Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Vibration of a Functionally Graded Nanobeam Based on the Nonlocal Strain Gradient Theory considering Thickness Effect

In this work, a nonlocal strain gradient beam model considering the thickness effect is developed to study the nonlinear vibration response of a functionally graded nanobeam. The governing equation of the functionally graded nanobeam is derived by using the Euler–Bernoulli beam theory with von Karman’s nonlinear strain-gradient relationship and the Hamilton principle. The expression of the nonlinear frequency for the functionally graded nanobeam with pinned-pinned boundary conditions is obtained with the help of Galerkin technique and the Hamiltonian approach. The obtained results show that the effect of thickness is very important for the size-dependent vibration response of the functionally graded nanobeam; the nonlinear vibration response of the nanobeam depends not only on the material length scale parameter and nonlocal parameter but also on the slenderness ratio. Effects of the slenderness ratio and the power-law index on the vibration response of the functionally graded nanobeam are also investigated and discussed. The numerical results show that the nonlocal parameter reduces the nonlinear frequency of the functionally graded nanobeam, while the material length scale parameter increases the nonlinear frequency of the functionally graded nanobeam. The slenderness ratio leads to an increase in the nonlinear frequency of the functionally graded nanobeam, while the power-law index leads to a decrease in the nonlinear frequency of the functionally graded nanobeam.
Paravertebral block with modified catheter under surgeon’s direct vision after video-assisted thoracoscopic lobectomy

Background Paravertebral block (PVB) conducted by epidural catheter is a prevalent pain management for patients undergoing video-assisted thoracoscopic surgery (VATS) lobectomy. The aim of this study was to assess the efficacy and safety of paravertebral block with a modified PVB (MPVB) catheter under surgeon’s direct vision after video-assisted thoracoscopic lobectomy. Methods Three hundred fifty-six patients undergoing VATS lobectomy were retrospectively reviewed and divided into two groups consecutively according to the catheter applied in PVB procedure (PVB group and MPVB group). In the MPVB group, a modified catheter with a flexible forepart and more apertures distributing along the forepart than the conventional epidural catheter was introduced. An infusion pump containing of 150 mL mixture was connected to the catheter to provide sustained regional analgesia. Intramuscular dezocine 10 mg was administered as a rescue medication when necessary. Postoperative pain management effect was assessed by visual analog scale (VAS) at rest and on coughing. Spirometry values and blood gas analysis were monitored and recorded for the first 3 postoperative days (PODs). Analgesia-related adverse events, characteristics of PVB procedure and postoperative major complication were also compared between the two groups. Results There were 172 patients who received PVB with conventional epidural catheter in the PVB group, and 184 patients were performed PVB with modified paravertebral catheter in the MPVB group. Significantly lower pain score at rest was found in MPVB group at 24 h postoperatively (P=0.006). The pain score on coughing in MPVB group was significantly lower than that in PVB group at 12 and 24 h postoperatively (P=0.037 and P<0.001, respectively). Patients needing for rescue medication was significantly lower in the MPVB group (P=0.028). The incidence of pleural perforation was lower in the MPVB group (P=0.020). Postoperative spirometry values revealed comparable pulmonary function between the two groups, and arterial blood gas analysis showed a normal range of pH and PaCO2 in both groups. There was no significant difference of analgesia-related adverse events as well as major complications between the two groups. Conclusions PVB with modified catheter under surgeon’s direct vision was effective and safe after video-assisted thoracoscopic lobectomy.
Uncertainty prediction method for traffic flow based on K-nearest neighbor algorithm

In order to overcome the problem of low fitting between traffic uncertainty prediction results and actual values in existing research methods, a traffic flow uncertainty prediction method based on K-nearest neighbor algorithm is proposed. The original database, classification center database, k-nearest neighbor database and intermediate search database are used to construct the database needed in the prediction process. Based on the database, multivariate linear regression is used to assign weights to state variables, and k-nearest neighbor algorithm and Kalman filter are used to update the weights to adapt to the uncertainties of traffic flow until the predicted values are obtained, and the uncertainties of traffic flow are predicted. The experimental results show that the maximum average absolute error and average relative error of the proposed method are 0.018 and 0.02, respectively. Compared with the traditional method, the proposed method has higher overall prediction accuracy, higher fitting degree, and is feasible.
Institutional Knowledge at Singapore Management University Institutional Knowledge at Singapore Management University How smart is institutional trading? How smart is institutional trading?

