Miracle-dz/UniFAR-FTUs huggingface dataset:
What Is Behind the Buzzword for Experts and Laymen: Representation of “Artificial Intelligence” in the IT-Professionals’ and Non-Professionals’ Minds

The aim of the presented research was to define the differences between information technology (IT) professionals (ITP) and non-professionals (NP) in the way of understanding artificial intelligence (AI). The research was designed in the tradition of categorization research. In an online study participants were asked to make typicality and familiarity judgments for 50 AI exemplars. Two types of analyses were carried out, which made it possible to identify and compare the hierarchy of AI designates (graded structure) and the dimensions of their groupings. We have found that “invisible AI” exemplars were highly rated by ITP, but “visible AI” by NP. Expert knowledge allows ITP to systematize AI exemplars based on both structural and functional elements. On the other hand, laymen indicate the functions that AI-driven products perform, rather than their structures. For ITP, they are primarily algorithmic systems, while for NP they are systems that emulate the functions of living organisms.
Investigation of adequacy of the analytical model of sound field in rectangular room

This paper used the 2D analytical model to ana-lyze the possibility to model sound field of the sound sources acting at certain frequency. The obtained results of the theoretical experiment were compared to the results of practical experiment. The adequacy of the acoustic field’s analytical model to the real acoustic field was analyzed. Results showed that using the created analytical model it is possible to model the acoustic field, produced by restricted rectangular space – the room with certain acoustical fea-tures and estimate the sound level at any point of the room.
Metabolomic Profile of Different Dietary Patterns and Their Association with Frailty Index in Community-Dwelling Older Men and Women

Diet quality has been associated with slower rates of aging; however, the mechanisms underlying the role of a healthy diet in aging are not fully understood. To address this question, we aimed to identify plasma metabolomic biomarkers of dietary patterns and explored whether these metabolites mediate the relationship between diet and healthy aging, as assessed by the frailty index (FI) in 806 participants of the Baltimore Longitudinal Study of Aging. Adherence to different dietary patterns was evaluated using the Mediterranean diet score (MDS), Mediterranean–DASH Diet Intervention for Neurodegenerative Delay (MIND) score, and Alternate Healthy Eating Index-2010 (AHEI). Associations between diet, FI, and metabolites were assessed using linear regression models. Higher adherence to these dietary patterns was associated with lower FI. We found 236, 218, and 278 metabolites associated with the MDS, MIND, and AHEI, respectively, with 127 common metabolites, which included lipids, tri/di-glycerides, lyso/phosphatidylcholine, amino acids, bile acids, ceramides, cholesterol esters, fatty acids and acylcarnitines, indoles, and sphingomyelins. Metabolomic signatures of diet explained 28%, 37%, and 38% of the variance of the MDS, MIND, and AHEI, respectively. Signatures of MIND and AHEI mediated 55% and 61% of the association between each dietary pattern with FI, while the mediating effect of MDS signature was not statistically significant. The high number of metabolites associated with the different dietary patterns supports the notion of common mechanisms that underly the relationship between diet and frailty. The identification of multiple metabolite classes suggests that the effect of diet is complex and not mediated by any specific biomarkers. Furthermore, these metabolites may serve as biomarkers for poor diet quality to identify individuals for targeted dietary interventions.
Can Equality Plans Contribute to the Sustainable Development Goal linked to Gender Equality in Higher Education and Research Performing Organisations?

This paper aims to evaluate the content of Equality Plans (EPs) and assess whether institutions with EPs perform better on gender equality. A mixed-method approach was adopted by performing a content analysis of 45 EPs and conducting a survey using an online questionnaire on a sample of 48 public and private Research, Development, and Innovation (RDI) institutions in Hungary. According to our findings, EPs in their present form are not sufficiently good tools to contribute to the deployment of the Sustainable Development Goal linked to Gender Equality (SDG #5). EPs, to be more powerful in their impacts, need to be embedded in complex organisational processes and infrastructures, including the commitment of top management to frame gender equality as a strategic priority. The novelty of the present research project is that it investigates the outcomes of EPs for RDI sector employers in Hungary.
Surface Modification Strategy for Enhanced NO2 Capture in Metal–Organic Frameworks

