Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of Prediction Model of Renal Damage in Children with Henoch-Schönlein Purpura Based on Machine Learning

Objective The children with Henoch-Schönlein purpura (HSP) may suffer from renal insufficiency, which seriously affects the life and health of the children. This study aims to construct a prediction model of Henoch-Schönlein purpura nephritis (HSPN). Methods A total of 240 children with HSP treated in dermatology and pediatrics in our hospital were selected. The general information, patients' clinical symptoms, and laboratory examination indicators were collected for feature selection, and the XGBoost algorithm prediction model was built. Results According to the input feature indexes, the top ten crucial feature indicators output by the XGBoost model were urine N-acetyl-β-D-aminoglucosidase, urinary retinol-binding protein, IgA, age, recurrence of purpura, purpura area, abdominal pain, 24-h urinary protein quantification, percentage of neutrophils, and serum albumin. The areas under the curves of the training set (0.895, 95% CI: 0.827-0.963) and test set (0.870, 95% CI: 0.799-0.941) models were similar. Conclusion The prediction model based on XGBoost is used to predict HSP renal damage based on clinical data of children, which can reduce the harm caused by invasive examination for patients.
FCJ-205 Life and Labour of Rovers on Mars: Toward Post-Terrestrial Futures of Creative Robotics: Toward Post-Terrestrial Futures of Creative Robotics

Four Earth-born, human-made robotic rovers have successfully landed on Mars. Equipped with a range of sophisticated technical instruments for imaging, sensing, measuring, data processing, communication and navigation, these semi-autonomous devices follow the directives of their human “drivers”, performing exploratory observations, assessments and evaluations and reporting the findings back to their command centres on Earth. In this paper I explore the ways in which robotic exploration of Mars facilitates productive exchanges within the ontological nexus of the human–technological, forging new configurations between the ambits of life and labour which may determine the prospects of postterrestrial robotic futures. doi: 10.15307/fcj.28. 205.2017 Curiosity rises early; it is bitterly cold on Mars at around 5am when NASA’s Jet Propulsion Laboratory (JPL) wakes up the rover with a different popular tune each day (from Hit the Road Jack and Walking on Sunshine to the Beastie Boys' Intergalactic and the Star Wars theme), and delivers it a list of its daily tasks. Curiosity then sets out through the dust haze enfolding the rusty terrain scorched by solar winds and galactic cosmic rays on its busy schedule to explore the red planet. Rolling on its six wheels across the uneven floor of the Gale Crater, the 2.2 metre tall, 2.3 metre wide and 2.9 metre long rover searches for clues about Mars' habitability. It uses instruments such as hazard avoidance cameras, an Alpha particle X-ray spectrometer and a radiation detector to navigate its Martian environment, sense it, measure its various properties and evaluate its capacity for hosting carbon-based forms of life. The rover pauses at times and uses its 2 metre long arm to take samples of soil and investigate their composition. Coming to a chunk of rock in its path, it engages a vaporising laser to turn it into a fine powder in order to scrutinise it more closely. It slowly but steadily roams a desolate landscape, taking images to document its progress and using its high-gain antennas to report back to Earth. It finishes its day at about 4pm Martian time, and turns off into a sleep mode until the next morning. Since its landing Curiosity has repeated this routine, and it will continue to do so until its instruments stop working, its body parts deteriorate and its battery is irreparably exhausted. And then, when its power source dries up, its vital functions collapse and its links with Earth are severed, Curiosity will halt to rest in the environs of Mars.
Constraint Guided Neighbour Generation for Protein Structure Prediction