We estimate daily aggregate order ﬂow of individual stocks from institutional investors as well as for hedge funds and other institutions separately. We ﬁnd that the estimated institutional order imbalance positively predicts stock return on the next day and outperforms other institutional order ﬂow estimates. The institutional order ﬂow from hedge funds generates greater and more persistent price impact than the order ﬂow from other institutions. We also ﬁnd that hedge funds trade on well-known anomalies while the other institutions do not. Our ﬁndings suggest that the superior trading skills of institutional investors can be largely attributed to hedge funds. Jeong-Bon Manconi, Massa, Kazuhiko Ohashi, Gilbert Park, Elvira Avanidhar Sulaeman, Yuehua Trzcinka, Dimitri Vayanos, Baolian Jialin Xiaoyan Hong Bart and Singapore Management University, the Fifth Asian Bureau Finance and Economic Research Conference, 2017 SMU Research Camp, 2017 Asian Finance Association Annual Conference, 2017 European Finance Association Annual Conference, 2017 Auckland Finance Meet-ing, 2018
Construction of Delay Gene Regulatory Network Based on Complex Network

By using the analysis idea and methods of complex network,we adopted a normalized Laplace matrix and a K-mean clustering method to detect multiple communities in a gene regulatory network(GRN). At the same time,gene-gene interactions both inside a community and among communities were also described.In addition,in order to reflect the actual gene-gene interaction process and to improve the modeling precision of GRN,a time-series spectrum analysis method was used to estimate delay of gene expression before the process of community detecting.Experimental results of the cell cycle-regulated genes of yeast show that the proposed construction method of GRN not only can reflect the actual gene-gene interaction process much exactly but also can provide details about the gene regulator model.
Drug Elimination in Two-Compartment Pharmacokinetic Models with Nonstandard Finite Difference Approach

We use the nonstandard finite difference (NSFD) method to solve the two-compartments pharmacokinetic models of drug elimination. Pharmacokinetic models are commonly used to predict drug concentrations in the body. These models are modeled by nonlinear ordinary differential equations. We apply the NSFD rules, based on Mickens’ idea of transferring nonlinear models into discrete schemes. The method used was compared with other established methods to verify its efficiency and accuracy. Two-compartment pharmacokinetic models are considered for different routes of administration: I.V. bolus injection and I.V. bolus infusion.
Multi-step LSTM Prediction Model for Visibility Prediction

In this paper, we present a deep learning framework with attention mechanism for visibility prediction. We firstly formulate visibility prediction as a temporal prediction problem. An encoder-decoder architecture based network is proposed to generate a multi-step prediction. To adaptively focus on different parts of the input and output sequence, we incorporate input attention and temporal attention into the network. Experiments verify the feasibility of the proposed model. We produce state-ofthe-art prediction accuracy (68.9%) on the runway visual range prediction in our customized data set collected at observation stations of the airport.
Summary-Based Symbolic Evaluation for Smart Contracts

This paper presents Solar, a system for automatic synthesis of adversarial contracts that exploit vulnerabilities in a victim smart contract. To make the synthesis tractable, we introduce a query language as well as summary-based symbolic evaluation, which significantly reduces the number of instructions that our synthesizer needs to evaluate symbolically, without compromising the precision of the vulnerability query. We encoded common vulnerabilities of smart contracts and evaluated Solar on the entire data set from Etherscan. Our experiments demonstrate the benefits of summary-based symbolic evaluation and show that Solar outperforms state-of-the-art smart contracts analyzers, TEETHER, Mythril, and Contract Fuzzer, in terms of running time and precision.
An Empirical Study of Human Behavioral Agents in Bandits, Contextual Bandits and Reinforcement Learning.

