Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis on Vibration and Noise of Vehicle Wheel

The wheel/rail surface roughness is used as responses and the interaction force of wheel/rail is calculated by the model of Vehicle-Track multi-rigid body coupling vibration.The finite element analysis model of vehicle wheel is established by using the theory of finite element method.The interaction force of wheel/rail is used as responses and the frequency response analysis is calculated.The vibration characters from the finite element analysis is used as boundary conditions and a acoustic boundary element model of vehicle wheel is constructed by using the theory of boundary element method,the sound radiation of vehicle wheel is calculated.The results campared to the results of acknowledged model and software is accordant,so the correctness of this method is proved.
Hydraulic Pump Fault Diagnosis Based on D-S Theory Evidence

The basic concept and basic theory about the theory of D-S evidence is introduced,the fault diagnosis method based on the D-S evidence theoretical information fuses is discussed and applied in the fault diagnosis of the hydraulic pump.The result indicates that the information fusion diagnosis method based on theory of D-S evidence can use the complementary and redundancy of multi-sensor information fully,realized more accurate and reliable results than single sensor nerve network.
An Authorization Framework for Preserving Privacy of Big Medical Data via Blockchain in Cloud Server

In recent years, cloud-based medical record sharing has greatly improved the process of researching the disease and patient diagnosis. However, since cloud systems are centralized, there is serious concern about data security and privacy. Blockchain technology is viewed as a promising method of dealing with privacy issues and data security because of its exclusive features of distributed ledgers, secrecy, verifiability, and enhanced security. The literature review has shown significant works on integrating blockchain technology with cloud system for managing and sharing healthcare data. It has been analyzed that previous works are primarily dependent on the centralized data storage approach, which raises privacy concerns. The previous works also do not emphasize handling big medical data and lack the reliability of the end-to-end security features system. This paper has presented an authorization framework for ensuring data security and privacy preservation using blockchain technology with IPFS as decentralized file storage and sharing system. The proposed study devises a proof of replication algorithm using smart contracts to provide a better access control mechanism. The implementation of the proposed framework is based on the symmetric encryption and Ethereum blockchain platform. The study outcome illustrates the efficiency and availability of the proposed scheme compared to the typical cloud-based blockchain method. Keywords—Medical data; cloud; blockchain; data sharing; access control; security; privacy preservation
Predicting the nonlinear amplification of disturbances in a boundary layer using the spatial perturbation equations

Predicting the laminar-turbulent transition process requires capturing the linear and nonlinear amplification of disturbances in laminar flows. Direct numerical simulations (DNS) faithfully predict disturbance growth, but at significant and often impractical expense (Slotnick et al. 2014). Utilizing the properties of slowly evolving shear flows, we can implement spatial marching techniques to reduce computational expense while accurately predicting the amplification of disturbances. The parabolized stability equations (PSE), which are based on a decomposition of the perturbations into slowly and fast-evolving components, are perhaps the best-known example (Bertolotti et al. 1992; Herbert 1997). The PSE can accurately capture the evolution of disturbances for many flows of interest at a fraction of the cost of DNS and thus allow predictions of transition to turbulence (Lozano-Durán et al. 2018). The particular formulation of the PSE, however also introduces fundamental limitations. Neglecting terms in the governing equations based on scaling analysis results in a set of equations that are not fully parabolic (Haj-Hariri 1994). Specifically, the streamwise pressure gradient term introduces residual ellipticity that requires numerical treatment to stabilize the marching procedure as discussed by Towne et al. (2019). Existing remedies include the use of an implicit Euler integration scheme in combination with a minimum step size (Li & Malik 1996, 1997), the disregard of the streamwise pressure gradient term in the governing equations (Chang et al. 1991; Li & Malik 1996), and the modification of the marching procedure via an additional damping term (Andersson et al. 1998). These modifications have undesirable disadvantages that have motivated recent development of alternative spatial marching techniques (e.g. Towne & Colonius 2015; Ran et al. 2019). A recently developed method of one-way spatial marching introduced by Towne & Colonius (2015) stabilizes the spatial marching of linear perturbations by splitting the governing equations using characteristic variables and removing the upstream-traveling components. Further development has led to a projection operator, which allows the inclusion of forcing terms in the governing equations (Towne 2016). The inclusion of any forcing, however, has led to observations of unstable behavior if certain grid conditions are not satisfied (Kamal et al. 2020). Ran et al. (2019) introduced the parabolized Floquet equations to study modal interaction in boundary-layer flows.The method captures the growth of a range of harmonics during the nonlinear amplification of disturbances through the successive linearization of the governing equations. In this work, we present a new approach using the spatial perturbation equations (SPE). The method has been developed as a fully predictive technique that captures the nonlinear amplification of disturbances in slowly varying shear flows. As shown in (Harris
ResqLP: Relation Sequences for Link Prediction in Knowledge Graphs

