Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-Assisted Partial Offloading for Dynamic NOMA-MEC Systems With Imperfect SIC and Reconfiguration Energy Cost

In this article, we investigate the long-term energy minimization for nonorthogonal multiple access (NOMA)-based mobile edge computing (MEC) systems with user mobility, continuous tasks arrival, and time-varying channel when the reconfiguration energy cost caused by dynamic voltage frequency scaling (DVFS) technology and the effect of imperfect successive interference cancelation (SIC) decoding in NOMA transmission are taken into account. We formulate the considered problem as a nonconvex optimization problem. To solve it, we decompose it into a computation resource and transmit power optimization subproblem, and an offloading ratio and transmission time optimization subproblem. We first show that when the offloading ratio and transmission time are given, the optimal local CPU frequency, the optimal computation resource allocation in the base station, and the optimal transmit power can be theoretically derived. Then, based on the above theoretical derivation, a soft actor–critic (SAC)-based deep reinforcement learning (DRL) algorithm is proposed to learn the near-optimal offloading ratio and transmission time for users. Simulation results show that the proposed algorithms can significantly improve the system performance.
The value of data for environmental sustainability as perceived by the customers of a tissue‐paper supplier

To manage their increasingly ambitious environmental agendas successfully, companies need better and more versatile data, yet the elements and mechanisms through which businesses and the environment benefit from data remain poorly understood. A single-case-study setting was designed for examining the perceived value of data for environmental sustainability as seen by the customers of a tissue-paper supplier. The findings, from material collected during interviews with customers in 2022, showcase the multifaceted nature of data's value for business operations and associated ability to support decisions and activities that encourage environment-informed choices and environmental improvements. Also, fully exploiting data's potential for environmental sustainability requires greater volume and detail of product-specific data, transparency of value chains and environmental impacts, and data's better management and sharing. The emerging insight related to the value of data strengthens understanding of how companies could, with data, support their customers' environmental performance.
A new generic separation routine and its application in a branch and cut algorithm for the capacitated vehicle routing problem

This thesis describes a new generic separation algorithm for combinatorial optimization models within the context of branch and cut, with specific application to the capacitated vehicle routing problem (CVRP). This model addresses minimum cost delivery from a central facility to a set of spacially distributed customers with known product demands using a fixed set of delivery vehicles of identical capacity. The original decomposition algorithm of T. Ralphs, on which our procedure is based, generates cuts in a branch and cut framework by representing the current fractional solution as a convex combination of integral vectors, each feasible for a relaxation of the original problem. Given such a representation, a valid inequality is violated by the fractional solution only if it is violated by at least one participant in the representation. This framework was utilized to search for violated capacity constraints for the CVRP. If, however, a representation does not exist, it was shown how to construct a cut separating the current solution from the feasible region of the relaxed problem. This approach has been effective in the solution of medium-size CVRP instances. However, it included an enumerative subroutine which rendered it unsuitable for solution of large instances. We have extensively modified the original decomposition routine, allowing its application to large CVRP instances. We attempt to represent the current solution as a convex combination of integral solutions to the CVRP, rather than its relaxation as before; this also allows us to restrict enumeration. This approach facilitates generation of cuts separating the current solution from the optimal solution. Such cuts were altered to suit numerical implementation; they were found to be computationally effective and relatively strong in the polyhedral sense. We also present a new cut arising when no vectors could possibly participate in the convex representation of the current solution. Taken together, these three components give rise to a new generic separation procedure that has less prerequisites for its application than the original one. The new procedure was found to be somewhat effective in reducing the size of the branch and cut search tree. Several previously unsolved canonical CVRP instances from the VRPLIB—with 76 nodes (for both 7 and 8 vehicles) and 101 nodes—were solved during our computational experiments.
ChatGPT and the Future of Digital Health: A Study on Healthcare Workers’ Perceptions and Expectations