Artificial behavioral agents are often evaluated based on their consistent behaviors and performance to take sequential actions in an environment to maximize some notion of cumulative reward. However, human decision making in real life usually involves different strategies and behavioral trajectories that lead to the same empirical outcome. Motivated by clinical literature of a wide range of neurological and psychiatric disorders, we propose here a more general and flexible parametric framework for sequential decision making that involves a two-stream reward processing mechanism. We demonstrated that this framework is flexible and unified enough to incorporate a family of problems spanning multi-armed bandits (MAB), contextual bandits (CB) and reinforcement learning (RL), which decompose the sequential decision making process in different levels. Inspired by the known reward processing abnormalities of many mental disorders, our clinically-inspired agents demonstrated interesting behavioral trajectories and comparable performance on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the PacMan game across different reward stationarities in a lifelong learning setting.
Financial and economic analyses of the impact of cattle mastitis on the profitability of Egyptian dairy farms

Aim: This study aimed to evaluate and quantify the different factors affecting the costs of mastitis in cattle, to quantify the annual and monthly financial losses attributed to mastitis, and to estimate production losses using average linear scores found on The Dairy Herd Improvement Association somatic cell count (SCC) sheets and bulk tank SCC per lactation number. Materials and Methods: All data (bovine mastitis-associated costs and expenditures in Egyptian dairy herds) were analyzed using SPSS/PCT, 2001. A partial budget technique using spreadsheet software and the general linear model procedure was used to analyze the productive and financial measures. Results: Mastitis was present in 57.1% of cows (240/420), while clinical mastitis (CM) was present in 19% of them (80/420). The geometric mean of SCC/ml in bulk tank milk samples of 150 cattle dairy farms was 556.3×103. The annual subclinical mastitis (SCM)-related economic loss was 21,933,258.6 LE, and the two most important cost components were the subsequent decrease in milk production and quality premium losses (93% and 7% of SCM costs, respectively). The quality premium loss was 1,369,602.1 LE. On the other hand, the annual economic loss due to decreased milk production as a result of SCM was 20,563,656.5 LE. The total cost of 80 CM cases, including the failure and preventive costs, was 1,196,871.4 LE, including 1,169,150.4 LE failure costs (106,336.0 LE in direct costs and 1,062,814.4 LE in indirect costs) and 27,721.0 LE preventive costs. The average cost per CM case was 28,760.9 LE, including veterinary time and consultation fees of 250.0 LE (1%), labor 562.5 LE (2%), premature culling 736,000.0 LE (77%), decreased milk production 4085.18 LE (13.7%), discarded milk 185.3 LE (1%), and drugs and treatments 328.9 LE (1%). The total costs of CM (expenditures) extra control and preventive measures, including the diagnosis of CM for 80 cows annually in 20 Egyptian dairy farms were 27,721.0 LE, representing 346.5 LE or 1% of the total cost of CM cases. The cost of monitoring and diagnostic measures was 8635.2 LE, representing 107.9 LE or 1% of the total cost of a case of CM. Conclusion: The method used for cost estimation, in this study, is highly adaptable to individual cattle farms and had a major role in assessing specific control and management measures. The concepts described in this paper help to improve our understanding of the full economic impact of clinical and subclinical mastitis in cattle in Egypt. Assessing the economic losses from mastitis to determine the economic costs and losses occurring in Egyptian dairy farms is critical for encouraging farmers to acknowledge the scale of the problem and implement effective management practices aimed at improving mastitis control and reducing the associated costs.
How can citation impact in bibliometrics be normalized? A new approach combining citing-side normalization and citation percentiles