Protein structure prediction (PSP) is essential for drug discovery. PSP involves minimising an unknown scoring function over an astronomical search space. PSP has achieved significant progress recently via end-to-end deep learning models that require enormous computational resources and almost all known proteins as training data. In this paper, we develop conformational search methods for PSP based on scoring functions involving geometric constraints learnt by deep learning models. When machine learning models achieve generality and thus obviously loose accuracy, conformational search methods could perform protein-specific fine tuning of the predicted conformations. However, effective conformational sampling in PSP remains a key challenge. Existing conformational search algorithms adopt random selection approaches for neighbor generation and thus greatly depend on luck. We propose a new approach to analyse geometric constraint-based scores, to identify the regions of the current conformations causing inferior scores, and to alter the identified regions to generate neighbour conformations. Our approach prefers informed decisions to random selections from an artificial intelligence perspective. The proposed method also provides promising search guidance as it obtains significant improvements from given initial conformations. Our approach significantly outperforms state-of-the-art PSP search algorithms that use random sampling with a similar scoring function on a set of benchmark proteins of varying types and sizes. Our sample generation approach could be used in other bioinformatics research areas requiring search.
Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal

Over the last few years, the fourth industrial revolution has attracted more and more attentions all around the world. In the current literature, there is still a lack of efforts to systematically review the state of the art of this new industrial revolution wave. The aim of this study is to address this gap by investigating the academic progresses in Industry 4.0. A systematic literature review was carried out to analyse the academic articles within the Industry 4.0 topic that were published online until the end of June 2016. In this paper, the obtained results from both the general data analysis of included papers (e.g. relevant journals, their subject areas and categories, conferences, keywords) and the specific data analysis corresponding to four research sub-questions are illustrated and discussed. These results not only summarise the current research activities (e.g. main research directions, applied standards, employed software and hardware), but also indicate existing deficiencies and potential research directions through proposing a research agenda. Findings of this review can be used as the basis for future research in Industry 4.0 and related topics.
Research on YOLO-v3 road target detection based on the combination of K-means++ algorithm and cross-entropy loss function

The accurate detection of targets on the road is of great significance (for the big data of road system). However, the traditional detection suffers from missed detection and false detection. In this work, an improved algorithm was proposed based on Yolo-v3 algorithm. The replacement of traditional K-means clustering algorithm with K-means++ algorithm endowed the system a more suitable size of detection frame. Meanwhile, the detection sensitivity of small or incomplete targets was obviously improved with the utilization of cross entropy loss function. The detection experiments on vehicles and pedestrians were performed in two actual situations, normal situation and congested situation. The average detection accuracy was increased by 2.46% with the improvement of clustering algorithm, and the accuracy was increased by 2.08% with the improvement of loss function. The average accuracy was increased by 3.56% with the combination of K-means++ algorithm and cross-entropy loss. The experiments results showed that the combined algorithm possessed a significant improvement in average accuracy, recall and detection speed. This research provides new ideas for the establishment of high-precision algorithms for road dynamic target detection.
Food promotion in supermarket promotional circulars: an analysis of the cover and internal content

Objective. To analyze food promotion on the cover and inside pages of supermarkets circulars. Methods. Promotional circulars data were collected from five supermarket chains in Rio de Janeiro (Brazil) in August 2019. Food was categorized according to the NOVA classification: fresh food or minimally-processed food (FF/MPF), processed culinary ingredients, processed food, and ultra-processed food (UPF). The density of the food groups on the cover and inside pages of circulars was calculated, and the comparison was performed using paired T-test or paired Wilcoxon test, depending on the normality and homogeneity of the data distribution. Results. A total of 59 circulars were analyzed and 7665 foods were identified, of which 55.6% UPF and 27.7% FF/MPF. FF/MPF are more present on the cover while the UPF are more present on the inside pages of the circulars. Conclusion. The cover of circulars does not reflect the entire contents. On the cover, foods with better nutritional profile are more present than inside of the circulars. It is necessary to develop government programs that regulate the promotion of foods in supermarkets circulars.
Named entity recognition and its application to ceramic coating literature

