Miracle-dz/UniFAR-FTUs huggingface dataset:
SYNTHESIS AND CHARACTERIZATION OF AN ECO-FRIENDLY MATERIAL FOR STONE MONUMENTS PREZERVATION STARTING FROM THE EGGSHELLS

Conservation and restoration of heritage structures represents a highlighted issue, the main challenge being to bring the cultural objective to its original state, or as close as possible to it by using eco-friendly materials with lover impact on the environment and human health. Within this study, the calcium zincate nanoparticles have been synthesized starting from the eggshels wastes through the precipitation method. For this purpose, the raw eggshells were washed for organic traces removal and disolved in concentrated HCl solution to form CaCl2. The Ca(Zn(OH)3)2•2H2O was obtained by adding into the chemical reaction media a solution of zinc acetate dihydrate previously dissolved in 100mL of methanol under vigorous stirring at room temperature and precipitating with NaOH 12M solution. The white precipitate was characterized by field emmision scanning electron microscope (SEM), X-Ray diffraction (XRD) and thermal analyses (DTA-TG). The Scherrer equation result showed the nanometric size of calcium zincate crystallite, about 23nm.
Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks

Self-assembled memristive nanonetworks composed of many interacting nano objects have been recently exploited for neuromorphic-type data processing and for the implementation of unconventional computing paradigms, such as reservoir computing. In these networks, information processing and computing tasks are performed by exploiting the emergent network behaviour without the need of fine tuning its components. Here, we propose grid-graph modelling of the emergent behaviour of memristive nanonetworks, where the memristive behaviour is decoupled from the particular and detailed behaviour of each network element. In this model, the memristive behavior of each edge is regulated by an analytical potentiation-depression rate balance equation deduced from physical arguments. By comparing modelling and experimental results obtained on nanonetworks based on Ag NWs, the model is shown to be able to emulate the main features of the emergent memristive behaviour and spatio-temporal dynamics of the nanonetwork, including short-term plasticity, paired-pulse facilitation and heterosynaptic plasticity. These results show that the model represents a versatile platform for exploring the implementation of unconventional computing paradigms in nanonetworks.
Optical Identification and Spectroscopy of Supernova Remnants in the Galaxy M51

Using a combination of ground-based and Hubble Space Telescope imaging, we have constructed a catalog of 179 supernova remnants (SNRs) and SNR candidates in the nearby spiral galaxy M51. Follow-up spectroscopy of 66 of the candidates confirms that 61 of these are SNRs and suggests that the vast majority of the unobserved objects are SNRs as well. A total of 55 of the candidates are coincident with (mostly soft) X-ray sources identified in deep Chandra observations of M51; searching the positions of other soft X-ray sources resulted in several additional possible optical candidates. There are 16 objects in the catalog coincident with known radio sources. None of the sources with spectra show the high velocities (≳500 km s−1) characteristic of young, ejecta-dominated SNRs like Cas A; instead, most if not all appear to be middle-aged SNRs. The general properties of the SNRs, size distribution and spectral characteristics, resemble those in other nearby spiral galaxies, notably M33, M83, and NGC 6946, where similar samples exist. However, the spectroscopically observed [N ii]:Hα ratios appear to be significantly higher than in any of these other galaxies. Although we have explored various ideas to explain the high ratios in M51, none of the explanations appear to be satisfactory.
Skeleton Action Recognition Based on Double Residual Convolutional Neural Network

Recently, methods for action recognition using bone point information have gained much attention and research. However, considering the problem, this paper presents a model of skeletal action recognition based on a dual-residual convolutional neural network. The 3D bone data was used as network input to replace the original network structure with ResNet18 network. With action coherence involved, long and short memory network (LSTM) is used to obtain time features for skeletal movement information flow, further improve the fusion strategy, add static attention mechanism to obtain the most important features, and further extract features. With this model, we achieved 85.3% accuracy on the X-Sub of the NTU RGB+D data set, with higher accuracy compared to the original and other networks.
Utilization of foliar fertilizer as an alternative medium for enlargement of Vanda orchid plantlets before acclimatization

