Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Prototyping Mandible Model for Dental Implant Surgery Simulation

AbstractThe dental implant surgery results usually depend on dentists’ clinical experiences and judgments. Most implant surgery utilizes the software to process pre-operation planning and simulation. Hence, this research intended to apply the rapid prototyping (RP) technique to build real mandible model for implant surgery simulation. Unlike other simple mandible RP models, vessels and nerves pathways are demonstrated in our model to help dentists to avoid false drillings on them. Besides, orientation pillars for surgical guide are included in the model. Moreover, for comparison’s purpose, the platforms of placing indexing balls are also integrated. Computed tomography (CT) data of mandible were first converted to a CAD model with vessels, nerve, orientation pillars, and indexing platforms by a medical imaging processing software. RP technique was then utilized to obtain a real 3D model for pre-operation planning, surgical guide fabrication, and surgery simulation. The mandible model helped to drill holes.
Self-Paced Learning and Privileged Information based Cascaded Multi-column Classification algorithm for ASD diagnosis

 Abstract — Autism spectrum disorder (ASD) is one of the most serious mental disorder in children. Machine learning based computer aided diagnosis (CAD) on resting-state functional magnetic resonance imaging (rs-fMRI) for ASD has attracted widespread attention. In recent years, learning using privileged information (LUPI), a supervised transfer learning method, has been generally used on multi-modality cases, which can transfer knowledge from source domain to target domain in order to improve the prediction capability on the target domain. However, multi-modality data is difficult to collect in clinical cases. LUPI method without introducing additional imaging modality images is worth further study. Random vector function link network plus (RVFL+) is a LUPI diagnosis algorithm, which has been proven to be effective for classification tasks. In this work, we proposed a self-paced learning based cascaded multi-column RVFL+ algorithm (SPL-cmcRVFL+) for ASD diagnosis. Initial classification model is trained using RVFL on the single-modal data (e.g. rs-fMRI). The output of the initial layer is then sent as privileged information (PI) to train the next layer of classification model. During this process, samples are selected using self-paced learning (SPL), which can adaptively select simple to difficult samples according to the loss value. The procedure is repeated until all samples are included. Experimental results show that our proposed method can accurately identify ASD and normal control, and outperforms other methods by a relatively higher classification accuracy.
Joint Inversion of Helicopter-Borne Electromagnetic Data Using Marquardt's Algorithm

The inversion stability of geological data can be enhanced by increasing the number of observation data or improving the accuracy of it. For the purpose of increasing the input parameters, the joint inversion algorithm based on Marquette’s method is introduced to the helicopter-borne electromagnetic system. Since double parameters of both the horizontal coplanar and vertical coaxial coils were taken into account, the stability of the inversion process is greatly improved. In fact, the joint inversion algorithm not only enhances the inversion’s stability, but also overcomes the limitation of the inversion with each single device in order to make the result closer to the real resistivity characteristics
To reduce side lobe level of slotted array antennas using nonuniform waveguides

In this article, a method is presented to reduce the side lobe level of slotted waveguide array antennas while the gain to be constant. In this method, the H‐plane dimension of the waveguide is considered as the variable rather than constant. The nonuniformity of waveguide walls obviates the need for offset of slots and thereby reduces the side lobe level of radiation pattern. A slotted nonuniform waveguide is designed at frequency 10 GHz and then fabricated and tested. © 2015 The Authors International Journal of RF and Microwave Computer‐Aided Engineering Published by Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:42–46, 2016.
Numerical‐experimental simulation of active flutter control for bridges

A coupled numerical and experimental simulation is used to investigate active flutter control for wind‐induced vibrations of a bridge girder. In addition to performance, the stability of a controlled aeroelastic structure is of great concern. The analytical model of the aeroelastic plant including flutter derivatives, is parametrized by frequency of motion and wind velocity. The flutter derivatives are obtained by the forced vibration method. The critical wind speeds of the uncontrolled and the controlled structure are determined analytically using the Hurwitz criterion and experimentally using the numerical–experimental set‐up, which includes a scaled model in a water channel. The analytical and experimental results show good agreement. Control scenarios under smooth and turbulent flow conditions have been simulated. Furthermore, the effect of saturation in active control on performance and on flutter stability has been studied. The example of a long‐span suspension bridge is used to demonstrate the effectiveness of the proposed coupled numerical and experimental scheme for flutter control simulation. Copyright © 2004 John Wiley & Sons, Ltd.
Improving the energy-efficient envelope design for Moroccan houses