Summary This project paper focuses on the link prediction (LP) problem for knowledge graphs (KGs). A KG is a collection of structured data that captures facts, or knowledge, about a domain by linking entities with relations. These facts collectively make up a directed graph. The LP problem is concerned with inferring new facts in a KG given its current state. This problem has many practical use cases. For instance, in bioinformatics, LP can be used to predict new possible drug targets and to reason about the interactions of chemical substances. LP is also found useful in question answering and recommender systems.
Metastable olivine within oceanic lithosphere in the uppermost lower mantle beneath the eastern United States

Approximately two-thirds of Earth’s outermost shell is composed of oceanic plates that form at spreading ridges and recycle back to Earth’s interior in subduction zones. A series of physical and chemical changes occur in the subducting lithospheric slab as the temperature and pressure increase with depth. In particular, olivine, the most abundant mineral in the upper mantle, progressively transforms to its high-pressure polymorphs near the mantle transition zone, which is bounded by the 410 km and 660 km discontinuities. However, whether olivine still exists in the core of slabs once they penetrate the 660 km discontinuity remains debated. Based on SKS and SKKS shear-wave differential splitting times, we report new evidence that reveals the presence of metastable olivine in the uppermost lower mantle within the ancient Farallon plate beneath the eastern United States. We estimate that the low-density olivine layer in the subducted Farallon slab may compensate the high density of the rest of the slab associated with the low temperature, leading to neutral buoyancy and preventing further sinking of the slab into the deeper part of the lower mantle.
Association of smoking and smoking cessation

These include: References http://tobaccocontrol.bmj. com/cgi/content/full/17/3/166#BIBL This article cites 32 articles, 15 of which can be accessed free at: Rapid responses http://tobaccocontrol.bmj. com/cgi/eletter-submit/17/3/166 You can respond to this article at: service Email alerting the top right corner of the article Receive free email alerts when new articles cite this article sign up in the box at
IOT Based Intelligent Farming Using Renesas With WebApp

-Agriculture is pivotal in the economy of our country. Major part of our country’s population depends on agriculture and one third of its revenue depends on farming. Agricultural issues have proven to be an obstacle for the development of our country. This obstacle is over come by using precision agriculture which is improvised method of conventional agriculture. Hence this project aims at making agriculture smart using automation and IoT technologies. The main objectives of this project include soil moisture, GPRS/GSM and status notification of the system. Using this system, the agriculture factors monitored and controlled correctly because of this system there will be improvement in the productivity.
Structural Similarity-Based Matrix Factorization Using Type Extension Trees for Item Cold-Start