This study aims to obtain an appropriate and inexpensive alternative media combination for the growth of Vanda hybrid orchids prior to acclimatization. Experiment using Factorial Randomized Block Design. The first factor is the type of media (Vacin -Went and foliar fertilizer ) and the second factor is the addition of organic matter (coconut water composition, sweet corn composition, tomato composition, BAP, NAA and without organic matter or PGR). The results showed that the interaction of treatment on plant height was only seen at 18 weeks after treatment, while the number of leaves could be seen earlier at 16 weeks after treatment. The independent effect of organic matter and PGR on the number of leaves has been seen since the age of the plant 10 weeks after treatment, while the plant height was only seen 16 weeks after treatment. Independent treatment of growing media had no effect on height and number of leaves up to 18 weeks after planting. The use of foliar fertilizer media with the type of organic material composition being tested can be suggested as an alternative growth medium for plantlet growth, not significantly different from vacin-went with BAP media.
Application of Negative Pressure Type Super - Strength Steel Strip Slag Cooler

Combining with the successful application of negative pressure type super - strength steel strip slag cooler in Jiawang Power Plant.this paper briefs the design principle,technological process and constituents of high temperature slag discharge system.The design of this system is simple,convenient to instal and maintain,with fetter tightness,being able to work protractedly,continuously and stably, and to discharge skg automatic ally, but also some faults occurred during operation,such as faults of salg - cooling fans,steel strip moving to one side etc.The article gives the handling method and puts forward the preventive measures for the faults easily appearing during testing,adjusting and operation.
Specific Aspects in Numerical Simulation of Complex Processes in Gas Turbine Engine Bearing Chamber

The complex interrelation of thermal and hydraulic processes in a gas turbine engine bearing chamber requires modelling methods based on the multiphase flow mechanics and Computational Fluid Dynamics (CFD) to predict the fluid distribution and heat transfer phenomena. This paper presents a study of different approaches to CFD modelling of multiphase oil-air flow in the bearing chamber. The Volume of Fluid and Eulerian multiphase models, Steady and Transient solvers, “Realizable k-ε” and “k-ω SST” turbulence models were analysed. The Eulerian Wall Film Model implemented in ANSYS Fluent was applied to model an oil film formation on the bearing chamber walls. The CFD results were compared with available experimental data to formulate practical recommendations for precise modelling of processes in the bearing chamber.
Operational modal analysis of Canton Tower by a fast frequency domain Bayesian method

The Canton Tower is a high-rise slender structure with a height of 610 m. A structural health monitoring system has been instrumented on the structure, by which data is continuously monitored. This paper presents an investigation on the identified modal properties of the Canton Tower using ambient vibration data collected during a whole day (24 hours). A recently developed Fast Bayesian FFT method is utilized for operational modal analysis on the basis of the measured acceleration data. The approach views modal identification as an inference problem where probability is used as a measure for the relative plausibility of outcomes given a model of the structure and measured data. Focusing on the first several modes, the modal properties of this supertall slender structure are identified on non-overlapping time windows during the whole day under normal wind speed. With the identified modal parameters and the associated posterior uncertainty, the distribution of the modal parameters in the future is predicted and assessed. By defining the modal root-mean-square value in terms of the power spectral density of modal force identified, the identified natural frequencies and damping ratios versus the vibration amplitude are investigated with the associated posterior uncertainty considered. Meanwhile, the correlations between modal parameters and temperature, modal parameters and wind speed are studied. For comparison purpose, the frequency domain decomposition (FDD) method is also utilized to identify the modal parameters. The identified results obtained by the Bayesian method, the FDD method and a finite element model are compared and discussed.
CFD Applications to Launch Vehicles with Reverse Flow Plumes

This paper summarizes the development of a CFD prediction methodology for computing the CM/LAS aerodynamic database, which is valid during an abort from all phases of the flight. This aerodynamic database contains axial, normal, and side forces, pitch, yaw, and rolling moment coefficients. Dynamic similitude arguments are employed to calculate these aerodynamic coefficients in an efficient manner. As part of this work, the CFD solver was validated and calibrated against wind tunnel data for a number of scaled CM/LAS configurations and tunnel conditions. The validated/anchored CFD prediction methodology was then utilized to calculate the aerodynamic coefficients for various flight conditions. The complete CFD approach, from generating the matrix of runs, creating the computational grid, and processing the results, is described in this paper.
Physical quality of coated and uncoated breadcrumbs of Moringa oleifera leaf-fish nugget with various fillers

