simpler, but the PDM-2 has a unique property of invariance to the frequency of the carrier wave, which neither coherent nor optimal incoherent methods have. An energy (autocorrelation) demodulator of signals with double PDM-1 is proposed, which has the property of relative or absolute invariance to changes in the frequency of the carrier wave, which can occur in digital information transmission systems during communication with rapidly moving objects. satellite communication systems, as well as fiber-optic communication systems, mobile broadband access with support for the fourth generation technology.
Towards an Ontology of Offshore Petroleum Production Equipment

This document shows the current research progress and methodology applied to build a domain ontology for describing facilities in offshore petroleum production plants, based on an extensive requirement collection developed in industry and formalized through a set of competency questions. The final goal is to create a uniform and formally defined reference vocabulary to help engineers and information technology people label and relate measures and facilities in the production plant monitoring and simulation. The ontology uses BFO as a top-level ontology and employs GeoCore and an ongoing version of the core ontology produced by the Industry Ontology Foundry (IOF) as middle-level ontologies. This research is part of the Petwin project that studies the best practices for developing a digital twin for monitoring and simulating petroleum production in the industry.
Semi-Analytic Ground State Solutions of Two-Component Bose-Einstein Condensate in Two Dimensions

In this thesis we study a two-component Bose-Einstein Condensate system in 2D, using a combination of approximate analytical and exact numerical methods of solving the Gross-Pitaevskii equation (GPE). We discuss some of the ways of finding approximate ground state solutions to the GPE, one of them being the Thomas-Fermi approximation. Using the Thomas-Fermi approximation, one can separate the system into two regimes; one where both components are disks and one where one of the components is a disk and the other component is an annulus. However, the Thomas-Fermi approximation does not hold true beyond a critical value. We demonstrate a method using which one can get more precise solutions for the GPE. This involves using a method of approximation of the GPE near the critical boundaries, which has been previously used for a single component Bose gas. This can be adapted for a two-component system in order obtain explicit solutions for a BEC in a 2D harmonic trap with repulsive interactions. We show in detail how this approximation works for the different regimes suggested by the Thomas-Fermi analysis and also for more generalized cases where one has different masses, particle numbers and trapping frequencies for the two components. This thesis also talks about how one could analyse the system using this approximation in 1D and 3D, shedding more light on what might be the parameters affecting the precision of the solution.
The Influence of COVID-19 and Psychosocial Risks on Burnout and Psychosomatic Health Problems in Non-University Teachers in Spain during the Peak of the Pandemic Regressions vs. fsQCA

During the COVID-19 pandemic, teachers had to adapt to the changes caused by remote working, experiencing prolonged stress situations that together with psychosocial factors can lead to burnout and psychosomatic health problems. This study focused on analyzing the influence of COVID-19 and psychosocial risks on psychosomatic problems and burnout at the peak of the pandemic in Spain, from March to April 2020. It combined two methodologies, hierarchical regression models (HRM) and qualitative comparative analysis (QCA). The two methodologies demonstrated greater predictive power for psychosocial demand factors (workload and role conflict) on burnout and psychosomatic health problems. In addition, the fsQCA models demonstrated the contribution of job insecurity and the lack of organizational justice, resources and COVID-19 information. These results can be used by educational managers to improve the daily work of teachers, impacting on the quality of teaching, as well as their response to pandemics, which will benefit society.
The Association between High-Sensitivity C-Reactive Protein and Metabolic Syndrome in an Elderly Population Aged 50 and Older in a Community Receiving Primary Health Care in Taiwan

Metabolic syndrome (MetS) has become the most important issue in family medicine and primary care because it is a cluster of metabolic abnormalities that are a burden on health care in many countries. Highly sensitive C-reactive protein (hsCRP), which is elevated in inflammatory situations, can be produced by monocyte-derived macrophages in adipose tissue. People with MetS tend to have more adipose tissue. Therefore, we aimed to investigate the association between hsCRP and MetS among elderly individuals aged 50 years and older in northern Taiwan. This study was a cross-sectional community-based study that included 400 middle-aged and elderly Taiwanese adults, and 400 participants were eligible for analysis. We divided the participants into a MetS group and a non-MetS group. Pearson’s correlations were calculated between hsCRP and other related risk factors. Furthermore, the relationship between hsCRP and MetS was analyzed with logistic regression. People in the MetS group were more likely to have higher hsCRP levels. The Pearson’s correlation analysis showed a positive correlation with hsCRP. In the logistic regression, hsCRP was significantly associated with MetS, even with the adjustment for BMI, uric acid, age, sex, smoking status, drinking status, hypertension, diabetes mellitus, and dyslipidemia. In summary, our research indicated that hsCRP could be an independent risk factor for MetS.
Model Counting, its Relationship to Symbolic Quantitative AI, and a Glimpse into Practical Solving

Model counting (# SAT ) asks to compute the number of satisfying assignments for a propositional formula. The decision version ( SAT ) received widespread interest in computational complexity, formed many applications in modern combinatorial problem solving, and can be solved effectively for millions of variables on structured instances. # SAT is much harder than SAT and requires more elaborate solving techniques. We revisit the problem, its complexity, and explain applications in symbolic quantitative AI. We briefly overview solving techniques and finally list connections to abstract argumentation.
Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

: During the operation period, the deformation of an ultra-high arch dam is affected by the large ﬂuctuation of the reservoir water level. Under the dual coupling of the ultra-high dam and the complex water level conditions, the traditional variational analysis method cannot be sufﬁciently applied to its deformation analysis. The deformation analysis of the ultra-high arch dam, however, is very important in order to judge the dam safety state. To analyze the deformation law of different parts of an ultra-high arch dam, the panel data clustering theory is used to construct a Spatio-temporal characteristic model of dam deformation. In order to solve the difﬁcult problem of the ﬂuctuating displacement of dam deformation with water level effect, three displacement component indexes (absolute quantity, growing, and ﬂuctuation) are proposed to characterize dam deformation. To further optimize the panel clustering deformation model, the objective weight coefﬁcient of clustering comprehensive distance is calculated based on the CRITIC (CRiteria Importance Through Inter-criteria Correlation) method. The zoning rules of the ultra-high arch dam are established by using the idea of the CSP (Constraint Satisfaction Problem) index, and the complex water level of the reservoir is simulated in the whole process. Finally, the dynamic cluster analysis of dam deformation is realized. Through a case study, three typical working conditions including the rapid rise and fall of water level and the normal operation are calculated, and the deformation laws of different deformation zones are analyzed. The results show that the model can reasonably describe the deformation law of an ultra-high arch dam under different water levels, conveniently and intuitively select representative measuring points and key monitoring parts, effectively reducing the analysis workload of lots of measuring points, and improve the reliability of arch dam deformation analysis.