Literature is an important source of materials data. However, unstructured literature data processing faces multiple challenges. Named entity recognition (NER), as a subtask of information extraction, can locate and classify named entities from unstructured text that can be used for database or knowledge base establishment. In this paper, a Bidirectional Mogrifier LSTM-CRF-based NER model (BiMogrifier LSTM-CRF) is proposed for ceramic coating literature processing. The average F1 score of seven types of commonly used material entities reaches 85.82%. Furthermore, based on the extracted entities, a web system is built for a relatively complete application to ceramic coating.
An Introduction to Online Video Game QoS and QoE Influencing Factors

Online video games and cloud gaming are rapidly growing in pervasiveness. Their resource demands can put significant stress on the global communication infrastructure. And network conditions are amongst the chief factors that influence one’s enjoyment while playing games. This makes it imperative for video games to be considered for network dimensioning, server placement or protocol development. For that reason, in this work we provide an introduction to the technical aspects of video games in general and of their network aspects in particular. This understanding forms the basis for a rich taxonomy of factors that influence and provide context to a video game’s Quality of Service (QoS) and Quality of Experience (QoE). The taxonomy covers influence factors from all aspects involved in a video game, from the subjective player and game influence factors to the system and networking influence factors. Finally, this work gives an overview of conducted and ongoing research as well as future research opportunities while taking into account lessons learned from past approaches.
The effect of an educational curriculum using shadow boxing exercises to learning perform the straight punch of boxing for students

The importance of the research lies in the use of shadow boxing exercises to learning perform the straight punch of boxing for students. As for the research problem, it was that learning the basic skills, especially the straight punch, is done through the use of exercises and usual and recognized methods, which leads to boredom or boredom, and thus leads to difficulty and delay in learning the skill due to the lack of performance of the necessary repetitions by the students, which means that they will not have the time Sufficient to perform appropriate and sufficient repetitions.
An Experimental Model of Unconfined Bubbly Lava Flows: Importance of Localized Bubble Distribution

Most lava flows carry bubbles and crystals in suspension. From earlier works, it is known that spherical bubbles increase the effective viscosity while bubbles deformed by rapid flow decrease it. Changes in the spatial distribution of bubbles can lead to variable rheology and flow localization and thus modify the resulting lava flow structure and morphology. To understand the roles of bubble and solid phase crystal distributions, we conducted a series of analog experiments of high bubble fraction suspensions. We poured the analog lava on an inclined slope, observed its shape, calculated the velocity field, and monitored its local thickness. A region of localized rapid flow and low vesicularity, whose thickness is thinner than the surrounding area, develops at the center of the bubbly flows. These features suggest that the locally higher liquid fraction decreases the effective viscosity, increases the fluid density, and accelerates the flow. We also found that a halted particle‐bearing bubbly flow can resume flowing. We interpret this to result from the upward vertical separation of bubbles, which generates a liquid‐rich layer at the bottom of the flow. In our experiment, bubbles are basically spherical and decrease the flow velocity, while our estimate suggests that bubbles in natural lava flows could increase or decrease flow velocity. Downstream decreases in flow velocity stops the bubble deformation and can cause a sudden increase of effective viscosity. The vertical segregation of the liquid phase at the slowed flow front may be a way to generate a cavernous shelly paho’eho’e.
Using Crowd Farms of Human as Power Plants in Public Spaces to Solve Energy Problems Crowd Farms as Human Power Plants (Main Entrance for Mansoura University "El-Baron Gate")