The interaction strength of nitrogen dioxide (NO2) with a set of 43 functionalized benzene molecules was investigated by performing density functional theory (DFT) calculations. The functional groups under study were strategically selected as potential modifications of the organic linker of existing metal–organic frameworks (MOFs) in order to enhance their uptake of NO2 molecules. Among the functional groups considered, the highest interaction energy with NO2 (5.4 kcal/mol) was found for phenyl hydrogen sulfate (-OSO3H) at the RI-DSD-BLYP/def2-TZVPP level of theory—an interaction almost three times larger than the corresponding binding energy for non-functionalized benzene (2.0 kcal/mol). The groups with the strongest NO2 interactions (-OSO3H, -PO3H2, -OPO3H2) were selected for functionalizing the linker of IRMOF-8 and investigating the trend in their NO2 uptake capacities with grand canonical Monte Carlo (GCMC) simulations at ambient temperature for a wide pressure range. The predicted isotherms show a profound enhancement of the NO2 uptake with the introduction of the strongly-binding functional groups in the framework, rendering them promising modification candidates for improving the NO2 uptake performance not only in MOFs but also in various other porous materials.
Anisotropic Acoustodynamics in Gigahertz Piezoelectric Ultrasonic Transducers

In this work, we employed our newly developed optical imaging method to probe detailed acoustodynamic physics in gigahertz unreleased ultrasonic transducers based on an AlN-on-silicon system, revealing mode superposition, anisotropic transduction, and dynamic mode evolution. Superpositioned upon the dominant breathing mode along the vertical direction of the AlN layer, multiple resonant lateral modes are identified, and they are shown to evolve into a surface mode beyond the piezoelectric transduction envelope, with strong anisotropic transduction brought by the shear motion of silicon. This acoustodynamic property is important for verifying and further improving design theories of broadband piezoelectric transducers and thin film piezoelectric-on-substrate systems in general.
Measurement of end-hat effects in a crossed-field amplifier

A linear format, low frequency (150 MHz), low power (10 to 100 W), crossed-field amplifier is operated with variable bias, electrostatic confining electrodes (end-hats). The end-hat bias is found to cause electron transport only in the vicinity of the end-hats. End-hat current measurements indicate a substantial part of the electron beam current (40%) can be collected when the end-hats are biased more positive than the floating potential. The observed change in gain versus end-hat bias can be accounted for by the lost beam current. Device gain versus sole bias measurements have been compared with numerical simulations and give general agreement. <<ETX>>
Sensitivity of non-destructive technique for determination of material properties in reinforced laminated composite plate based on vibration response

The paper presents a sensitivity analysis of the non-destructive numerical-experimental method for the identification of the elastic properties in laminated composite plate. The identification was performed on a laminated carbon fibre composite plate with laminate stacking sequence 0o/90o. The method is based on comparing natural frequencies between physical experiment and numerical results obtained by using response surface. Minimizing the error functional between the numerical and experimental data of vibration responses, the identification of materials properties can be performed. When solving the identification problem, it is recommended to take all experimentally obtained frequencies into the error functional. At the same time, different amounts of natural frequencies used for identification in the error functional show various values of material properties. Depending on the amount of frequencies in the error functional, different values of the elastic properties can be obtained. Eleven combinations of frequencies were used for identification of elastic properties. The percentage difference between experimentally measured and numerically calculated frequencies with various values of obtained elastic constants do not exceed 2.4%. Despite different values of elastic constants, small percentage difference shows good agreement between experimentally measured and numerically calculated frequencies. It can be seen that elastic constants obtained for the composite plate are fairly accurate in each case and can be used for modelling composite structures.
Real-time quantification technology of the crowd massing risk in urban public venues

