Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of a Hybrid Digital Twin Concept for Factory Layout Planning

As the modern society is in the middle of its fourth industrial revolution, many enabling technologies are now viable to use in an engineering context. Several of these technologies are mature and available off the shelf; however, in the industrial setting they are rather novel. Two of these are virtual reality (VR), which has grown immensely in the gaming sector, and 3-D imaging, which is commonly used in archeology and construction. This study presents lessons learned from combining these two technologies in an industrial context with the digital twin concept. Three industrial case studies have been performed, and several observations have been identified in all three aspects of sustainability. For example, improved solution fidelity at an early stage can be achieved by externalizing tacit knowledge, and multiple issues during planning and installation phases have been avoided by utilizing the hybrid digital twin models. This type of digital twin enables highly detailed production system access, enabling engineering abilities from anywhere, anytime. Furthermore, the model becomes a powerful communication tool, which has reduced the resistance to workplace changes, as stakeholders lacking computer-aided design (CAD) knowledge can be involved in the change process. The highly detailed models have also allowed more focus to be put on safety and regulations, as these aspects naturally are more suited to experience in immersive VR. In conclusion, the hybrid digital twin concept developed in this study is a promising tool for decision makers and stakeholders alike, bound to benefit those who use it in all three aspects of sustainability.
Aplicación para el control remoto del robot Turtlebot2.

This work is oriented to the need of the ARCO Group of Escuela Superior de Informática of the UCLM to carry out a mapping of the Wi network of the school building. Since it is an arduous and repetitive task, and since the Group has the possession of an autonomous robot Turtlebot2, the possibility of carrying out this task with the support of Turtlebot2 was considered. The role of Turtlebot2 in this task would be to transport the device that will make the Wi mapping calculations on its tray, making the necessary movements for the device to perform its function correctly, ie, move forward through the building, stop for a certain amount of seconds for the device to perform the calculations, and continue moving. Since the handling of Turtlebot2 for the performance of the mapping task is hard and not very intuitive, the need for a web application to control Turtlebot2 remotely.
The development of Spiking Neural Network: A Review

Spiking neural networks (SNNs), known as third-generation neural networks, are practical tools for processing complex spatiotemporal information. However, the lack of clarity on how biological neurons encode information via impulse signals and the lack of neuronal models and recognized efficient training algorithms that combine low implementation cost, and high biological interpretation have left the SNNs behind second-generation neural networks in most learning tasks. This paper describes the main research areas in SNNs, including neuronal models, coding methods, network architectures, and learning algorithms, and discusses in detail each perspective's current state of the arts, and reviews future research directions in the field.
Scientific personnel training in convolutional neural networks for the implementation of research projects of the MegaScience class

Megascience projects require an all-inclusive interdisciplinary approach. Because of that scientific personnel engaged in projects of this type must possess relevant required interdisciplinary skills. Artificial intelligence in particular convolutional neural networks has a wide range of applications, and it could be used to solve complicated problems in all kinds of various fields of science. Thus, the understanding of the principles of neural networks’ working is a necessary skill for scientific personnel. In this paper, we’re considering practical examples and fields of applications of neural networks in the real world.
Evolution of mobile phases in cometary interiors

Abstract Beginning with loose aggregations of dust particles coated with heterogeneous ices under vacuum at Kuiper Belt temperatures, moving to Jupiter/Saturn distances and eventually to low-perihelion orbit, we consider the likely development of the gaseous phase within a cometary nucleus over the course of its lifetime. From the perspective of physical chemistry, we consider limits on the spatial and temporal distribution and composition of this gaseous phase. The implications of the gaseous phase for heat transfer and for the possible spatial and temporal development of liquid phases are calculated. We conclude that the likely temperatures, pressures, and compositions beneath the outer crust of typical cometary nuclei are such that fluidised phases can exist at significant depths and that these reservoirs give a coherent explanation for the high-intensity outbursts observed from cometary nuclei at large distances from perihelion.
Friction and wear analysis of the external return spherical bearing pair of axial piston pump/motor

By discretizing the contact area between the external retainer plate and the external spherical hinge, a mathematical model for the force relation of an arbitrary contact point in the external return spherical bearing pair was established and a mathematical expression for the friction power of the external return spherical bearing pair was deduced. The influences of the slant inclination of the external swash plate, pump shaft rotating speed, eccentricity, spring force and number of discrete contact points on the friction power were also analysed. The results show that the power fluctuation amplitude of the discrete contact point in the external return spherical bearing pair increases with increasing slant inclination of the external swash plate, pump shaft rotating speed and spring force; the total friction power was found to increase linearly. However, the power fluctuation amplitude of the discrete contact point in the external return spherical bearing pair was found to decrease with increasing eccentricity, with the total friction power decreasing nonlinearly until reaching a certain value. The distribution shape of the friction power of the discrete contact point is only affected by eccentricity. If the eccentricity is large, the friction power of the discrete point presents a double-peak distribution, whereas if it is small, a multiple-peak distribution is observed.
Drivers of carbon emissions in China’s tourism industry

