Miracle-dz/UniFAR-FTUs huggingface dataset:
In situ characterization of early-age hydration process of recycled powder blended cement paste using low-field NMR

Abstract The relaxation signal intensity and transverse relaxation time of Portland cement pastes with different dosages of recycled powder at different ages were measured by low-field nuclear magnetic resonance (NMR) technology. Combined with scanning electron microscopy (SEM), the effect of recycled powder on early-age hydration and microstructure of Portland cement was studied. The results show that the hydration process of composite slurry can be divided into four stages, namely, induction period, acceleration period, attenuation period and stable period. As the hydration progressed, the T2 distribution of evaporable water in the composite slurry shifted to short relaxation time, indicating the pores were filled by hydration productions and pore diameter was getting smaller. Recycled powder has high water-adsorption capacity and water slowly-release function, which will lead to the formation of interfacial transition zone (ITZ) and compromise the mechanical property. The transverse relaxation time T2 of samples with higher recycled powder content always maintains obvious bimodal distribution during hydration.
Microwave Assisted Green Synthesis of Ag, Ag2O, and Ag2O3 Nanoparticles

In this study, nanoparticles containing Ag, Ag2O, and Ag2O3 mixture were synthesized by using a microwave-assisted green synthesis method. For the reduction of Ag+ to Ag0, Rhododendron ponticum plant extract was used while the microwave synthesis method was used for the formation of silver oxides. Nanoparticles synthesized under 90 °C, 450 W, and 30-minute microwave synthesis conditions were characterized by UV-Vis, XRD, and STEM. As a result of characterization, Ag-NPs were found to show maximum absorbance peak at 432 nm in the UV-Vis spectrum, crystallite size was 46 nm according to XRD analysis, and nanoparticles showed in a spherical homogeneous distribution by STEM analysis. Our results showed that the phytochemicals in the plant extract of R. ponticum reduce Ag+ ions to Ag-NPs and that the mixture of silver and silver oxide can be synthesized quickly and easily with microwave heating support. This study is important to increase the use of Ag2O and Ag2O3 nanoparticles in industrial production and medical applications. In particular, nanoparticles of silver and silver(I) oxide show great promise for widespread usage in medical polymers and nanodrugs. Because in this study, toxic chemicals were not used in the synthesis of silver oxide nanoparticles and it is a safe synthesis because there is no risk of explosion.
Agents-Based Algorithm for a Distributed Information System in Internet of Things

Traditional information management approaches in distributed systems are most often unsuitable for modern Internet of Things (IoT) environments due to the huge amount and the extreme dynamism of the entities involved. Smart objects (enhanced devices or IoT services belonging to a smart system) interact and maintain relations which need of effective and efficient handling mechanisms better to meet users and services requirements. This article proposes an agent-based algorithm for achieving a distributed resources organization in an IoT environment. Software agents, representing each a single smart object, execute a set of local computations simultaneously, enabling an organized structure to emerge at global level. A natural language processing approach, able to capture the semantic context and represent objects with high-dimensional vectors, was exploited to map the IoT objects, so enabling advanced agents’ operations. The emerging organized virtual structure, i.e., a similarity-based overlay network of agents, allows building informed resource selection/discovery services, therefore more efficient, in a highly dynamic and, basically, unstructured environment. Preliminary results confirm the validity of the approach.
Vague eggs and tags: Prevelar merger in Seattle

Abstract This study describes prevelar merger, the raising of low-front /æ, ɛ/ and lowering of mid-front /e/ before the voiced velar /ɡ/, in Seattle, Washington. In the most advanced part of this change in progress, all twenty speakers (age 18–62, half men, half women, all white) produced /ɛɡ/ and /eɡ/ (beg, vague) as upgliding diphthongs merged in F1 and F2 directly between their nonprevelar counterparts (dress, face). /æɡ/ (bag) was also diphthongal, but its height varied between speakers, with middle-aged men showing near-complete three-way merger with beg-vague and younger speakers raising less, suggesting reversal or avoidance of this component. Previous work lacked information about vague and thus described bag- and beg-raising as failing to reach the height of nonprevelar face. This study revealed that vague is lowered, creating a merger target for both raised beg and bag within a separate diphthongal prevelar subsystem.
Numerically Based Reduced-Order Thermal Modeling of Traction Motors

