Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid approach for article recommendation in research social networks

With the prevalence of research social networks, determining effective methods for recommending scientific articles to online scholars has become a challenging and complex task. Current studies on article recommendation works are focused on digital libraries and reference sharing websites while studies on research social networking websites have seldom been conducted. Existing content-based approaches or collaborative filtering approaches suffer from the problem of data sparsity. The quality information of articles has been largely ignored in previous studies, thus raising the need for a unified recommendation framework. We propose a hybrid approach to combine relevance, connectivity and quality to recommend scientific articles. The effectiveness of the proposed framework and methods is verified using a user study on a real research social network website. The results demonstrate that our proposed methods outperform baseline methods.
Variance reduction properties of the reparameterization trick

The reparameterization trick is widely used in variational inference as it yields more accurate estimates of the gradient of the variational objective than alternative approaches such as the score function method. Although there is overwhelming empirical evidence in the literature showing its success, there is relatively little research exploring why the reparameterization trick is so effective. We explore this under the idealized assumptions that the variational approximation is a mean-field Gaussian density and that the log of the joint density of the model parameters and the data is a quadratic function that depends on the variational mean. From this, we show that the marginal variances of the reparameterization gradient estimator are smaller than those of the score function gradient estimator. We apply the result of our idealized analysis to real-world examples.
Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery.
SLIC Based Digital Image Enlargement

Low resolution image enhancement is a classical computer vision problem. Selecting the best method to reconstruct an image to a higher resolution with the limited data available in the low-resolution image is quite a challenge. A major drawback from the existing enlargement techniques is the introduction of color bleeding while interpolating pixels over the edges that separate distinct colors in an image. The color bleeding causes to accentuate the edges with new colors as a result of blending multiple colors over adjacent regions. This paper proposes a novel approach to mitigate the color bleeding by segmenting the homogeneous color regions of the image using Simple Linear Iterative Clustering (SLIC) and applying a higher order interpolation technique separately on the isolated segments. The interpolation at the boundaries of each of the isolated segments is handled by using a morphological operation. The approach is evaluated by comparing against several frequently used image enlargement methods such as bilinear and bicubic interpolation by means of Peak Signal-to- Noise-Ratio (PSNR) value. The results obtained exhibit that the proposed method outperforms the baseline methods by means of PSNR and also mitigates the color bleeding at the edges which improves the overall appearance.
Dynamic Modeling and Simulation of Electro-Mechanical Actuator Coupled with Nonlinear Factors

In order to meet the demands of precision modeling of Electro-Mechanical Actuator in aircraft HILS areas, kinetic equations of subsystems in EMA are described respectively, and a complete EMA model with nonlinear factors, such as clearance, stiffness and friction are established. By comparing the simulation results with the experimental ones, it shows that the kinetic equations of subsystems are reasonable and correct, and the model have greater reference value for guiding the design and optimization of EMA.
Dynamic Modeling and Simulation of Flexible Load System Operated by Reconfigurable Manipulators Under Different Constraints

This paper aims at solving the problems that the manipulator will be constrained by the flexible load, and the caused constraint force is difficult to control. Based on the deflection screw theory, the dynamic model of the manipulator system is established by using the Lagrange equation. Firstly, according to the interaction of the end effector, the flexible load and the environmental constraints of the manipulator, the equation of motion of the entire system is built. Secondly, the dynamic model of the manipulator system is set up, that the system including the flexible load and the manipulator. Finally, Under the certain known environmental constraints, the dynamic modeling of a manipulator is performed. The effectiveness of the proposed method is verified by the simulation of a flexible beam operated by a 3-DOF manipulator.
Fitness Inheritance Assisted MOEA/D-CMAES for Complex Multi-Objective Optimization Problems

Multi-objective optimization is a significant topic since many real-world problems consider different aspects. The potential conflicts among the aspects make optimization even more difficult. The MOEA/D-CMAES has shown its capability in tackling complex multi-objective optimization problems. However, MOEA/D-CMAES needs to limit the offspring population size of each subproblem to save computational cost, which causes its vulnerability to premature convergence due to the deficiency of sampling points. This study aims to address this issue with two features: fitness inheritance and information sharing. More specifically, fitness inheritance is used to reduce the computational cost at fitness evaluation and therefore enables a larger size of offspring population. In addition, information sharing facilitates communication and utilization of offspring information among different subproblems. A series of experiments are conducted on the complex multi-objective problems. The experimental results show that the proposed MOEA/D-FICMAES is effective and efficient in solving the complex multi-objective optimization problems, in comparison to two decomposition based and one fitness inheritance assisted multiobjective optimization evolutionary algorithms.
Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information