Abstract This manuscript examines the driving forces of carbon emissions in China’s tourism industry. Tourism carbon emissions are estimated by constructing China’s Economic-Environmental Accounts (EEA). Analysis is divided into five-time intervals and specifically examines intensity, scale, structure, and technology. Following index and structural decomposition methods, changes in tourism carbon emissions were segmented into sixteen economy-wide and tourism-specific driving forces. Results demonstrate that direct and total tourism carbon emissions compose 0.7% and 2.7% of total carbon emissions in China. Analysis revealed the positive driver of tourism emissions was domestic tourists, representing 140.4% increase in direct and 263.4% increase in total tourism carbon emissions. Modelling identified energy intensity as the main negative driver in total and direct tourism carbon emissions, especially for national economic sectors (−208.6%) and non-transport tourism sectors (−33.8%). Future research should focus on the measurement and implementation of mitigation policies for domestic tourism emissions.
Comparison in Some Biochemical and Hematological Tests Between Chronic Hepatitis B and C

Chronic viral hepatitis is an important health problem in the world, where hepatitis B virus (HBV) or hepatitis C virus (HCV) infections are the main causes of liver insufficiency. The study included 100 blood samples from patients with chronic viral hepatitis, fifty of them with HBV infection and 50 with HCV infection. Twenty apparently healthy age and gender matched subjects were included as a control group. Out of the 50 patients with HBV, 36(72%) were males and 14(28%) were females. Thirty two (64%) patients with HCV were males and 18(36%) were females. The mean age for HBV patients was 36.9 ± 15.8 year and for HCV patients it was 39.9 ±14.2 year. The results of the liver function tests showed no significant difference between HBV and HCV patients. Both of HBV and HCV patients showed a significant difference regarding liver function tests when compared with the control group. Total white blood cell count and hemoglobin concentration were lower (4296± 1050.9 cell/ mm3 and 10.9 ± 2.2g/dl respectively) in HCV patients than those in HBV patients (6224 ± 1749.1 cell /mm3 and 13.4 ±1.3 g/dl ) with a significant difference. However there was no significant difference between HBV patients and control regarding total white blood cell count and hemoglobin concentration. Aim of study: to compare between chronic hepatitis B, chronic hepatitis C and control regarding some hematological and biochemical parameters.
NAVIGATING THE WEB OF CHOICE: SCHOOL DISTRICT ENROLLMENTS AND RESPONSES TO CYBER CHARTER SCHOOLS

This study examined the enrollment and organizational influence of cyber charter school policy and practice on Pennsylvania traditional public school districts. Specifically, it examined the demographic composition of school districts most likely to lose students to cyber charter schools and how this composition changes over time, the prevalence of a school district response to create a full-time online school, and the effectiveness of this response in recovering student enrollment. The methods used in the study were mainly quantitative (ordinary least squares regression, logistic regression, and Cox proportional hazard modeling). Qualitative supplements (interviews of school district administrators) informed patterns seen in the quantitative data. Three major findings emerged from this study. First, no demographic pattern existed in the districts that lost the most students to cyber charter schools early in the cyber charter movement. Over time, as the reputation of cyber charter school quality skewed negative, disadvantaged districts—particularly those with low levels of education and low statewide test scores—were most likely to experience continually increasing enrollment losses to cyber charter schools. These students overwhelmingly moved into programs with lower academic growth rates compared to the traditional public schools that the students left. Second, based on a random sample of school districts, the majority of school districts in Pennsylvania (more than 80%) created an in-district, full-time online learning option during the cyber charter school movement. The first adoption of school district online programs is explained both by market and institutional patterns. Third, analysis in the trends of choosers before and after a school district created an in-district online program showed that the district’s strategy tended not to decrease enrollment losses to cyber charter schools. There are a number of possible implications of these findings. For example, when a new educational reform or program is perceived as an inadequate option in a choice-based system, findings here suggest that advantaged populations will first refuse it and perpetuate their advantage. Additionally, higher student enrollment losses and increased institutional pressures both relate to district adoption of a new online school. Further, since students can leave regardless of whether a district provides the same online options as cyber charters, there exists online learning program redundancy. These trends have placed Pennsylvania in a situation where districts lose millions of dollars per year to cyber charter schools while unsuccessfully recovering student enrollment. The dissertation concludes with leadership and policy recommendations related to these implications.
Post-Packaging Simulation Based on MOSFET Characteristics Variations Due to Resin-Molded Encapsulation