Collaborative ﬁltering(CF) is a family approaches that are frequently used in the recommendation domain. It works by recommending items based on similar behaviors among users. There are many CF approaches one of which is matrix factorization(MF) that lets the system discover the underlying characteristics of user-item interactions and is well known for its eﬀectiveness. However in MF, as in most CF approaches the cold-start problem is present, which occurs because of the system’s inability to draw any inferences for users or items due to lack of information. This paper proposes a combination of a well documented matrix factorization method: Learning Local Collective Embeddings (LCE)[19], together with a statistical relational learning approach: Type Extension Trees (TETs)[9]. This is done in the form of a pipeline, where we ﬁrst use the TETs to learn the structural similarity between all items and using the metadata of the items, which can then be described with a item-item similarity matrix. Secondly, the resulting item-item similarity matrix is used to train the LCE model to provide better content based recommendations in item-cold start scenarios. Our approach is evaluated by testing in a cold-start scenario using three diﬀerent publicly available datasets and comparing the results against other hybrid approaches. The experiments shows a slight increase in eﬀec-tiveness compared to the original LCE in the item cold-start scenario.
Toward Computer-Mediated Emotional Monitoring and Burnout Mitigation for University STEM Students

Increasing levels of stress and burnout are pervasive among university STEM students leading to decreased well-being, lower productivity during coursework-related tasks, and negative long-term mental health outcomes. Monitoring physiological cues such as emotional expressions during computing tasks opens up numerous opportunities to support their well-being and productivity while also potentially mitigating burnout. However, researchers have raised concerns about the accuracy of vision-based emotional state recognition tools. In this workshop paper, we explore using off-the-shelf tools to model and code for the emotional states of users. Our goal is to evaluate these tools for use during just-in-time interventions designed to help students perform better in coursework while maintaining positive mental health
Persuasive Data Storytelling with a Data Video during Covid-19 Infodemic: Affective Pathway to Influence the Users' Perception about Contact Tracing Apps in less than 6 Minutes

The current pandemic showed us the importance of swiftly disseminating data-based information to the masses of people. This study explores an affect-centered narrative to convey data-driven messages regarding contact tracing apps (CTAs) using video as a medium (i.e., data video). A between-subjects online study compared the effect of three storytelling approaches on viewers' perception. A video developed by Google was selected as the baseline video (Control Condition; 2min 23s) due to its high quality and relevance to CTAs. The central messages of this baseline video were; a) how CTAs work, and b) how safe and effective CTAs are. Infographics supporting these messages were then added to the baseline video (the second condition; 3min 19s); this was a simple data video (DV), and it did not intend to induce specific emotional experiences in participants (i.e., cognition-centered video). Finally, an affect-focused DV (AFDV) was also created by emphasizing the emotion-based narrative aspect of the message (the third condition; 4min 6s). In this video, three cute human-like cartoon characters were introduced. Viewers in this condition needed to process both cognitive and affective information. Note all three videos (i.e., control video, DV, and AFDV) conveyed identical messages. Participants watched one of these three videos only once, and we explored the video effect on their perception. Our results repeatedly indicated the potential benefits of including affect in data storytelling.
Evaluation of Global Food Security Index Indicators with 2020 COVID-19 Period Data and Country Comparisons

Fluctuating global economic growth, rising inequality, political instability and forced migration have a significant impact on whether the population is well-nourished. While climate change and depletion of natural resources increase these negativities, they make it difficult to reach the United Nations' Sustainable Development Goals (UN SDGs) by 2030. According to research by the UN Food and Agriculture Organization (FAO), 35 to 122 million people will fall into poverty by 2030 and there will be less food security due to climate-related problems. The food security and nutritional status of the most vulnerable communities are expected to worsen due to the health and socio-economic impacts of the COVID-19 pandemic. In the study, the comparative situations of the countries including Turkey were planned to be analyzed by Multi-Criteria Decision Making methods with the 2020 COVID-19 period data in terms of food security, which is among the main headings of the United Nations 2030 Development Goals. The study presents an novelty to the literature by drawing attention to the increasing food security problem with the global COVID-19 pandemic, and also by using Multi-Criteria Decision Making methods and cluster analysis from data mining methods. According to the final ranking obtained by the Borda Count method in the study, Singapore ranks first, followed by Finland, Sweden, Switzerland, the United States and the Netherlands, respectively. In both the COPRAS and MAUT rankings, six of the top 10 nations are European Union members. Indonesia, India, South Africa, Thailand, Brazil, and Slovakia are at the bottom of the Borda ranking.
for residual feed intake Intestinal integrity , endotoxin transport and detoxification in pigs divergently selected