With high density population and its great fluidness in urban public venues,crowd crushing and trampling accidents will occur in emergency that result in great casualties and negative impacts on society.Based on the FIST model,it proposes four parameters to describe the crowd massing risk in public venues that are the density(D),mutual interaction between each others(I),the personnel characteristics(C) and the impact derived form environmental(E) disturbance on the massing crowd.Then,it carries out the corresponding technical analysis for the four predefined parameters.First,it uses the crowd monitoring systems to estimate the crowd density on the spot;Second,the value of crowd pressure will be obtained through the pressure measurement network which describes the mutual affects between different persons;Third,it has neglected the influence of individual differences on the crowd as a whole;Fourth,it attributes all the influence factors to an index-Integrated Disturbance Intensity,then establishes the corresponding mathematical model to quantify its intensity.Finally,it builds up the DICE model as the quantification of crowd massing risk.At the same time,it puts forward the thresholds of crowd density,crowd pressure and crowd massing risk together with their judgment criteria,the whole work puts the technology of crowd massing risk quantification and management into practice.
Simulation of a High-Performance Polarization Beam Splitter Assisted by Two-Dimensional Metamaterials

It is challenging to simultaneously consider device dimension, polarization extinction ratio (PER), insertion loss (IL), and operable bandwidth (BW) to design a polarization beam splitter (PBS) that is extensively used in photonic integrated circuits. The function of a PBS is to separate polarizations of light, doubling the transmission bandwidth in optical communication systems. In this work, we report a high-performance PBS comprising two-dimensional subwavelength grating metamaterials (2D SWGMs) between slot waveguides. The 2D SWGMs exhibited biaxial permittivity by tailoring the material anisotropy. The proposed PBS showed PERs of 26.8 and 26.4 dB for TE and TM modes, respectively, and ILs of ~0.25 dB for both modes, with an unprecedented small footprint of 1.35 μm × 2.75 μm working at the wavelength λ = 1550 nm. Moreover, the present structure attained satisfactory PERs of >20 dB and ILs of <0.5 dB within an ultrabroad BW of 200 nm.
Analysis of the Effect of Artificial Intelligence on Role Cognition in the Education System

Taking the entire education system in Taiyuan City, Shanxi Province, Central China, as an example, this paper uses the questionnaire survey method to analyze the effect of artificial intelligence (AI) on role cognition in the education system. The education system targeted by this questionnaire survey involves 8 categories: preschool education, primary education, secondary education, higher education, adult education, computer network education, enterprise education, and social education; the respondents include 368 teachers, 402 students or learners, 118 school managers, and 124 family members of students or learners in all above education categories. The questionnaire design has a total of 34 question classified into 6 role cognition items, with a 5-level score; a total of 1012 questionnaires were distributed, and 978 were recovered with a recovery rate of 96.64%, in which 957 were valid questionnaires with an effective rate of 97.85%. The study results show that the learning of AI-assisted courses is strongly dependent on course role cognition, and the construction of role cognition is related to the understanding of course content, teaching methods, and activity methods. Therefore, the effect of AI on role cognition in the education system needs to be systematically analyzed from the aspects of function realization form, resource presentation method, supporting hardware form, teacher-student interaction method, and representation method of works. As connecters, teacher's role cognition is limited by the degree of understanding learners, the amount of resources, and data processing capabilities, but the advantage is that they can flexibly monitor and adjust. AI technology is flexible and diverse, it functions in learning and teaching activities in a variety of ways, and there is no agreement on the terminology to describe its role in role recognition. The results of this paper provide a reference for further researches on the effect of AI on role cognition in the education system.
The Scalable Solid-State Synthesis of a Ni5P4/Ni2P–FeNi Alloy Encapsulated into a Hierarchical Porous Carbon Framework for Efficient Oxygen Evolution Reactions