The building sector accounts for about 36% of final energy consumption in Morocco, with 29% reserved for residential and 7% for the tertiary sector. This energy consumption is expected to increase in view of major projects initiated and planned by the government in key sectors of the Moroccan economy. Thus, this article presents a dynamic energetic study for an individual house building located in Oujda city (Morocco) by using TRNSYS software. A typical meteorological year (TMY) corresponding to Oujda city was established and used to determine the required energy for obtaining the thermal comfort in winter and summer conditions. Special attention was paid to the effect of different parameters, such as insulation and thermal mass, colour of external surfaces, window type and area and shading on the annual required energy. Results show that annual heating required energy is reduced by 8.38% and the annual cooling required energy is reduced by 70.54% by using 6 cm of EPS thermal insulation to the outside of exterior walls. The window area plays an important role in building energy consumption in the Moroccan environment. A saving of 1.91% in annual required heating energy and 6% in annual cooling required energy for the base case house can be achieved by replacing the single glazing by double glazing.
An Automatic Petechia Dots Detection Method on Tongue

Tongue diagnosis with features like tongue coating, petechia, color, size and so on is of great effectiveness and convenience in traditional Chinese medicine. With the development of image processing techniques, automatic image processing can reduce hospital inspection for patients. However, there are ubiquitous problems of inadequate accuracy in petechia dots detection with previous methods. In this paper, we propose a method of petechia dots detection on tongue based on SimpleBlobDetector function in OpenCV library and support vector machines model, which improves the detective accuracy. We test 128 clinic tongue images and select 9 of the images with plentiful petechia dots for further experiments. Our method achieves mean value of false alarm rate 4.6% and missing alarm rate 11.8%, which have 19.4% and 8.2% reduction respectively compared to previous work.Clinical Relevance—The method can provide detailed information of tongue, which assists doctors to investigate curative effect.
Designing a wiki-based pedagogy in embedded system education

This paper designs a wiki-based pedagogy and explores its potential in embedded system education. This pedagogy comprises three key elements. The first is a wiki system, which is used as the collaborative learning environment. The second element is the student team achievement division (STAD), which is used as the teaching strategy. The third element is the hierarchical scaffolding, which is designed to assist students in performing their embedded system design project. To investigate the potential of the proposed pedagogy, the technology acceptance model (TAM) was used as the theoretical basis of experiment. A total of 21 university students participated in the experiment, and the partial least squares (PLS) method was used to treat the small samples. The results showed that the wiki system and the hierarchical scaffolding have a positive effect on system usage, while the strategy of STAD did not play a significant role in influencing learners to accept the proposed pedagogy.
HOWLEAKY? THE JOURNEY TO DEMYSTIFYING SIMPLE TECHNOLOGY

A decision support tool has been developed to demystify the art of computer simulation. Howleaky? represents a revision of water balance and crop models with the aim of providing insight into the impact of different land uses and management on water balance, soil erosion and water quality. Howleaky? ’s distinctiveness lies in its user interface which provide accessible and highly visual representation of inputs and outputs. Howleaky? represents the latest instalment in daily time step models that are physically based. Similar models have been in use for at least 25 years, but mainly used by a few computer literate “gurus”. The capacity to simulate transport of agricultural chemicals in surface runoff has also been included in the model (Rattray et al., these proceedings). Model verification has shown that similar fits to observed data can be achieved using Howleaky? as have been achieved using similar models that have more detailed crop models and capacity to modify management. Key questions explored by Howleaky? include; how much water is lost as runoff and drainage, what erosion rates are expected from different tillage practices, what are the probabilities of losing pesticides applied, and what are the interactions between crop type, climate and tillage management.
A Study on Functional and Nutritional Characteristics of Barnyard Millet and Foxtail Millet

The chemical analysis of both foxtail and barnyard millets showed excellent sources nutrients and phytochemicals. The total phenolic content value of foxtail millet was 0.60 compared to barnyard millet 0.227 mg of Gallic Acid Equivalent (GAE) / g of sample. Foxtail millet showed the highest total flavonoid content 28.94 mg when compared to barnyard millet 7.61 mg of Quercetin/ g of sample. Regarding tannin content Foxtail millet showed the highest tannin value 12.97 when compared to barnyard millet 6.67 mg of Tannic acid equivalent / g of sample. Both foxtail and barnyard millets exhibited very good cooking performance in cooking quality of rice, fermentation performance with regard to preparation of idli and dosa and excellent performance in development of bakery products. Hence these millets offer great opportunities for diversified utilization and value addition in various food products. Food & Nutrition Journal Sadhu VJ and Naidu S. Food Nutr J. 6: 234. https://www.doi. org/ 10.29011/2575-7091.100234 www.gavinpublishers. com
Greater Innovation by the Crowd in Crowdsourcing: The Sequencing of Knowledge Types That Balance Divergence and Convergence