Microbes and microbial components potentially impact the performance of pigs through immune stimulation and altered metabolism. These immune modulating factors can include endotoxin from gram negative bacterial outer membrane component, commonly referred to as lipopolysaccharide (LPS). In this study, our objective was to examine the relationship between intestinal barrier integrity, endotoxin and infl ammation with feed effi ciency (FE), using pig lines divergently selected for residual feed intake (RFI) as a model. Twelve gilts (62 ± 3 kg BW) from the low RFI (LRFI, more effi cient) and 12 from the high RFI (HRFI, less effi cient) were used. Individual performance data was recorded for 5 wk. At the end of the experimental period, ADFI of LRFI pigs was less (P < 0.001), ADG not different between the 2 lines (P = 0.72) but the G:F of LRFI pigs was greater than for HRFI pigs (P = 0.019). Serum endotoxin concentration (P < 0.01) and the acute phase protein haptoglobin (P < 0.05) were greater in HRFI pigs. Transepithelial resistance of the ileum, transport of fl uorescein isothiocyanate labeled-Dextran and-LPS in ileum and colon, as well as tight junction protein mRNA expression in ileum, did not differ between the lines, indicating the 2 lines did not differ in transport characteristics at the intestinal level. Ileum infl ammatory markers, myeloperoxidase (P < 0.05) and IL-8 (P < 0.10), were found to be greater in HRFI pigs. Alkaline phosphatase (ALP) activity was signifi cantly increased in the LRFI pigs in ileum and liver tissues and negatively correlated with blood endotoxin (P < 0.05). Lysozyme activity in the liver was not different between the lines; however, the LRFI pigs had a twofold greater lysozyme activity in ileum (P < 0.05). Despite the difference in their activity, ALP or lysozyme mRNA expression was not different between the lines in either tissue. Decreased endotoxin and infl ammatory markers and the enhanced activities of antimicrobial enzymes in the LRFI line may not fully explain the difference in the FE between the lines, but they have the potential to prevent the growth potential in HRFI pigs. Further studies are needed to identify the other mechanisms that may contribute to the greater endotoxin and acute phase proteins in the HRFI pigs and the greater FE in the LRFI pigs.
Duplication Free and Minimal Keyword Search in Large Graphs

—Keyword search over a graph searches for a subgraph that contains a set of query keywords. A problem with most existing keyword search methods is that they may produce duplicate answers that contain the same set of content nodes (i.e., nodes containing a query keyword) although these nodes may be connected differently in different answers. Thus, users may be presented with many similar answers with trivial differences. In addition, some of the nodes in an answer may contain query keywords that are all covered by other nodes in the answer. Removing these nodes does not change the coverage of the answer but can make the answer more compact. The answers in which each content node contains at least one unique query keyword are called minimal answers in this paper. We deﬁne the problem of ﬁnding duplication-free and minimal answers, and propose algorithms for ﬁnding such answers efﬁciently. Extensive performance studies using two large real data sets conﬁrm the efﬁciency and effectiveness of the proposed methods.
Prediction of Aﬀective State Using Consumer Graded Hardware and Sensor Fusion