The exploration of high-performance and low-cost electrocatalysts towards the oxygen evolution reaction (OER) is essential for large-scale water/seawater splitting. Herein, we develop a strategy involving the in situ generation of a template and pore-former to encapsulate a Ni5P4/Ni2P heterojunction and dispersive FeNi alloy hybrid particles into a three-dimensional hierarchical porous graphitic carbon framework (labeled as Ni5P4/Ni2P–FeNi@C) via a room-temperature solid-state grinding and sodium-carbonate-assisted pyrolysis method. The synergistic effect of the components and the architecture provides a large surface area with a sufficient number of active sites and a hierarchical porous pathway for efficient electron transfer and mass diffusion. Furthermore, a graphitic carbon coating layer restrains the corrosion of alloy particles to boost the long-term durability of the catalyst. Consequently, the Ni5P4/Ni2P–FeNi@C catalyst exhibits extraordinary OER activity with a low overpotential of 242 mV (10 mA cm−2), outperforming the commercial RuO2 catalyst in 1 M KOH. Meanwhile, a scale-up of the Ni5P4/Ni2P–FeNi@C catalyst created by a ball-milling method displays a similar level of activity to the above grinding method. In 1 M KOH + seawater electrolyte, Ni5P4/Ni2P–FeNi@C also displays excellent stability; it can continuously operate for 160 h with a negligible potential increase of 2 mV. This work may provide a new avenue for facile mass production of an efficient electrocatalyst for water/seawater splitting and diverse other applications.
Condition assessment of high-speed railway track structure based on sparse Bayesian extreme learning machine and Bayesian hypothesis testing

ABSTRACT Aiming at condition assessment of ballastless high-speed railway track structures, in this study, a probability prediction model of the structural static responses under temperature loads based on sparse Bayesian extreme learning machine (SBELM) is constructed. Utilizing the probabilistic predictions of the structural static responses, a Bayesian hypothesis testing-based condition assessment method for track structures is proposed. This method is employed for long-term monitoring data analysis of a high-speed railway track structure with a small-radius curve. Implicit mappings between the temperature loads and the structural static responses are obtained by training a SBELM model, and reliable predictions of the subsequent structural static responses based on the monitored temperature data are yielded. Subsequently, the probabilistic predictions of the structural responses are compared with measured data by Bayesian hypothesis testing for effective condition assessment. The illustrative application validates that the proposed method can realize the condition assessment of high-speed railway track structures effectively.
Location-allocation models for casualty response planning during catastrophic health events

Catastrophic health events are natural or man-made incidents that result in casualty numbers that overwhelm the immediate response capabilities of healthcare systems. These events overpower hospitals even when the latter trigger contingency capacity surges that are usually sufficient to deal with regular disasters. We seek to build effective location-allocation plans for the overwhelming number of casualties expected in these events by using alternative care facilities to address surge capacity issues and incorporating triage and the movement of self-evacuees for effective treatment allocation. We first tailor and apply our framework in a deterministic location-allocation model for catastrophic radiological incidents, which are events whereby the release of radioactive material leads to significant consequences to people, the environment, and facilities. We formulate a mixed integer linear programming model to locate alternative care facilities, allocate casualties for triage, and allocate triaged casualties for treatment to minimize the total weighted casualty transportation time. The model is applied to the study of two separate radiological dispersal device incidents, both based on Department of Homeland Security’s National Planning Scenario 11. We generate the optimal plan for each incident and use sensitivity analyses to draw insights on facility budgeting and triage capacity allocation at hospitals. These insights lead to some response planning rules of thumb. With the above model as a basis, we incorporate data uncertainties, using probabilistically distributed scenarios, and the time sensitivity of information to create a three-stage stochastic programming location-allocation model for general catastrophic health events. In the first stage, the model locates alternative care facilities prior to any scenario realization. In the second stage, it allocates casualties for triage, given initial damage scenarios involving uncertainties in casualty demands, transportation times, and numbers of self-evacuees. In the third stage, it allocates casualties for treatment, given triage scenarios and initial damage scenarios. Model solution time increases exponentially with the number of binary location variables. Given that large solution times are unacceptable in disaster response, we propose an algorithm based on Benders’ decomposition to obtain good solutions fast. The algorithm uses valid inequalities to reduce feasibility cuts, flow cover inequalities to generate stronger cuts, and perturbed dual subproblems to reduce the effect of degeneracy. We implement the model and algorithm in the case study of an earthquake situation in the southernmost part of the San Andreas Fault. We compare the effectiveness of our algorithm to classical Benders’ decomposition and perform sensitivity analyses on facility budgets and capacities to obtain interesting insights.
Multilayer graphene-based radiation modulator for adaptive infrared camouflage with thermal management