This article presents an approach based on numerical reduced-order modeling to analyze the thermal behavior of electric traction motors. In this article, a single conjugate heat transfer analysis provides the possibility to accurately predict thermal performances by incorporating both computational fluid dynamic and heat transfer modules. Then, the developed model is used as the basis for deriving a fast reduced-order model of the traction motor enabling prediction of motor thermal behavior in duty cycles with a high number of operating points. All the results achieved are verified using flow and temperature measurements carried out on a traction motor designed and built for a traction application. A good agreement between the measured and estimated values of flows and temperatures is achieved while keeping the computation time within a reasonable range for both the full-order and reduced-order conjugate heat transfer models. The optimized full-order model can be run in minutes and the reduced-order model computation time is less than one second per operating point. The transient simulation based on the reduced-order model is conducted and both the learning phase and validation results are well illustrated. It is shown than the deviation of the reduced-order model in estimating the motor thermal performance is less than one celsius degree from the full-order model.
Utilization of Three Dimensional Printers as a Production Tool

A 3D printer is a product resulting from the development of a product. As a product, a 3-dimensional printer has its value as an object with a function because with its function as a printing tool; the printer can produce an object in physical form. So that it can produce real objects as the output. By using a qualitative descriptive method, this research gets the result that all elements contained in a 3-dimensional printer can not only produce a real object that is following its initial function. In line with the method, this study aims to explain the function of a 3-dimensional printer, which can directly produce products. Other than that, 3 Dimensional printers are now present as tools that can make objects real. So that it has a positive impact on the creation of an object. 3-dimensional printers are still owned by limited circles, but indirectly it can influence potential users of these tools in the form of demands regarding knowledge in the process of making an object using a 3-dimensional printer.
Superior Line from Anther Culture of Dendrocalamus latiflorus Selected after Field Trial

The selection of superior lines is extremely important to improve the utilization rate and economic value of bamboo. In this research, 120 anther-regenerated bamboo lines were planted in the field, and the survival rate reached 84.2% one year after planting. During five years of observations, we continuously measured and recorded the number of shoots and the size of the new bamboo of these regenerated lines. The results showed that there were considerable differences in culm size and growth rate among the different lines. After comprehensive evaluation, we found that one of the lines (P82) had obvious advantages in culm size and growth rate compared with the others. The chromosome ploidy of line P82 and the other three lines (P38, P84, and P34) was detected. It was found that P82 was hexaploid, while the other three lines were dodecaploid. Nutritional components of the P82 shoots were further detected. The results showed that the content of soluble sugar was 1.4%, the content of free amino acid was 3.5 g·kg−1 (FW, fresh weight), and the content of protein was 14.8 g·kg−1 (FW), and there were no significant differences compared with the local wild mature bamboo. Anatomical analysis showed that the vascular bundle size of the line P82 (hexaploid) was significantly larger than that of line P38 (dodecaploid), and the length of parenchyma cells in the culm wall of line P82 was similar to that of line P38, however, the cell width of line P82 was significantly wider than that of line P38. In this study, the breeding of superior lines of regenerated bamboo plants from an anther culture was realized, which provided an example for a new method for selecting superior lines from an anther culture, and also enriched the resources of superior lines of D. latiflorus.
Comparative study of academic research on project management in Iran and the World with text mining approach and TF–IDF method

PurposeThe purpose of this study is to compare the trend of academic project management research in Iran and the World in five-year periods with a text mining approach and TF–IDF method.Design/methodology/approachThe research population consists of 1205 theses presented between 2000 and 2019 in Iranian universities. The central library website of the mentioned universities was used for data collection, and the text mining approach with the TF–IDF method was used for data analysis.FindingsThe remarkable results of this study include: Concrete structures are the most frequent among structural systems, Risk Management is the most frequent among PMBOK Knowledge Areas, Design-build (DB) system is the most frequent among Project Delivery Systems, Engineering, procurement and construction (EPC) is the most frequent among DB Project Delivery Systems, Financial Management is the most frequent among specialized construction knowledge areas, Soft Skills is the most frequent among Global Trends, Contracting Companies is the most frequent among Project Parties, Construction Projects is the most frequent among Project Areas, Power Plant and Refinery is the most frequent among Project Subjects, Optimization is the most frequent among Problem-Solving Approaches, Fuzzy Logic is the most frequent among Novel Algorithms and Motivation is the most frequent among Soft Skills.Originality/valueThe innovative aspect of this research is that for the first time, text mining has been used to analyze academic research on project and construction management, and also for the first time, academic research on construction industry in Iran has been compared with global research.
Ab initio Investigation of the Structure and Electronic Properties of Normal Spinel Fe2SiO4