Nugget is a restructured meat product made by adding various spices and flour as filling and coating materials. Flour as a filler plays an important role in the physical quality of nuggets. The nuggets are coated with breadcrumbs that have a function to improve appearance and increase acceptability of product. This study aims to determine effect of various fillers on physical properties of coated and uncoated breadcrumbs of Moringa leaf fish nuggets. The factors were different ratio of fillers (comparison of corn starch and sago flour) and breadcrumbs coating (coated and uncoated). Nuggets with 100% wheat flour filler were used as a control. The results showed that the use of sago and corn starch flour as a filler increased the ability of WHC, hardness, gumminess, and chewiness but reduced the cooking loss of nuggets. The used of 100% sago flour was the highest of WHC (139.36%). The coating breadcrumbs had lower WHC (111.87%) ability. The cooking loss value ranged from 8.65– 17.66%. The cooking loss was more visible in the samples without breadcrumb coating. The different ratio of filler significantly affected increasing the hardness, the springiness, and the cohesiveness while coated of breadcrumb reduced the hardness.
DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks

Fabricated media from deep learning models, or deepfakes , have been recently applied to facilitate social engineering efforts by constructing a trusted social persona. While existing works are primarily focused on deepfake detection, little is done to understand how users perceive and interact with deep-fake persona (e.g., proﬁles) in a social engineering context. In this paper, we conduct a user study ( n = 286) to quantitatively evaluate how deepfake artifacts affect the perceived trustworthiness of a social media proﬁle and the proﬁle’s likelihood to connect with users. Our study investigates artifacts isolated within a single media ﬁeld (images or text) as well as mismatched relations between multiple ﬁelds. We also evaluate whether user prompting (or training) beneﬁts users in this process. We ﬁnd that artifacts and prompting significantly decrease the trustworthiness and request acceptance of deepfake proﬁles. Even so, users still appear vulnerable with 43% of them connecting to a deepfake proﬁle under the best-case conditions. Through qualitative data, we ﬁnd numerous reasons why this task is challenging for users, such as the difﬁculty of distinguishing text artifacts from honest mistakes and the social pressures entailed in the connection decisions. We conclude by discussing the implications of our results for content moderators, social media platforms, and future defenses.
Manipuri-English Machine Translation Manipuri-English Machine Translation Manipuri-English Machine Translation Manipuri-English Machine Translation using Comparable Corpus using Comparable Corpus using Comparable Corpus using Comparable Corpus

Unsupervised Machine Translation (MT) model, which has the ability to perform MT without parallel sentences using comparable corpora, is becoming a promising approach for developing MT in low-resource languages. However, majority of the studies in unsupervised MT have considered resource-rich language pairs with similar linguistic characteristics. In this paper, we investigate the effectiveness of unsupervised MT models over a Manipuri-English comparable corpus. Manipuri is a low-resource language having different linguistic characteristics from that of English. This paper focuses on identifying challenges in building unsupervised MT models over the comparable corpus. From various experimental observations, it is evident that the development of MT over comparable corpus using unsupervised methods is feasible. Further, the paper also identifies future directions of developing effective MT for Manipuri-English language pair under unsupervised scenarios.
Long-term population dynamics of seeded plants

In recent decades, dozens of studies have involved attempts to introduce native and desirable nonnative plant species into grasslands dominated by invasive weeds. The newly introduced plants have proved capable of establishing, but because they are rarely monitored for more than four years, it is unknown if they have a high likelihood of persisting and suppressing invaders for the long term. Beyond invaded grasslands, this lack of long-term monitoring is a general problem plaguing efforts to reintroduce a range of taxa into a range of ecosystems. We introduced species from seed and then periodically measured plant abundances for nine years at one site and 15 years at a second site. To our knowledge, our 15-year data are the longest to date from a seeding experiment in invaded, never-cultivated grassland. At one site, three seeded grasses maintained high densities for three or more years, but then all or nearly all individuals died. At the second site, one grass performed similarly, but two other grasses proliferated and at least one greatly suppressed the dominant invader (Centaurea maculosa). In one study, our point estimate suggests that the seeded grass Thinopyrum intermedium reduced C. maculosa biomass by 93% 15 years after seeding. In some cases, data from three and fewer years after seeding falsely suggested that seeded species were capable of persisting within the invaded grassland. In other cases, data from as late as nine years after seeding falsely suggested seeded populations would not become large enough to suppress the invader. These results show that seeded species sometimes persist and suppress invaders for long periods, but short-term data cannot predict if, when, or where this will occur. Because short-term data are not predictive of long-term seeded species performances, additional long-term data are needed to identify effective practices, traits, and species for revegetating invaded grasslands.
Predicting clinically signficant change in an inpatient program for people with severe mental illness