This research focuses on an organizational form - collaborative crowdsourcing for innovation - in which the public is asked to collaboratively solve, online, an organizational problem in innovative ways over a reasonably short period of time (i.e., days or weeks). Using the lens of creative synthesis (Harvey 2014), the research seeks to address the question of how sequences of knowledge contributions independently offered by different participants affect the emergence of innovative solutions. Through an analysis of time-stamped contributions made in seven collaborative crowdsourcing events, the findings show that certain exemplar sequences have a positive impact on the emergence of innovative solutions in the crowd. On the other hand, some other sequences can negatively impact the emergence of innovative solutions. Specifically, the findings show that the emergence of innovative solutions are more likely after sequences from different contributors in which: 1) one contributor offers an early idea seed after others offer problem facts and analogies (rather than the inverse in which idea seeds followed by problem facts and analogies), and 2) one contributor describes a paradox which is followed by others adding more problem facts (rather than the inverse in which paradoxes are posted after facts). Additionally, innovative solutions are more likely to emerge subsequent to a single contribution of a paradox. However, multiple paradoxes raised sequentially dampen the likelihood of an innovative
Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists

Healthcare agents, in particular in the oncology field, are currently collecting vast amounts of diverse patient data. In this context, some decision-support systems, mostly based on deep learning techniques, have already been approved for clinical purposes. Despite all the efforts in introducing artificial intelligence methods in the workflow of clinicians, its lack of interpretability - understand how the methods make decisions - still inhibits their dissemination in clinical practice. The aim of this article is to present an easy guide for oncologists explaining how these methods make decisions and illustrating the strategies to explain them. Theoretical concepts were illustrated based on oncological examples and a literature review of research works was performed from PubMed between January 2014 to September 2020, using “deep learning techniques,” “interpretability” and “oncology” as keywords. Overall, more than 60% are related to breast, skin or brain cancers and the majority focused on explaining the importance of tumor characteristics (e.g. dimension, shape) in the predictions. The most used computational methods are multilayer perceptrons and convolutional neural networks. Nevertheless, despite being successfully applied in different cancers scenarios, endowing deep learning techniques with interpretability, while maintaining their performance, continues to be one of the greatest challenges of artificial intelligence.
Low-Cost Sustainer with a Simple Energy Recovery Circuit Utilizing Parallel Resonance

A low-cost sustainer with a simple energy recovery (ER) circuit for plasma display is proposed in this paper. The proposed ER circuit employs a bidirectional switch consisting of one switch and four diodes. In the proposed sustainer, the insulated gate bipolar transistor (IGBT) can be used as a switch. Since all power switches are turned off under zero-current switching (ZCS), the tail current problem associated with IGBT does not occur in the proposed sustainer. Theoretical analysis and performance of the proposed ER circuit were verified on an experimental prototype operating at 200 kHz switching frequency.
Realizing Equity and Inclusion Goals in the Design of MOOCs

Our research explores coherence between diversity, equity, and inclusion goals that faculty articulate in advance of the design process and their enactment within massive open online courses (MOOCs). The purpose of the study is to gain an understanding of the types of goals identified by faculty within course design proposals and how those goals are instantiated in corresponding course designs when working with design teams. Our team analyzed 11 single MOOC and MOOC series proposals to characterize the design goals stated. Following the proposal analysis, we analyzed 32 corresponding courses to identify instances in which stated goals related to diversity, equity, and inclusion were realized. Our analysis revealed patterns between proposed goal types and the ways in which goals manifest in courses related to the way in which content or learning processes were central to the design. We intend to use the results to inform the development of processes to engage in a systematic and purposeful approach for the realization of equitable and inclusive design goals in MOOCs.
COMPARISON OF INSIDE CONTACT PHASE AND OUTSIDE CONTACT PHASE IN CURVED SPRINTING