Transition metal spinel oxides have recently been predicted to create efficient transparent conducting oxides for optoelectronic devices. These compounds can be easily tuned by doping or defect to adapt their electronic or magnetic properties. However, their cation distribution is very complex and band structures are still subject to controversy. We propose a complete density functional theory investigation of fayalite (Fe2SiO4) spinel, using Generalized Gradient Approximation (GGA) and Local Density Approximation (LDA) in order to explain the electronic and structural properties of this material. A detailed study of their crystal structure and electronic structure is given and compared with experimental data. The lattice parameters calculated are in agreement with the lattice obtained experimentally. The band structure of Fe2SiO4 spinel without Coulomb parameter U shows that the bands close to Fermi energy appear to be a band metal, with four iron d-bands crossing the Fermi level, in spite of the fact that from the experiment it is found to be an insulator.
Analysis of Pulses Bandwidth and Spectral Resolution in Femtosecond Stimulated Raman Scattering Microscopy

In the last decade, stimulated Raman scattering (SRS) imaging has been demonstrated to be a powerful method for label-free, non-invasive mapping of individual species distributions in a multicomponent system. This is due to the chemical selectivity of SRS techniques and the linear dependence of SRS signals on the individual species concentrations. However, even if significant efforts have been made to improve spectroscopic coherent Raman imaging technology, what is the best way to resolve overlapped Raman bands in biological samples is still an open question. In this framework, spectral resolution, i.e., the ability to distinguish closely lying resonances, is the crucial point. Therefore, in this paper, the interplay among pump and Stokes bandwidths, the degree of chirp-matching and the spectral resolution of femtosecond stimulated Raman scattering microscopy are experimentally investigated and the separation of protein and lipid bands in the C-H region, which are of great interest in biochemical studies, is, in principle, demonstrated.
Coupled Structural-Rheological Numerical Analysis of Injection Mold

The production of injection molds is an important industry for manufacturing of plastic parts. Considering that time to market reduction is important, it becomes fundamental the use of numerical simulation to reduce design errors. The objective of this work is the presentation of an algorithm for the connection between Moldflow and ANSYS to import loads and then the study of the differences on the approaches studying the tool behavior when considering quasi-static or transient numerical simulation. From this work it was possible to have an overview of where each kind of simulation approach could be used.
A Comparative Study between Women of Different Age Groups: Meaning of Life and Positive Life Orientation

As the struggle for a sense of significance and purpose in life, meaning of life is a prime motivational principle, which can be defined as a personal experience consisting the cognizance of purpose, order, and coherence of one’s pursuit, existence and attainment of valuable goals. Along with meaning of life the positive orientation towards life is closely related to optimism, emotional vitality, generation of positive emotions, and emotional wellbeing. The current paper aims to explore the life orientation and meaning of life in women, it samples a total of 60 women from two different age groups, ranging from 20 to 40 and 40 to 60. The meaning of life and life orientation is examined with regard to the whole sample in general. Standardized test scales were used to measure the variables. The results of the study conducted showed that there was no co-relation in life orientation and meaning of life, it was also found that there was no difference in meaning of life and life orientation in young adult women and older adult women. Optimism and gratitude are known to play a crucial role in the search and presence of meaning in life and also in gaining a positive orientation in life.
Guidance on Implementing Renewable Energy Systems in Australian Homes

The purpose of this paper is to examine several real house cases as renewable energy resources are installed. It is an empirical study, based on first principles applied to measured data. In the first case presented, a PV solar system has been installed and a hybrid vehicle purchased. Battery storage is being considered. Smart Meter data (provided in Victoria, Australia) measures the electrical energy flowing to and from the grid in each half hour. Missing is the story about what the house is generating and what its energy requirements are through each half hour interval. We apply actual (on site) solar PV data to this study, resolving the unknown energy flows. Analysing energy flow has revealed that there are five fundamental quantities which determine performance, namely energy load, energy import, energy harvesting, energy export and energy storage. As a function of PV size these quantities depend on four parameters, easily derivable from the Smart Meter data, namely the house load, the night-time house load (no PV generation), the rating of the solar PV system and the tariffs charged. This reveals most of the information for providing advice on PV array size and whether to install a battery. An important discovery is that a battery, no matter what size, needs a PV system large enough to charge it during the winter months. The analysis is extended to two more houses located within 5 km for which detailed solar data is unavailable.
Selecting a Cost-Effective Seed for Maximizing the Social Influence Under Real-life Constraints