Objectives: Emotions are an important part of UX, but traditional evaluation methods makes them prone to bias. Literature shows an increase in attempts to evaluate emotions using sensors. This work attempts to use sensor fusion techniques on physiological data gathered from consumer-grade hardware to predict subjective SAM ratings. Methods: IAPS pictures were used to induce affective states, and subjective emotional responses were evaluated using SAM. SAM ratings were separated into groupings with a single division and groupings with two divides. Physiological data was collected using EEG, GSR, ECG, and facial tracking. The test had 49 participants (21 female and 28 males, aged 19-33 (mean 22.22; standard deviation 2.75). Data from each individual sensor were used to train a SVM for classifying arousal and valence. Furthermore, two decision fusion techniques were used: weighted voting and stacking. Results: Accuracies for a single divide grouping ranged from 74.5% to 84.8% and on groupings with two divides, from 57.8% to 67.1%. These results were signiﬁcantly better than naive guessing, which ranged from 58.8% to 66.1% on single divide groupings, and 49.3% to 49.8% on two divide groupings. While the weighted voting technique performed slightly worse than all the machines trained on individual sensors, the stacking technique proved to be signiﬁcantly better. Conclusion: We found that it is possible to predict subjective SAM ratings using physiological sensors. Furthermore, the accuracy can be increased by using sensor fusion, if the right fusion technique is chosen. It was found that using stacking achieved signiﬁcantly better results than voting.
Fuzzy-Afﬁne-Model Based Filtering Design for Continuous-Time Roesser-Type Two-Dimensional Nonlinear Systems

—This paper tackles the problem of ﬁltering design for continuous-time Roesser-type two-dimensional (2-D) nonlinear systems via Takagi-Sugeno (T-S) fuzzy afﬁne models. The aim is to design an admissible piecewise afﬁne (PWA) ﬁlter such that the ﬁltering error system is asymptotically stable with a prescribed disturbance attenuation level. Firstly, 2-D Roesser nonlinear systems are approximated by a kind of 2-D fuzzy afﬁne models with norm-bounded uncertainties. Then, the premise variable space of the 2-D fuzzy afﬁne systems is partitioned into two classes of subspaces, i.e., crisp regions and fuzzy regions. For each region, boundary continuity matrices and characterizing matrices are constructed by utilizing the space partition information and 2-D structure. After that, novel piecewise Lyapunov functions are constructed, based on which together with S-procedure, the asymptotic stability with H 1 performance is guaranteed for the ﬁltering error system. By Projection lemma and some elegant convexiﬁcation techniques, the PWA H 1 ﬁltering design conditions are obtained. Finally, the less conservativeness and effectiveness of the proposed approach over a common Lyapunov function based one is illustrated by simulation studies.
THE MECHANISM OF THE EMOTIONS

Heretofore I have constantly used phrases, though under protest, such as "the physiological phenomena occasioned by the emotions," or "the physiological phenomena which accompany emotion," etc. I have employed provisionally these customary expressions for the relation in question in order to be understood. Strangely enough up to the present time this relation never has been in any way accurately defined. I know of no attempt to determine its exact nature. The matter is very simple in the popular conception. Here emotions are entities, substances, forces, daemons, which seize man and produce in him bodily as well as mental manifestations: "grief seized me," "a joy came to me," "anger controlled me," "fear overwhelmed me," etc.
Focus on the Action: Learning to Highlight and Summarize Jointly for Email To-Do Items Summarization

Automatic email to-do item generation is the task of generating to-do items from a given email to help people overview emails and schedule daily work. Different from prior research on email summarization, to-do item generation focuses on generating action mentions to provide more structured summaries of email text.Prior work either requires large amount of annotation for key sentences with potential actions or fails to pay attention to nuanced actions from these unstructured emails, and thus often lead to unfaithful summaries. To fill these gaps, we propose a simple and effective learning to highlight and summarize framework (LHS) to learn to identify the most salient text and actions, and incorporate these structured representations to generate more faithful to-do items. Experiments show that our LHS model outperforms the baselines and achieves the state-of-the-art performance in terms of both quantitative evaluation and human judgement. We also discussed specific challenges that current models faced with email to-do summarization.