A method for estimating circuit performance variation caused by packaging-induced mechanical stress is proposed. The developed method is based on the stress distribution chart for the target integrated circuit (IC) and the stress sensitivity characteristics of individual devices. This information is experimentally obtained using a specially designed test chip and a cantilever bending calibration system. A postpackaging analysis and simulation tool, called Stress Netlist Generator (SNG), is developed for conducting the proposed method. Based on the stress distribution chart and the stress sensitivity characteristics, SNG modifies the SPICE model parameters in the target netlist according to the impact of the packaging-induced stress. The netlist generated by SNG is used to estimate packaging-induced performance variation with high accuracy. The developed method is remarkably effective even for small-scale ICs with chip sizes of roughly 1 mm2, such as power management ICs, which require higher precision. key words: stress, piezoelectric, package, simulation, SPICE
Effects of Stem Based Activities on In-Service Teachers' Views

In this study we investigated the effect of STEM based activities on in-service teachers’ views about STEM teaching. Pre-test and post-test research design was employed to investigate teachers’ reactions to STEM based activities. The participants were 39 in-service teachers from different branches who were working as teachers in public schools in Turkey. The teachers attended a 40 hour STEM training course in which STEM based activities performed. Pre-service Teachers’ Integrative STEM Teaching Intention Questionnaire was used to measure teachers' views on STEM teaching. The results showed that STEM based activies had positive effect on teachers’ views about STEM education.
Small spaces, big impacts: contributions of micro-environmental variation to population persistence under climate change

Abstract Individuals within natural populations can experience very different abiotic and biotic conditions across small spatial scales owing to microtopography and other micro-environmental gradients. Ecological and evolutionary studies often ignore the effects of micro-environment on plant population and community dynamics. Here, we explore the extent to which fine-grained variation in abiotic and biotic conditions contributes to within-population variation in trait expression and genetic diversity in natural plant populations. Furthermore, we consider whether benign microhabitats could buffer local populations of some plant species from abiotic stresses imposed by rapid anthropogenic climate change. If microrefugia sustain local populations and communities in the short term, other eco-evolutionary processes, such as gene flow and adaptation, could enhance population stability in the longer term. We caution, however, that local populations may still decline in size as they contract into rare microhabitats and microrefugia. We encourage future research that explicitly examines the role of the micro-environment in maintaining genetic variation within local populations, favouring the evolution of phenotypic plasticity at local scales and enhancing population persistence under global change.
Visualization Framework for High-Dimensional Spatio-Temporal Hydrological Gridded Datasets using Machine-Learning Techniques

Numerical modelling increasingly generates massive, high-dimensional spatio-temporal datasets. Exploring such datasets relies on effective visualization. This study presents a generic workflow to (i) project high-dimensional spatio-temporal data on a two-dimensional (2D) plane accurately (ii) compare dimensionality reduction techniques (DRTs) in terms of resolution and computational efficiency (iii) represent 2D projection spatially using a 2D perceptually uniform background color map. Machine learning (ML) based DRTs for data visualization i.e., principal component analysis (PCA), generative topographic mapping (GTM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are compared in terms of accuracy, resolution and computational efficiency to handle massive datasets. The accuracy of visualization is evaluated using a quality metric based on a co-ranking framework. The workflow is applied to an output of an Australian Water Resource Assessment (AWRA) model for Tasmania, Australia. The dataset consists of daily time series of nine components of the water balance at a 5 km grid cell resolution for the year 2017. The case study shows that PCA allows rapid visualization of global data structures, while t-SNE and UMAP allows more accurate representation of local trends. Furthermore, UMAP is computationally more efficient than t-SNE and least affected by the outliers compared to GTM.
Texture-aware Network for Smoke Density Estimation

Smoke density estimation, also termed as soft segmentation, was developed from pixel-wise smoke (hard) segmen-tation and it aims at providing transparency and segmentation confidence for each pixel. The key difference between them lies in that segmentation focuses on classifying pixels into smoke and non-smoke ones, while density estimation obtains inner transparency of smoke component rather than treat all smoke pixels as an equal value. Based on this, we propose a texture-aware network being able to capture inner transparency of smoke components rather than merely focus on general smoke distribution for pixel-wise smoke density estimation. Besides, we adapt the Squeeze-and-Excitation (SE) layer for smoke feature extraction by involving max values for robustness. In order to represent inhomogeneous smoke pixels, we proposed a simple yet efficient attention-based texture-aware module that involves both gradient and semantic information. Experimental results show that our method outperforms others in both single image density estimation or segmentation and video smoke detection.
An Approach for COVID-19 Identification from Chest X-ray Images Using High-Resolution Networks