Non-coding RNA (ncRNA) plays a crucial role in numerous biological processes including gene expression and post-transcriptional gene regulation. The biological function of ncRNA is mostly realized by binding with related proteins. Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research. The major challenge at this stage is the waste of a great deal of redundant time and resource consumed on classification in traditional interaction pattern prediction methods. Fortunately, an efficient classifier named LightGBM can solve this difficulty of long time consumption. In this study, we employed LightGBM as the integrated classifier and proposed a novel computational model for predicting ncRNA and protein interactions. More specifically, the pseudo-Zernike Moments and singular value decomposition algorithm are employed to extract the discriminative features from protein and ncRNA sequences. On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively. The experimental results of 10-fold cross-validation shown that the proposed method performs much better than existing methods in predicting ncRNA-protein interaction patterns, which could be used as a useful tool in proteomics research.
CD36 is indispensable for nutrient homeostasis and endurance exercise capacity during prolonged fasting

During fasting, most tissues including skeletal muscle heavily rely on utilization of fatty acids (FA) and minimize glucose use. In contrast, skeletal muscle prefers carbohydrate use as exercise intensity increases. In mice deficient for CD36 (CD36−/− mice), FA uptake is markedly reduced with a compensatory increase in glucose uptake in skeletal muscle even during fasting. In this study, we questioned how exercise endurance is affected during prolonged fasting in CD36−/− mice where glucose utilization is constantly increased. With or without a 24‐h fast, a single bout of treadmill exercise was started at the speed of 10 m/min, and the speed was progressively increased up to 30 m/min until mice were exhausted. Running distance of wild type (WT) and CD36−/− mice was comparable in the fed state whereas that of CD36−/− mice was significantly reduced after a 24‐h fast. Glycogen levels in liver and skeletal muscle were depleted both in WT and CD36−/− mice after a 24‐h fast. In CD36−/− mice, FA uptake by skeletal muscle continued to be reduced during fasting. Glucose utilization also continued to be enhanced in the heart and oxidative skeletal muscle and glucose supply relative to its demand was diminished, resulting in accelerated hypoglycemia. Consequently, available energy substrates from serum and in muscle for exercise performance were very limited in CD36−/− mice during prolonged fasting, which could cause a remarkable reduction in exercise endurance. In conclusion, our study underscores the importance of CD36 for nutrient homeostasis to maintain exercise performance of skeletal muscle when nutrient supply is limited.
A Novel Single-Feed Dual-Element Antenna Using Phase Compensation and Magnitude Regulation to Achieve Circular Polarization

This paper investigates the design of a novel single-feed dual-element antenna that can be used to achieve circular polarization (CP) performance for mobile devices at arbitrary locations. The severe conditions needed to achieve CP remain as difficult issues for small mobile antennas; this is the case because the ground plane, rather than the antenna structure, contributes to the dominant radiation as the antenna volume shrinks. A novel concept of decomposing the antenna into two elements, which are independently coupled to two orthogonal ground modes for far-field radiation, is proposed through a theoretical analysis based on characteristic mode theory. The proposed dual-element antenna is comprised a loop-type element and monopole-type element, where one element is directly fed and the other resonates parasitically. In this way, the magnitudes and phase difference between two radiated field components can be controlled through antenna tuning without needing to manipulate the ground plane. Both simulations and measurements are conducted to verify the proposed antenna design with a widely used smartphone.
Mobile Cloud Computing: the State of Art, Application Scenarios and Challenges

With the wide use of mobile devices and the development of cloud computing and wireless network technology, mobile cloud computing has become a primary research direction in the field of mobile applications. Mobile cloud computing allows mobile devices to take advantage of the abundant cloud resources to complete computation-intensive tasks, thus improving performance and saving energy. On the basis of presenting the concept of mobile cloud computing, this paper analyzes the state of art of mobile cloud computing architecture, describes the application scenarios, and points out the main problems existing during the applying process of mobile cloud computing. Finally, the research and development direction in the future are prospected.
An Improved Control Strategy for Double Feed Induction Generator in Low Frequency Resonant Power Grid