This study aimed to assess the knowledge, attitudes, and intended practices of healthcare workers (HCWs) in Saudi Arabia towards ChatGPT, an artificial intelligence (AI) Chatbot, within the first three months after its launch. We also aimed to identify potential barriers to AI Chatbot adoption among healthcare professionals. A cross-sectional survey was conducted among 1057 HCWs in Saudi Arabia, distributed electronically via social media channels from 21 February to 6 March 2023. The survey evaluated HCWs’ familiarity with ChatGPT-3.5, their satisfaction, intended future use, and perceived usefulness in healthcare practice. Of the respondents, 18.4% had used ChatGPT for healthcare purposes, while 84.1% of non-users expressed interest in utilizing AI Chatbots in the future. Most participants (75.1%) were comfortable with incorporating ChatGPT into their healthcare practice. HCWs perceived the Chatbot to be useful in various aspects of healthcare, such as medical decision-making (39.5%), patient and family support (44.7%), medical literature appraisal (48.5%), and medical research assistance (65.9%). A majority (76.7%) believed ChatGPT could positively impact the future of healthcare systems. Nevertheless, concerns about credibility and the source of information provided by AI Chatbots (46.9%) were identified as the main barriers. Although HCWs recognize ChatGPT as a valuable addition to digital health in the early stages of adoption, addressing concerns regarding accuracy, reliability, and medicolegal implications is crucial. Therefore, due to their unreliability, the current forms of ChatGPT and other Chatbots should not be used for diagnostic or treatment purposes without human expert oversight. Ensuring the trustworthiness and dependability of AI Chatbots is essential for successful implementation in healthcare settings. Future research should focus on evaluating the clinical outcomes of ChatGPT and benchmarking its performance against other AI Chatbots.
Study on Superconducting Microstrip Antenna Array Using TTL Analysis

— For the microstrip patch resonator, as a planar antenna of a microstrip, a set of equations that represents the electromagnetic fields in the x and z direction as function of the electric field in the y direction are obtained applying the transverse transmission line method. This method is very suitable at microwave and antenna applications, and gives accurate complex resonant frequency which contributes definitively to obtain higher precision antenna parameters. For each type of array is calculated the array factor, considering the excitation, phase and the relative displacement between the elements as well as the dimensions and number of elements.
Better predictions when models are wrong or underspecified

Many statistical methods rely on models of reality in order to learn from data and to make predictions about future data. By necessity, these models usually do not match reality exactly, but are either wrong (none of the hypotheses in the model provides an accurate description of reality) or underspecified (the hypotheses in the model describe only part of the data). In this thesis, we discuss three scenarios involving models that are wrong or underspecified. In each case, we find that standard statistical methods may fail, sometimes dramatically, and present different methods that continue to perform well even if the models are wrong or underspecified. The first two of these scenarios involve regression problems and investigate AIC (Akaike's Information Criterion) and Bayesian statistics. The third scenario has the famous Monty Hall problem as a special case, and considers the question how we can update our belief about an unknown outcome given new evidence when the precise relation between outcome and evidence is unknown.
Social media adoption through product diversification and market diversification: The dynamic capabilities perspective

The literature highlights social media as an effective communication platform for firms to connect with potential customers. Although scholars have investigated various benefits of social media, limited studies have explored the antecedences of social media adoption for firms. Particularly, we lack an understanding of which firm strategies could affect social media adoption and how. Drawing on the dynamic capabilities perspective, this study utilizes logistic regression to analyze data from 1,392 public firms to investigate how particular strategies could influence social media adoption. The results indicate that a firm’s market diversification strategies are a significant determinant of social media adoption, while product diversification strategies play a relatively less important role. A diversification strategy involving social media may be more favorable when entering new markets as opposed to launching new products. The findings of this study contribute to the existing research on social media and shed light on strategic implementations that lead to social media usage by firms.
High‐Temperature Thermophysical Property Characterization of Molten Blast Furnace Slag: A Critical Reviews

Centrifugal granulation‐assisted thermal energy recovery (CGATER) remains the leading technology for the iron and steel industry to enable the low‐carbon treatment of high‐temperature blast furnace (BF) slag. Accurate knowledge of the thermophysical properties of BF slag is pivotal for the commercialization of the CGATER. However, it presents a formidable task to obtain these quantities due to the extremely high temperature of molten BF slag (≈1500 °C). To date, the thermophysical properties of BF slag have not been well characterized yet. Herein, five typical thermophysical properties including density, viscosity, surface tension, thermal conductivity, and emissivity are scrutinized with emphasis on the experimental measurements and empirical estimates. The working principles of these measurement methods are briefly overviewed and their merits and demerits are highlighted. It is noted that density, viscosity, and surface tension have been intensively studied; the quantities can be estimated with moderate uncertainty. However, the thermal conductivity and emissivity of BF slag are less tackled, mainly because of the scarcity of measurement tools at high temperatures. In the future, innovative measurement tools tailored for high‐temperature materials are essential, not only for enriching the thermophysical property database of BF slag but also for paving the way toward characterizing other complex high‐temperature melts.
Quantitative Parameters Analysis for Prenatally Echocardiographic Diagnosis of Atrioventricular Septal Defects