Graphene film is a promising thermal camouflage and thermal management material because of its thin, light, flexible structural characteristics and controllable broad-spectrum electromagnetic radiation modulation properties. In this study, a thermal radiation modulator (TRM) based on multilayer graphene (MLG) was studied by simulation and an equivalent transmission line model. The physical mechanism underlying the spectral characteristics and the sensitivity of infrared (IR) radiation modulation to the number of graphene layers is revealed. Furthermore, to solve the problem of thermal instability in the MLG-based TRM, a design scheme integrating a TRM and a meta-absorber is proposed. By electrical control of the MLG, the improved modulator can achieve dynamic emissivity modulation in the wavelength ranges of 3–5 µm and 8–14 µm for adaptive thermal camouflage while maintaining a high emissivity at 5–8 µm for radiative cooling. The compatibility of tunable IR emission and radiative heat dissipation enables graphene to be used for thermal camouflage in complex environments and at high temperatures. The results not only promote the exploration of advanced thermal camouflage materials or devices but also provide inspiration for the application of graphene in thermal management, thermophotovoltaics, IR displays and communications.
Exercise and Reduced Nicotine Content Cigarettes in Adult Female Smokers: A Pilot Trial

Background: Although Reduced Nicotine Cigarettes (RNC) are suggested to improve smoking cessation and cardiometabolic health in relation to cancer risk, the effectiveness of exercise training with RNC on smoking cessation and cardiometabolic health is unknown. Methods: Female smokers (N = 27) were randomized to: (1) usual nicotine cigarettes (i.e., control), (2) RNC or (3) RNC plus exercise treatment for 12 weeks. Smoking withdrawal symptoms (e.g., Wisconsin Smoking Withdrawal Scale) and cardiometabolic health (e.g., weight, VO2max, resting respiratory exchange ratio (RER), glucose, HOMA-IR) were examined before and after treatment. Results: Treatments had no differential effect on weight (p = 0.80; partial η2 = 0.29), VO2max (p = 0.20, partial η2 = 0.18), or total cholesterol/HDL ratios (p = 0.59, partial η2 = 0.06). However, RNC + Exercise tended to maintain RER (i.e., fat oxidation; p = 0.10, partial η2 = 0.10) as well as insulin resistance (p = 0.13, partial η2 = 0.25) and cortisol compared (p = 0.06, partial η2 = 0.30) with control and RNC. Increased VO2max was also associated with lower nicotine dependence scores (r = −0.50, p < 0.05). Conclusion: In this pilot study, improved fitness was associated with lower nicotine dependence. Additional work is warranted to examine the effects of exercise in smokers as a tool to improving smoking cessation and lower disease risk.
EXPERIMENTAL STUDY OF COMBINED OBLIQUE WEIR AND GATE STRUCTURE

Flow measurement in open channel is important to support the system management. An experimental study in a laboratory flume is carried out to study flow over a sharp crested weir and under gate in combined structure as flow measurement device. Four different oblique angles to the longitudinal axis of the channel and four different heights of gate opening were used. The basic principles of statistical analysis are employed to correlate between the hydraulic and geometric dimensionless parameters with the discharge coefficient. Different relationship models with acceptable significance are suggested. It was found that the maj or parameters effecting significantl y on discharge coefficient are (h/d, α, L/d, P/d), and value of Cd range from 0.623 to 0.403, with Standard Error 0.0047. Within the limitations of the present experimental work two different discharge equations were predicted. The first equation Which assumed that the coefficient of discharge is the same for weir and gate with R 2 of o.972, and the second which assumed that there is different percent of discharge for each weir and gate with R 2 of 0.997.