The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adoption of products among users in a social network by identifying and activating a set of initial users. In real-life applications, it is not unrealistic to have a higher activation cost for a user with higher influence. However, the existing works on IM consider finding the most influential users as the seed set, ignoring either the activation costs of such individual nodes and the total budget or the size of the seed set, which may not be always an optimal solution, particularly from the financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation termed as multi-constraint influence maximization (MCIM) aiming to achieve a cost-effective solution under both budgetary and cardinality constraints. Unlike the existing IM formulations, the proposed MCIM is no longer a monotone but a submodular function. As it is also proved to be an NP-hard problem, we propose a simple additive weighting (SAW) assisted differential evolution (DE) algorithm for solving the large-size real-world problems. Experimental results on four real-world datasets show that the proposed formulation and algorithm are effective in finding a cost-effective seed set.
Using Eye Tracking for Measuring Cognitive Workload During Clinical Simulations

High-fidelity clinical simulations can be used by clinicians to acquire technical (physical ability and knowledge) and non-technical (cognitive and social processes) skills. Excessive cognitive workload contributes to medical errors because of the impact on both technical and non-technical skills. Many studies measure cognitive workload with psychometric instruments that limit the assessment of cognitive workload to a single time period and may involve response bias. Using eye tracking to measure task-evoked pupillary responses allows the measurement of changes in pupil diameter related to the cognitive workload associated with a specific activity. Incorporating eye tracking with high-fidelity clinical simulations provides a reliable and continuous assessment of cognitive workload. The purpose of this literature review is to summarize the use of eye-tracking technology to measure cognitive workload of healthcare providers to generate evidence-based guidelines for measuring cognitive workload during high-fidelity clinical simulations. What this manuscript adds to the body of literature is a summary of best practices related to the different methods of measuring cognitive workload, benefits and limitations of using eye tracking, and high-fidelity clinical simulation design considerations for successful integration of eye tracking.
A 3.52-GHz Harmonic-Rich-Shaping VCO with Noise Suppression and Circulation, Achieving -151-dBc/Hz Phase Noise at 10-MHz Offset

This paper presents a transformer-based harmonic- rich-shaping voltage-controlled oscillator (VCO). Its active core features noise suppression and circulation to improve the phase noise (PN) performance, and its 2:2 transformer allows a very short common-mode (CM) return path without sacrificing the tank's quality factor. The proof-of-concept prototype is a 3.52GHz VCO in 65-nm CMOS. It scores a -151-dBc/Hz PN at 10MHz offset, and consumes 6.77 mW of power at a 0.7-V supply. The achieved Figure-of-Merit (FOM) is 187.8/192.3/193.7 dBc/Hz at 0.1/1/10-MHz offsets, with a 1/f3 PN corner of 220 kHz. Over a 22.1% tuning range, the VCO upholds a consistent FOM of >193.3 dBc at 10-MHz offset, and a 1/f3 PN corner of <250 kHz. It has a very low supply pushing of -10 and 37 MHz/V at the lowest and highest frequencies, respectively. The core area is 0.33 mm2.
Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, such as sensor networks. The promising results of robustness against parameter changes are calling for future research in the direction of robust DL control.
Deep Siamese Neural Networks for Facial Expression Recognition in the Wild

This article introduces an algorithm for facial expression recognition (FER) using deep Siamese Neural Networks (SNNs) that preserve the local structure of images in the embedding similarity space. We designed the network to reveal the input pairs similarity by comparing features through a designed metric. Furthermore, we developed a novel image pairing (i.e., positive and negative pairs) strategy technique to train our Siamese model. Our Siamese model comprises of a verification framework and an identification framework to learn a joint embedding space. The verification path reduces the intra-class variations by minimizing the distance between the extracted features from the same class, while the identification path increases the inter-class variations by maximizing the distance between the features extracted from different classes. We apply transfer learning to only use the identification model for facial expression classification. We evaluated our algorithm using AffectNet, FER2013, and Compound Facial Expressions of Emotion (CFEE) datasets, where better results are achieved compared to other deep learning-based approaches.
Characterizing the microbiota of wooden boards used for cheese ripening

Graphical Abstract Summary: Wooden boards were obtained from 3 cheese-processing facilities and assessed for bacterial diversity. Samples were obtained from the surface and at depths of 0.2 and 0.9 cm. DNA was extracted and amplified by PCR and 16S rRNA analysis was performed to identify bacterial communities present on the surface and within the boards. Although there was board-to-board variation in both diversity and richness, all boards contained bacteria in the phyla Actinobacteria, Firmicutes, and Proteobacteria. In general, the boards selected for saltand cold-tolerant bacteria, with 7 operational taxonomic units (Staphylococcus, Brevibacterium, Psychrobacter, Brachybacterium, Jeotgalicoccus, Nocardiopsaceae, and Yaniella) forming a core microbiota found on all boards.