Background: Atrioventricular septal defects (AVSDs) are screened and diagnosed usually rely on the imaging characteristics of fetal echocardiography (FE). However, diagnosis on images is heavily depended on sonographers’ experience and the quantitative data are rarely studied. Objective: This study aimed to realize the prenatal diagnosis of AVSDs by analyzing the quantitative data on FE. Methods: One hundred and thirteen cardiac quantitative data was analyzed in 370 normal and 49 AVSDs fetuses retrospectively. The top six with the highest diagnostic accuracy rate were acquired according to the area under the curve (AUC), and the diagnostic value of six variables was analyzed. Results: Six parameters obtained on the four-chamber view (4CHV), including the atrial to ventricular length ratio in end-diastole (AVLR-ED), AVLR-ED combined with the atrial to ventricular length ratio in end-systole (AVLR-ES), quantile score (Q score) of AVLR-ED, Q score of AVLR-ES, Q score of ventricle length in end-diastole (VL-ED), and AVLR-ES, were the top six with the highest diagnostic value, and the AUC was 0.99 (95%CI 0.99–1.00), 0.99 (95%CI 0.99–1.00), 0.99 (95%CI 0.98–1.00), 0.95 (95%CI 0.91–0.99), 0.93 (95%CI 0.87–0.99), and 0.91 (95%CI 0.83–1.00), respectively. And within the 20% false positive rate, the diagnostic sensitivity was greater than 100%, 100%, 100%, 90%, 90%, and 88%, respectively. Conclusions: Six variables could be used for prenatal diagnosis of AVSDs. Among them, AVLR-ED and Q score of AVLR-ED, obtained on the 4CHV, were more convenient to acquire and had higher diagnostic accuracy.
Studies of Low Luminosity Active Galactic Nuclei with Monte Carlo and Magnetohydrodynamic Simulations

Studies of Low Luminosity Active Galactic Nuclei with Monte Carlo and Magnetohydrodynamic Simulations by Guy Hilburn Results from several studies are presented which detail explorations of the physical and spectral properties of low luminosity active galactic nuclei. An initial Sagittarius A* general relativistic magnetohydrodynamic simulation and Monte Carlo radiation transport model suggests accretion rate changes as the dominant flaring method. A similar study on M87 introduces new methods to the Monte Carlo model for increased consistency in highly energetic sources. Again, accretion rate variation seems most appropriate to explain spectral transients. To more closely resolve the methods of particle energization in active galactic nuclei accretion disks, a series of localized shearing box simulations explores the effect of numerical resolution on the development of current sheets. A particular focus on numerically describing converged current sheet formation will provide new methods for consideration of turbulence in accretion disks.
Time-variable gravity potential components for optical clock comparisons and the definition of international time scales

The latest generation of optical atomic clocks is approaching the level of one part in 1018 in terms of frequency stability and uncertainty. For clock comparisons and the definition of international time scales, a relativistic redshift effect of the clock frequencies has to be taken into account at a corresponding uncertainty level of about 0.1 m2 s−2 and 0.01 m in terms of gravity potential and height, respectively. Besides the predominant static part of the gravity potential, temporal variations must be considered in order to avoid systematic frequency shifts. Time-variable gravity potential components induced by tides and non-tidal mass redistributions are investigated with regard to the level of one part in 1018. The magnitudes and dominant time periods of the individual gravity potential contributions are investigated globally and for specific laboratory sites together with the related uncertainty estimates. The basics of the computation methods are presented along with the applied models, data sets and software. Solid Earth tides contribute by far the most dominant signal with a global maximum amplitude of 4.2 m2 s−2 for the potential and a range (maximum-to-minimum) of up to 1.3 and 10.0 m2 s−2 in terms of potential differences between specific laboratories over continental and intercontinental scales, respectively. Amplitudes of the ocean tidal loading potential can amount up to 1.25 m2 s−2, while the range of the potential between specific laboratories is 0.3 and 1.1 m2 s−2 over continental and intercontinental scales, respectively. These are the only two contributors being relevant at a 10−17 level. However, several other time-variable potential effects can particularly affect clock comparisons at the 10−18 level. Besides solid Earth pole tides, these are non-tidal mass redistributions in the atmosphere, the oceans and the continental water storage.
Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series