Objective: The first aim of this study was to assess the proportion of patients who achieved reliable and clinically significant change over the course of treatment in an inpatient psychosocial rehabilitation program. The second aim was to determine whether age, gender, length of stay, and diagnosis and comorbid diagnosis predicted those who were classified as improved or not improved, using clinical significance criteria. Method: Three hundred and thirty-seven patients from inpatient units at Bloomfield Hospital, Orange, New South Wales, Australia were assessed at admission, 3-month reviews and discharge using the expanded Brief Psychiatric Rating Scale, the Health of the Nation Outcome Scales and the Kessler 10. Results: Reliable and clinically significant improvement was found for 32.4% of inpatients on psychiatric symptomatology, 19.5% on psychosocial functioning and 20.2% on psychological distress. Logistic regression analyses found that the predictor variables collectively predicted those who made reliable and clinically significant improvement on psychiatric symptomatology, but not on psychosocial functioning or psychological distress. Those with a primary diagnosis of schizoaffective disorder had higher rates of improvement in psychiatric symptomatology compared to those with a diagnosis of schizophrenia. Those with co-morbid substance abuse disorders showed a trend towards greater improvement. Conclusions: Inpatient treatment is associated with clinically significant improvements for some patients with a severe mental illness. Patients with schizo-affective disorders are proportionally more likely to make improvement.
Generating Residential PV Production and Electricity Consumption Scenarios via Generative Adversarial Networks

Due to the natural intermittency of renewable energy resources (such as wind and PV) and high variations in electricity consumption profiles, planning and scheduling optimisations generally require a large amount of historical energy data to produce accurate forecasts. Often in practical situations, historical data is not sufficient to capture the uncertainties in generation and consumption. A promising approach to solve this issue is to generate various generation and consumption scenarios, specifying possible trajectories of solar and load power. Then these scenarios can be incorporated into the optimisation models for infrastructure planning and power operation. In this paper, we propose a data driven Generative Adversarial Networks (GAN) based model to generate domestic solar production and electricity consumption scenarios. We train our generative model using historical solar and load data collected by Solar Analytics from Australian residential PV customers. Moreover, by using a conditional GAN, we demonstrate we can generate synthetic data conditioned on site specific conditions. By validating the distributions of our generated data against real-time data, we illustrate that we can produce realistic PV generation and consumption profiles.
University of Groningen Comparing Cognitive and Somatic Symptoms of Depression in Myocardial Infarction Patients and Depressed Patients in Primary and Mental Health Care Groenewold,

Models describing the neural correlates of biased emotion processing in depression have focused on increased activation of anterior cingulate and amygdala and decreased activation of striatum and dorsolateral prefrontal cortex. However, neuroimaging studies investigating emotion processing in depression have reported inconsistent results. This meta-analysis integrates these findings and examines whether emotional valence modulates such abnormalities. A systematic literature search identified 26 wholebrain and 18 region-of-interest studies. Peak coordinates and effect sizes were combined in an innovative parametric meta-analysis. Opposing effects were observed in the amygdala, striatum, parahippocampal,
Temperature Measurement System for Low Pressure Ratio Turbine Testing

In 1999, ITP (Industria de Turbopropulsores, S.A.) launched a wide on-going research program focusing on new technologies to provide significant improvements in Low Pressure Turbines cost and weight. As consequence of the new technologies the experience limits are exceeded and new unknown concepts, like high stage loading turbines, must be explored and then a wide experimental work is required for validation purposes. Cold flow single stage rigs in high-speed facilities were selected by ITP as main vehicle to carry out the experimental validation. Single stage Low Pressure Turbine rigs have low-pressure ratio and power consumption, therefore efficiency predictions based on temperature drop require high accuracy thermocouple measurement systems (precision uncertainties lower than ±50 mK), if small efficiency variations must be captured. In this paper, a detailed uncertainty analysis is introduced and a temperature measurement system that allows achieving such high measurement accuracy is evaluated and described. Type T thermocouples are proposed for use in the range 0°C to 80°C, which are individually calibrated. The procedure followed for this calibration is presented and how is possible to achieve a precision of 30 mK. It is also shown as conventional UTR based on metal plates can behave as good as thermal baths in terms of temperature uniformity and errors, with the adequate isolation and temperature reference calibration. The conventional data recording and voltage measurement systems are experimentally evaluated, and they are found as main source of temperature errors. Although following some recommendations the precision of those systems can be improved, it is experimentally probed and therefore suggested the use of high accuracy voltmeter with a commutation unit to reduce significantly the temperature uncertainty. Finally a miniature Kiel Shroud is proposed and aerodynamically characterised in a high-speed facility. Mach, Reynolds number, yaw, blockage and manufacturing tolerance impact on recovery factor can be inferred from those results.Copyright © 2003 by ASME