One of differences of running between in straight path and in bent path is the body lean inward. When athletes run in the bent path, athletes are influenced by centrifugal force. Athletes produce the medio-lateral component of ground reaction force (GRF) with inclining the body inward, to balance the centrifugal force. The centripetal force can be estimated by F=mv2 where F is the centripetal force, m is the mass of the body, v is the running velocity, and r is the radius of track. We can see from this equation, the influence of the running velocity on centripetal force is strong. So we can’t ignore the centripetal force in sprint race (i.e. 200m dash). The body lean angle increase, as the running velocity increase. Athletes must change the running direction along the bent path. One step of running consist of the flight phase and the contact phase. It is impossible to change running direction during the flight phase. Therefore it is expected to change running direction during the contact phase. It was said the functions of inside and outside legs were asymmetrical (Stoner and Ben-Sira, 1979, Hamill et al, 1987). But there are no study to compare the inside foot contact phase with the outside foot contact phase with in relation to the centripetal force and the body lean angle. The purpose of this study is to compare the inside (left) foot contact phase with the outside (right) foot contact phase with in relation to the centripetal force and the body lean angle. Then we would obtain new knowlege about curved sprinting.
X-ray Diffraction Analysis to Explore Molecular Traces of Eccentric Contraction on Rat Skeletal Muscle Parallelly Evaluated with Signal Protein Phosphorylation Levels

We performed X-ray diffraction analyses on rat plantaris muscle to determine if there are strain-specific structural changes at the molecular level after eccentric contraction (ECC). ECC was elicited in situ by supramaximal electrical stimulation through the tibial nerve. One hour after a series of ECC sessions, the structural changes that remained in the sarcomere were evaluated using X-ray diffraction. Proteins involved in cell signaling pathways in the muscle were also examined. ECC elicited by 100, 75, and 50 Hz stimulation respectively developed peak tension of 1.34, 1.12 and 0.79 times the isometric maximal tetanus tension. The series of ECC sessions phosphorylated the forkhead box O proteins (FoxO) in a tension-time integral-dependent manner, as well as phosphorylated the mitogen-activated protein kinases (MAPK) and a protein in the mammalian target of rapamycin (mTOR) pathway in a maximal tension dependent manner. Compared to isometric contractions, ECC was more efficient in phosphorylating the signaling proteins. X-ray diffraction revealed that the myofilament lattice was preserved even after intense ECC stimulation at 100 Hz. Additionally, ECC < 75 Hz preserved the molecular alignment of myoproteins along the myofilaments, while 75-Hz stimulation induced a slight but significant decrease in the intensity of meridional troponin reflection at 1/38 nm−1, and of myosin reflection at 1/14.4 nm−1. These two reflections demonstrated no appreciable decrease with triple repetitions of the standard series of ECC sessions at 50 Hz, suggesting that the intensity decrease depended on the instantaneous maximal tension development rather than the total load of contraction, and was more likely linked with the phosphorylation of MAPK and mTOR signaling proteins.
Division I College Athletes’ Self-Perception: Investigating the Impact of Race and Discrimination

Self-perception is the level of competency at which individuals evaluate themselves in certain areas or domains (Marsh & Shavelson, 1985). An individual’s self-perceptions contribute to their global self-worth and even predicts performance (Cuellar, 2014; Harter & Neemann, 2012). This study measures self-perception scores, as well as experiences with racial discrimination, of 306 NCAA Division I college athletes using the Self-Perception Profile for College Students (Harter & Neemann, 2012). Scores are compared across race. Findings suggest that White college athletes have significantly higher self-perception scores than college athletes of color - with recent discrimination (within the last year) as a significant predictor of multiple areas of self-perception. The implications of this study suggest that faculty and other campus stakeholders should pursue positive relationships with the college athletes they encounter. Positive relationships between college athletes and faculty may help raise college athlete self-perceptions, and in turn, performance in a variety of areas.
Model Scaling in Smartphone GNSS-Aided Photogrammetry for Fragmentation Size Distribution Estimation

Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data.
ENERGY EFFICIENT CLUSTERING TECHNIQUE IN WIRELESS SENSOR NETWORKS

: In wireless sensor networks(WSNs), it's far essential to reduce power dissipation and extend community lifetime. The facts amassed by each sensor is communicated via the network to a unmarried processing middle that uses all reported information to determine characteristics of the surroundings or locate an event. The communique or message passing method must be designed to preserve the limited electricity sources of the sensors. Clustering sensors into groups, in order that sensors speak facts handiest to cluster heads after which the cluster heads speak the aggregated information to the processing middle, may also save energy. The goal of this paper is to provide a kingdom of survey on clustering parameters and additionally clustering algorithms reported inside the literature of Wireless Sensor Networks. Our paper also provides energy efficient clustering algorithms in Wireless Sensor Networks.
Unilateral retinal vein occlusion in a young, healthy male following Sputnik V vaccination

2. Bertoli F, Veritti D, Danese C, Samassa F, Sarao V, Rassu N, et al. Ocular findings in COVID‐19 patients: A review of direct This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution‐NonCommercial‐ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non‐commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.