Since the 1980s, many different methods have been proposed to field-normalize citations. In this study, an approach is introduced that combines two previously introduced methods: citing-side normalization and citation percentiles. The advantage of combining two methods is that their advantages can be integrated in one solution. Based on citing-side normalization, each citation is field weighted and, therefore, contextualized in its field. The most important advantage of citing-side normalization is that it is not necessary to work with a specific field categorization scheme for the normalization procedure. The disadvantages of citing-side normalization—the calculation is complex and the numbers are elusive—can be compensated for by calculating percentiles based on weighted citations that result from citing-side normalization. On the one hand, percentiles are easy to understand: They are the percentage of papers published in the same year with a lower citation impact. On the other hand, weighted citation distributions are skewed distributions with outliers. Percentiles are well suited to assigning the position of a focal paper in such distributions of comparable papers. The new approach of calculating percentiles based on weighted citations is demonstrated in this study on the basis of a citation impact comparison between several countries.
Modulating gut microbes

Fecal microbiota transplant and modulation of microbial species show therapeutic promise There are hundreds of trillions of microbes within the human body, which have a profound impact on modulating host function. Many of these microbes reside in the gastrointestinal tract and have been shown to influence normal physiology across all body systems (1). Disruptions in the delicate balance of microbes within the gut and other niches are associated with numerous disease states—including neurologic disorders, cardiovascular disease, gastrointestinal disorders, and even cancer (2). Accordingly, there is intense interest in targeting these microbes to promote overall health and to abrogate disease, with considerable advances made recently. Strategies to modulate gut microbes include fecal microbiota transplant (FMT), which involves the transfer of fecal material from one individual to another for a desired physiologic effect. This approach, among other gut microbiota modulation strategies, has shown promise in treating several disease conditions, although opportunities exist to iterate and build on these approaches.
Highly Contiguous Nanopore Genome Assembly of Chlamydomonas reinhardtii CC-1690

The current Chlamydomonas reinhardtii reference genome remains fragmented due to gaps stemming from large repetitive regions. To overcome the vast majority of these gaps, publicly available Oxford Nanopore Technology data were used to create a new reference-quality de novo genome assembly containing only 21 contigs, 30/34 telomeric ends, and a genome size of 111 Mb. ABSTRACT The current Chlamydomonas reinhardtii reference genome remains fragmented due to gaps stemming from large repetitive regions. To overcome the vast majority of these gaps, publicly available Oxford Nanopore Technology data were used to create a new reference-quality de novo genome assembly containing only 21 contigs, 30/34 telomeric ends, and a genome size of 111 Mb.
CarOSense

Ultra-Wideband (UWB) is a popular technology to provide high accuracy localization, asset tracking and access control applications. Due to the accurate ranging feature and robustness to relay attacks, car manufacturers are upgrading the keyless entry infrastructure to UWB. As car occupancy monitoring is an essential step to support regulatory requirements and provide customized user experience, we build CarOSense to explore the possibility of reusing UWB keyless infrastructure as an orthogonal sensing modality to detect per-seat car occupancy. CarOSense uses a novel deep learning model, MaskMIMO, to learn spatial/time features by 2D convolutions and per-seat attentions by a multi-task mask. We collect UWB data from 10 car locations with up to 16 occupancy states in each location. We implement CarOSense as a cross-platform demo and evaluate it in 15 different scenarios, including leave-one-out test of unknown car locations and stress test of unseen scenarios. Results show that the average accuracy is 94.6% for leave-one-out test and 87.0% for stress test. CarOSense is robust in a large set of untrained scenarios with the model trained on a small set of training data. We also benchmark the computation cost and demonstrate that CarOSense is lightweight and can run smoothly in real-time on embedded devices.
Partial Sequence Cloning of LHR Gene in Cynoglossus semilaevis and Its Tissue Expression Analysis