The energy problems today combine these issues: much of the world's population has too little energy to meet basic human needs; the monetary costs of energy are rising nearly everywhere; the environmental impacts of energy supply are growing and already dominant contributors to global environmental problems. Confront with global energy problems will require greatly increased investment in improving the efficiency of energy end use and in reducing the environmental impacts of contemporary energy technologies. It will require financing a transition over the next several decades to a set of more sustainable and renewable energy sources. Truth be told, we can see that everyone considers the human is the main reason for many problems especially which related to energy and environment. In the following paragraph, I’ll point out the importance of Life Energy as a solution of energy problems. In streets, malls, buildings, clubs, gym, also at homes. Only by considering the human isn’t a reason of wasting energy but is a resource of generating power, from his movement, heat, playing, breathe, from his life as a clean, sustainable and renewable energy resource. The denial that the energy generated from human body in power plants seems to me to constitute a fundamental misunderstanding about the real human role in the life and society. Human generating power, Start using the thermal and kinetic energy of people in a lot of buildings. To recapitulate, what we have here is an exposition of how we can deal with crowded farms in Egypt as a human power plant to generate life energy. The intriguing ideas expressed here open the door to questions about how we can use the population in solving our problems, can we consider life energy a new renewable source of energy, what is the efficiency of energy generated from human.
A Farm Information Model for Development and Configuration of Interoperable ICT Components to support Collaborative Business Processes

Farm enterprises2 The objective of this paper is to describe a farm information model and a proof of concept that demonstrates how a collaborative Business Process for farming can be configured using this farm information model. Knowledge to develop this model and a proof of concept is obtained by case study research focusing on the collaborative Business Processes of spraying and crop protection of potatoes against late blight disease. need to collaborate with numerous actors that are part of Agri-Food Supply Chain Networks (AFSCNs) such as governments, advisory services, contractors, processors, input providers and certification bodies. This collaboration is required to produce food in a more sustainable, safe and transparent manner. To collaborate efficiently and effectively, information needs to be shared within collaborative Business Processes. The information sharing within such collaborative Business Processes should be supported by an ICT infrastructure consisting of interoperable ICT Components. Currently, most of the available ICT Components are not interoperable, hindering data exchange between ICT Components of various vendors. Consequently, this situation is hindering optimization of farm production processes and collaboration in AFSCNs. Therefore, a platform, called FIspace, is being established for multiple domains that support the development and configuration of interoperable ICT Components into a system that is able to support collaborative farm Business Processes. To develop interoperable ICT Components and configure these in an easy and flexible manner to support farm enterprises, a farm information model is, amongst other models, required. The presented farm information reference model is able to describe the relations between a farm enterprise and its collaborators, the Business Processes related to the supporting ICT Components and the data messages for data exchange between ICT Components.
Simulation implementation of intelligent track-to-track association algorithm for multi-radar networking

This paper proposes a track association algorithm based on convolutional neural network. The algorithm uses multi-time history correlation local track estimation, and adjusts the position coordinate estimation in the track data to classify the track association. It is used to solve the technical difficulties of track splitting and redundancy caused by track association of multiple radar stations. After simulation analysis, the results show that the intelligent track association algorithm proposed in this paper has higher correct association probability and better universality.
An Integrator-Differentiator Transimpedance Amplifier Using Tunable Linearized High-Value Multi-Element Pseudo-Resistors