With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks, and in particular Long Short-Term Memory (LSTM) networks have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context, when trained across all available time series. However, if the time series database is heterogeneous accuracy may degenerate, so that on the way towards fully automatic forecasting methods in this space, a notion of similarity between the time series needs to be built into the methods. To this end, we present a prediction model using LSTMs on subgroups of similar time series, which are identified by time series clustering techniques. The proposed methodology is able to consistently outperform the baseline LSTM model, and it achieves competitive results on benchmarking datasets, in particular outperforming all other methods on the CIF2016 dataset.
Remnant Magnetisation State Control for Positioning of a Hybrid Tunable Magnet Actuator

The recent development of a hybrid tunable magnet actuator proposes to eliminate Joule heating when maintaining a force or position offset. By controlling the remnant magnetic flux in an AlNiCo magnet within the actuator, the actuation force can be linearly varied and maintained. While a method for tuning the magnetisation state of the magnet with minimal magnetic field changes has been demonstrated previously, the method is inefficient due to its slow tuning rate, which hinders its use in controlling the actuator’s position. This paper presents a novel method of magnetisation state tuning with a fast tuning rate and validates its effectiveness for controlling the position of a short-stroke linear actuator. This tuning method is implemented and verified for changing the flux density of an AlNiCo magnet in the range of ±1.2 T, with a root-mean-square error (RMSE) of 7.2 mT. An accurate estimation of the magnetisation state is furthermore achieved during positioning, guided by the design and experimental validation of a lumped parameter model, allowing the position to be controlled with an RMSE of 4.0 µm in a range of −157 to 320 µm.
Analysis of a stochastic SIR model with fractional Brownian motion

Abstract In this article, a stochastic version of a SIR nonautonomous model previously introduced in Kloeden and Kozyakin (2011) is considered. The noise considered is a fractional Brownian motion which satisfies the property of long range memory, which roughly implies that the decay of stochastic dependence with respect to the past is only subexponentially slow, what makes this kind of noise a realistic choice for problems with long memory in the applied sciences. The stochastic model containing a standard Brownian motion has been studied in Caraballo and Colucci (2016). In this paper, we analyze the existence and uniqueness of solutions to our stochastic model as well as their positiveness.
The lid-driven square cavity flow : From stationary to time periodic and chaotic

Ranging from Re=100 to Re=20,000, several computational experiments are conducted, Re being the Reynolds number. The primary vortex stays put, and the longterm dynamic behavior of the small vortices determines the nature of the solutions. For low Reynolds numbers, the solution is stationary; for moderate Reynolds numbers, it is time periodic. For high Reynolds numbers, the solution is neither stationary nor time periodic: the solution becomes chaotic. Of the small vortices, the merging and the splitting, the appearance and the disappearance, and, sometime, the dragging away from one corner to another and the impeding of the merging—these mark the route to chaos. For high Reynolds numbers, over weak fundamental frequencies appears a very low frequency dominating the spectra—this very low frequency being weaker than clear-cut fundamental frequencies seems an indication that the global attractor has been attained. The global attractor seems reached for Reynolds numbers up to Re=15,000. This is the lid-driven square cavity flow; the motivations for studying this flow are recalled in the Introduction. AMS subject classifications: 76M20, 76D05, 76F06, 37N10
Physics Conceptual Understanding in a Computational Science Course

Students 1 face many difficulties dealing with physics principles and concepts during physics problem solving. For example, they lack the understanding of the components of formulas, as well as of the physical relationships between the two sides of a formula. To overcome these difficulties some educators have suggested integrating simulations design into physics learning. They claim that the programming process necessarily fosters understanding of the physics underlying the simulations. We investigated physics learning in a high-school course on computational science. The course focused on the development of computational models of physics phenomena and programming corresponding simulations. The study described in this paper deals with the development of students' conceptual physics knowledge throughout the course. Employing a qualitative approach, we used concept maps to evaluate students' physics conceptual knowledge at the beginning and the end of the model development process, and at different stages in between. We found that the students gained physics knowledge that has been reported to be difficult for high-school and even undergraduate students. We use two case studies to demonstrate our method of analysis and its outcomes. We do that by presenting a detailed analysis of two projects in which computational models and simulations of physics phenomena were developed.