When the grid contains sub-synchronous oscillations below 50Hz, corresponding power pulsations will occur on both the DFIG side and the grid side. The traditional PI control strategy can hardly restrain sub-synchronous oscillations. If long-term operation lasts in this condition, sub-synchronous oscillations will affect the life of the unit and the power quality of the grid, In this paper, the mechanism of sub-synchronous resonance is analyzed first, then an improved PIR control strategy is proposed to suppress SSR, and the effectiveness of the proposed strategy is validated by Matlab / Simulink simulation. The results showed that compared with PI control, PIR control has better SSR inhibitory effect.
Repurposing of Bromocriptine for Cancer Therapy

Bromocriptine is an ergot alkaloid and dopamine D2 receptor agonist used to treat Parkinson’s disease, acromegaly, hyperprolactinemia, and galactorrhea, and more recently diabetes mellitus. The drug is also active against pituitary hormone-dependent tumors (prolactinomas and growth-hormone producing adenomas). We investigated, whether bromocriptine also inhibits hormone-independent and multidrug-resistant (MDR) tumors. We found that bromocriptine was cytotoxic towards drug-sensitive CCRF-CEM, multidrug-resistant CEM/ADR5000 leukemic cells as well as wild-type or multidrug-resistant ABCB5-transfected HEK293 cell lines, but not sensitive or BCRP-transfected multidrug-resistant MDA-MB-231 breast cancer cells. Bromocriptine strongly bound to NF-κB pathway proteins as shown by molecular docking and interacted more strongly with DNA-bound NF-κB than free NF-κB, indicating that bromocriptine may inhibit NF-κB binding to DNA. Furthermore, bromocriptine decreased NF-κB activity by a SEAP-driven NF-κB reporter cell assay. The expression of MDR-conferring ABC-transporters (ABCB1, ABCB5, ABCC1, and ABCG2) and other resistance-mediating factors (EGFR, mutated TP53, and IκB) did not correlate with cellular response to bromocriptine in a panel of 60 NCI cell lines. There was no correlation between cellular response to bromocriptine and anticancer drugs usually involved in MDR (e.g., anthracyclines, Vinca alkaloids, taxanes, epipodophyllotoxins, and others). COMPARE analysis of microarray-based mRNA expression in these cell lines revealed that genes from various functional groups such as ribosomal proteins, transcription, translation, DNA repair, DNA damage, protein folding, mitochondrial respiratory chain, and chemokines correlated with cellular response to bromocriptine. Our results indicate that bromocriptine inhibited drug-resistant tumor cells with different resistance mechanisms in a hormone-independent manner. As refractory and otherwise drug-resistant tumors represent a major challenge to successful cancer chemotherapy, bromocriptine may be considered for repurposing in cancer therapy.
Investigating Multidimensional Interoceptive Awareness in a Japanese Population: Validation of the Japanese MAIA-J

The Multidimensional Assessment of Interoceptive Awareness (MAIA) is a self-report instrument to assess relevant dimensions of bodily awareness. The aim of this study was to offer a Japanese version and adaptation of the MAIA (MAIA-J), as well as to analyse its psychometric properties in a Japanese population. The English MAIA was systematically forward and backward translated by bi-lingual Japanese experts; additionally, content validity aspects regarding language were discussed by a panel of experts. The MAIA-J was administered to 390 Japanese young adults (age: 20.3 ± 2.2), 67.7% women and 32.2% men. Exploratory factor analysis (EFA) reduced the questionnaire from 32 to 25 items and from 8 to 6 factors (Noticing, Not-Distracting, Attention Regulation, Emotional Awareness, Body Listening, and Trusting). The Japanese version showed appropriate indicators of construct validity and reliability, with Cronbach's α between 0.67 and 0.87 for the 6 MAIA-J dimensions. The findings demonstrate that MAIA-J has a slightly different factor structure compared to the original English MAIA. Results are discussed with respect to cultural differences. However, the study results support acceptable reliability of the MAIA-J in the Japanese sample, warranting its use for future studies with Japanese populations.
Two-degree-of-freedom $\mathbf{H}_{\infty}$ velocity controller design for PMLSM

Permanent magnet linear synchronous motor (PMLSM) has great structural advantage under the background of rapid development of manufacturing industry which puts higher requirements to motor tracking performance. This paper presents a two-degree-of-freedom (2DOF) velocity loop controller design method for the problem that exists in PMLSM. One of the degrees of freedom of the controller is using $\mathrm{H}_{\infty}$ mixed sensitivity design method to accomplish the objectives of disturbance suppression and noise resistance, and the other degree of freedom controller could improve the tracking performance without any loss on the ability of disturbance suppression and noise resistance. This method has shown great advantages on the control problems that require different performances which always conflict to each other. The effectiveness of the algorithm has been verified on the physical PMLSM platform.
Effect of Temperature on Oviposition Behavior and Egg Load of Blow Flies (Diptera: Calliphoridae)