Partial cDNAs encoding LHR(luteinizing hormonene receptor) gene were cloned from Cynoglossus semilaevis by nested reverse transcrip-polymerase chain reaction(nested RT-PCR). Alignment analysis showed that the sequence was 97%,94%,85%,83%,82% and 80% similar to those of Hippoglossus hippoglossus,Paralichthys olivaceus,Acanthopagrus schlegelii,Oreochromis niloticus,Oryzias latipes and Takifugu rubripes,respectively.The length of the fragment is 614 bp,and encodes a protein of about 204 amino acids,which contains the conserved seven transmembrane helix domains(TM helix),and it belongs to the glycoprotein hormone receptor(GHR)family.Tissue expression analysis showed that LHR mRNA expressed in all eleven tissues including ovary,spermary,liver,stomach,intestines,gill,heart,spleen,kidney,head kidney,and brain,from which the spleen and kidney presented the strongest amplification signals.
Demonstration of Real-Time Quasi-Physical Atmosphere Density Estimation Approach for Space Traﬃc Management

In this paper, we demonstrate a new data-driven framework for real-time neutral density estimation via model-data fusion in quasi-physical ionosphere-thermosphere models. The framework has two main components: (i) the development of a quasi-physical dynamic reduced order model (ROM) that uses a linear approximation of the underlying dynamics and eﬀect of the drivers, and (ii) dynamic calibration of the ROM through estimation of the ROM coeﬃ-cients that represent the model parameters. We have previously demonstrated the development of a quasi-physical ROM using simulation output from a physical model and assimilation of non-operational density estimates derived from accelerometer measurements along a single orbit. In this paper, we demonstrate the potential of the framework for use with operational measurements. We use simulated GPS-derived orbit ephemerides with 5 minute resolution as measurements. The framework is a ﬁrst of its kind, simple yet robust and accurate method with high potential for providing real-time operational updates to the state of the upper atmosphere using quasi-physical models with inherent forecasting/predictive capabilities.
Identification of Pseudoknot in the Intergenic Region between VP30 and VP24 Gene of Ebola Virus and Designing Aptamer against the Pseudoknot

Ebola has been identified to be one of the most ominous pathogens capable of causing pandemic fatal disease in humans. Efforts are on to control the pathogen using various different therapeutic techniques and the vaccination effort is in its early second phase according to WHO sources. However, whether the candidate vaccine can confer “herd immunity” to a particular population remains a doubt. Thus alternative therapeutic approaches should be continuously explored, so as to combat the spread of the disease. In this work we attempt to identify one of the most predominant and essential RNA structural motifs pseudoknot in the entire Ebola genome. The results indicate that a conserved pseudoknot motif is present in between the intergenic regions in between VP 24 and VP 30. Accepted: 12 April 2016 Available Online: 06 May 2016
SUME

Recent years have witnessed a rapid proliferation of personalized mobile applications, which poses a pressing need for accurate user demographics inference. Facilitated by the prevalent smart devices, the ubiquitously collected mobility trace presents a promising opportunity to infer user demographics at large-scale. In this paper, we propose a novel Semantic-enhanced Urban Mobility Embedding (SUME) model, which learns dense representation vectors for user demographic inference by jointly modelling the physical mobility patterns and the semantic of urban mobility. Specifically, SUME models urban mobility as a heterogeneous network of users and locations, with various types of edges denoting the physical visitation and semantic similarities. Moreover, SUME optimizes the node representation vectors with two alternating objective functions that preserve the feature in physical and semantic domains, respectively. As a result, it is able to capture the effective signals in the heterogeneous urban mobility network. Empirical experiments on two real-world mobility traces show the proposed model significantly out-performs all state-of-the-art baselines with an accuracy margin of 8.6%~14.3% for occupation, gender, age, education and income inference. In addition, further experiments show SUME is able to reveal meaningful correlations between user demographics and the mobility patterns in spatial, temporal and urban structure domain.