In this paper, we present an approach for COVID-19 identification from chest X-ray images by using high-resolution neural networks. These networks allow to connect high-to-low convolution streams in parallel. They can maintain high-resolution representations and generate different resolutions throughout the whole process. The high-resolution based models have shown the superior performance in several applications. The experiments were evaluated on a collection of three data sources containing 24,786 lung X-ray images, which were categorized into three classes including covid pneumonia, non-pneumonia, and viral pneumonia. The proposed approach can attain the overall accuracy of 98.2% and 97.56% for the training and testing set, respectively. The accuracy for each class is 99.37%, 94.83%, and 97.27%, respectively, for non-pneumonia, covid-pneumonia, and viral-pneumonia.
Sociodemographic and clinical characteristics of adults admitted to the psychiatric service of a tertiary hospital in the emirate of Abu Dhabi, United Arab Emirates

Background: While the prevalence of mental disorders continues to increase globally and most mental health services in Abu Dhabi are delivered by the tertiary psychiatric hospital, scant information is available to inform mental health service development in the emirate. This study described the sociodemographic and clinical characteristics and discharge dispositions of patients who use this service. Methods: An electronic health record review was conducted on a randomly selected sample (n=285) of patients aged 18 years and older admitted to the psychiatric hospital in Abu Dhabi city from 1 January 2015 to 31 December 2015. Results: Eighty-seven percent of patients were from Abu Dhabi region, 75.8% were aged 18-34 years and 69.5% were male. The most common discharge diagnoses were mood disorders (29.5%) and psychotic disorders (24.9%), except among Emirati males who were most commonly diagnosed with substance use disorders (48.2%). The psychiatric hospital was most commonly identified as the intended provider of follow-up care (52.6%) and 94% of these patients were scheduled to receive out-patient care delivered by psychiatrists. At least 32.3% of citizens of other countries had discharge plans indicating they required follow-up in their home countries. Conclusions: With the limited resources available to fund mental health services and given that specialized psychiatric hospitals are costly, understanding those who use the specialized psychiatric service in Abu Dhabi is critical to addressing the current and projected treatment gaps. This study informs the development of an effective local model of service delivery that matches best-fit clinicians with service users across a range of services that are humane, effective and sustainable over the long-term.
Irrigation scheduling techniques and irrigation frequency on capsicum growth and yield

The present study aimed to evaluate different irrigation scheduling strategies on capsicum growth and yield inprotected environment. The experiment was carried out at the Northeastern of Brazil. Five irrigation scheduling techniques to define water depth (weighing lysimeter, Hargreaves-Samani equation, Piché evaporimeter, tensiometer and soil moisture sensor) andtwo application frequencies (F1-once a day and F2-alternating frequency) were tested. A completely randomized factorial design experiment was installed in a 5 x 2 factorial scheme, with eight replicates. It was observed that the variables stem diameter and leaf area index were influenced by the irrigation scheduling techniques, and treatments based on Hargreaves-Samani and lysimeter scheduling methods led to the lowest values. Fruit biometric parameters were significantly affected only by the Hargreaves-Samani treatment. It can be concluded that both irrigation scheduling techniques and frequencies influenced capsicum growth and yield. Furthermore, irrigation management techniques based on soil sensors caused the highest yields.
Single and combined effects of Drosophila suzukii and Drosophila melanogaster on sour rot development in viticulture

Sour rot is a disease complex that causes serious damage in viticulture. The common vinegar fly Drosophila melanogaster (Diptera: Drosophilidae) is associated with sour rot in overripe or otherwise damaged grapes. Drosophila suzukii (Diptera: Drosophilidae) is an invasive species, which is suspected to induce sour rot in previously undamaged grapes due to the flies' ability to infest healthy, undamaged soft fruits with its serrated ovipositor. As a consequence, infection of healthy grapes by D. suzukii may facilitate the colonization by D. melanogaster. We investigated the single and combined effects of D. suzukii and D. melanogaster on sour rot development by measuring volatile acidity under near‐natural conditions in the vineyard, along with laboratory experiments under controlled climate. In 2017, the combined field and laboratory experiments suggested that the presence of D. suzukii and D. melanogaster increased the volatile acidity levels at a similar rate. In 2018, the field experiments showed an only marginal increase in sour rot development in treatments with both Drosophila species. Under more favourable laboratory conditions, the presence of D. suzukii, but not D. melanogaster triggered sour rot emergence. A facilitating effect of D. suzukii infestation for D. melanogaster was not detectable. These findings suggest that D. suzukii does in fact have the potential to trigger sour rot, but will probably rarely do so under field conditions in the vineyard, at least in the studied region. Instead, our study showed that D. melanogaster can have a similar impact on sour rot development as D. suzukii, emphasizing the need of comparative studies.