Abstract Making optimal oviposition decisions is especially important for female carrion colonizing insects whose larvae often depend on ephemeral resources. Optimal oviposition theory predicts that females should exhibit behaviors that will maximize the performance of their offspring. Oviposition can be influenced by a variety of factors, including temperature. The aim of this study was to investigate the effects of temperature on the oviposition behavior and egg load of two blow fly species native to southern Ontario: Phormia regina Meigen, and Lucilia sericata Meigen (Diptera: Calliphoridae). Using fetal pig carcasses as an oviposition substrate, we assessed the length of time until the first oviposition event, preferred oviposition sites, and the total number of eggs oviposited. For each of the two species, five different temperature treatments ranging from 15 to 35°C were used. Temperature influenced time to the first oviposition event, as well as the number of eggs deposited by L. sericata and P. regina. As temperature increased, oviposition occurred faster with more eggs deposited for these two species. Female P. regina, in particular, demonstrated a higher egg load with increasing temperature. Differences in optimal temperatures for reproduction, including egg load and oviposition, may be a mechanism driving seasonal temporal resource partitioning leading to species coexistence utilizing ephemeral resources.
A 0.6-V Supply Two-Step Time-to-Digital Converter Using Dynamic Threshold Technology

A two-step time-to-digital converter (TDC) that achieves linear, high-speed time quantization, high-resolution under a 0.6-V supply is proposed in this paper. In order to work in low-voltage supply, dynamic threshold technology is used. The TDC mainly consists of two vernier delay-line TDCs and an array of SR-latch-based time amplifiers to achieve high resolution. This TDC is designed in a 65-nm CMOS technology. Simulation results of this TDC show that the minimum time resolution is 2.5ps while only consuming 0.5mW. The differential nonlinearity (DNL) of this proposed TDC is 0.9LSB and the integral nonlinearity (INL) is 2.3LSB.
Authentication Protocol for Distributed Cloud Computing: An Explanation of the Security Situations for Internet-of-Things-Enabled Devices

As a popular conception, the Internet of Things (IoT) allows people to access more and more data around the world. However, a concern arises of how to store the large amount of data created through this technology. To solve the problem, cloud computing services are brought in as a critical technology. However, it is necessary to guard the private information that is collected via IoT devices and stored in distributed cloud servers. Though authentication schemes are provided, many security weaknesses exist. We present an advanced authentication scheme for cloud computing using only hash functions and exclusive or computations that are suitable for IoT devices, which tend to have limited computation and storage abilities.
Warmer Temperatures Affect the in situ Freezing Resistance of the Antarctic Vascular Plants

Although positive effects on growth and reproduction of Antarctic vascular plants have been reported under warmer temperatures, it could also increase the vulnerability of these plants to freezing. Thus, we assessed in situ whether warming decreases the freezing resistance of Colobanthus quitensis and Deschampsia antarctica, and we compared the level and mechanism of freezing resistance of these species in the field with previous reports conducted in lab conditions. We assessed the freezing resistance of C. quitensis and D. antarctica by determining their low temperature damage (LT50), ice nucleation temperature (NT) and freezing point (FP) in three sites of the King George Island. Plants were exposed during two growing seasons to a passive increase in the air temperature (+W). +W increased by 1K the mean air temperatures, but had smaller effects on freezing temperatures. Leaf temperature of both species was on average 1.7K warmer inside +W. Overall, warming decreased the freezing resistance of Antarctic species. The LT50 increased on average 2K for C. quitensis and 2.8K for D. antarctica. In contrast, NT and FP decreased on average c. 1K in leaves of warmed plants of both species. Our results showed an averaged LT50 of -15.3°C for C. quitensis, and of -22.8°C for D. antarctica, with freezing tolerance being the freezing resistance mechanism for both species. These results were partially consistent with previous reports, and likely explanations for such discrepancies were related with methodological differences among studies. Our work is the first study reporting the level and mechanisms of freezing resistance of Antarctic vascular plants measured in situ, and we demonstrated that although both plant species exhibited a great ability to cope with freezing temperatures during the growing season, their vulnerability to suffer freezing damage under a warming scenario increase although the magnitude of this response varied across sites and species. Hence, freezing damage should be considered when predicting changes in plant responses of C. quitensis and D. antarctica under future climate conditions of the Antarctic Peninsula.