In this paper, we present an integrator-differentiator transimpedance amplifier (I-D-TIA) with a dc compensation, which incorporates widely tunable multi-element pseudo-resistors (MEPRs) in its dc servo loop and ac signal path. The implemented MEPR in the dc path is continuously tunable from <inline-formula> <tex-math notation="LaTeX">$460 \,\mathrm {k\Omega }$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$300 \,\mathrm {G\Omega }$ </tex-math></inline-formula> allowing the TIA to process dc currents with a dynamic range of more than <inline-formula> <tex-math notation="LaTeX">$100 \,\mathrm {dB}$ </tex-math></inline-formula>. The MEPR in the differentiator ac signal path provides a tunable resistance between <inline-formula> <tex-math notation="LaTeX">$0.3 \,\mathrm {M\Omega }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$100 \,\mathrm {M\Omega }$ </tex-math></inline-formula>, resulting in an overall ac transimpedance between <inline-formula> <tex-math notation="LaTeX">$3 \,\mathrm {M\Omega }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1 \,\mathrm {G\Omega }$ </tex-math></inline-formula>. For the lowest ac transimpedance, a bandwidth of <inline-formula> <tex-math notation="LaTeX">$10 \,\mathrm {MHz}$ </tex-math></inline-formula> is achieved. The TIA provides a minimum input-referred current noise density of <inline-formula> <tex-math notation="LaTeX">$1.6\,\mathrm {fA}/\mathrm {\sqrt {Hz}}$ </tex-math></inline-formula>. The implemented MEPR has been optimized regarding its high-frequency noise by minimizing its parasitic capacitances. The MEPR shows an inherent shot noise suppression such that its noise stays close to the theoretical thermal noise limit and significantly below the theoretical shot noise limit, even for large dc currents. By using a sub-VSS supply for the MEPR, the asymmetry in its output characteristic is greatly reduced, leading to a linear signal swing of <inline-formula> <tex-math notation="LaTeX">$1.5\,\mathrm {V_{pp}}$ </tex-math></inline-formula> with a THD below <inline-formula> <tex-math notation="LaTeX">$1 \mathrm {\%}$ </tex-math></inline-formula> on a <inline-formula> <tex-math notation="LaTeX">$1.8-\mathrm {V}$ </tex-math></inline-formula> supply. Thanks to this high linearity, large bandwidth, and high dc current dynamic range, the proposed TIA can be used in a wide variety of applications from high-sensitivity, low-bandwidth lock-in detection to transient current sensing with sub-microsecond timing resolutions.
Publicity or impact? The use of crowdfunding by Indonesian social media influencers during the covid-19 pandemic

Indonesia is marching toward tackling challenges during the endless Covid-19 pandemic. Under a situation known as VUCA (volatile, uncertain, complex, and ambiguous), a troop of social media influencers (SMI) paved the way to create crowdfunding initiatives. Shedding from the discussion of disaster capitalism, where individual or entities make profit from large-scale crises, also take advantage of the destabilized atmosphere, this paper analyses the initiatives of three Indonesian social media influencers in raising funds through Kitabisa.com from March to November 2020. Under the hashtag #BersamaLawanCorona (together fight the corona), the biggest crowdfunding platform, Kitabisa.com has gathered more than 158 billion rupiahs (11,2 million USD) taken from 5.655 campaigns during March to November 2020 period. These efforts are mainly pioneered by SMIs and their followers. Combining desk review and social media analysis method, the paper revealed that the crowdfunding initiatives by SMIs portrayed two sides of a coin: on the one hand reflects the emergence of solidarity and social contribution to create a positive impact, and on the other, generates publicity. It is, however, too quick to conclude whether the initiatives reflect a practice of disaster capitalism, because the publicity that they generated for these crowdfunding initiatives were not higher compare to publicity they gained from their everyday content.
Experimental Study on Generative Adversarial Network for Precipitation Nowcasting

Precipitation nowcasting is an important task, which can be used in numerous applications. The key challenge of the task lies in radar echo map prediction. Previous studies leverage Convolutional Recurrent Neural Network (ConvRNN) to address the problem. However, the approaches are built upon mean square losses and the results tend to have inaccurate appearances, shapes and positions for predictions. To alleviate this problem, we explore the idea of adversarial regularization, and systematically compare four types of Generative Adversarial Networks (GANs), which are the combinations of GAN/Wasserstein GAN and its multi-scale version. Extensive experiments on a real-world radar data set and four typical meteorological examples are conducted. The results validate the effectiveness of adversarial regularization. The developed models show superior performances over the existing prediction approaches in the majority circumstances. Moreover, we find that the Wasserstein GAN regularization often delivers better results than the GAN regularization due to its robustness, and the Multi-scale Wasserstein GAN, in general, performs the best among all the methods. To reproduce the results, we release the source code at: https://github.com/luochuyao/MultiScaleGAN and the test system at: http://39.97. 217.145:80/.