Miracle-dz/UniFAR-FTUs huggingface dataset:
Pricing Models for Crowdsourcing Tasks Based on Geographic Information

With the popularity of smart phones and mobile Internet services, crowdsourcing has become a popular business model by which the tasks are published at some crowdsourcing platforms, while the registered members can download the tasks and earn money after finishing the tasks in a distributive manner. This kind of self-service labor crowdsourcing platforms based on mobile Internet provides enterprises with various business inspections and information collection services. During the entire process, how to determine the prices for different tasks is one of key issues of crowdsourcing business. In this paper, we propose three pricing models for crowdsourcing based on geographical information, and then take the Chinese MCM problem B of 2017 as a case study, to give an evaluation on the reasonability and the effectiveness of the proposed pricing models for crowdsourced photography.
Implicit negative link detection on online political networks via matrix tri-factorizations

ABSTRACT Political conversations have become a ubiquitous part of social media. When users interact and engage in discussions, there are usually two mediums available to them; textual conversations and platform-specific interactions such as like, share (Facebook) or retweet (Twitter). Major social media platforms do not facilitate users with negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Thus, detecting implicit negative links is an important and a challenging task. In this work, we propose an unsupervised framework utilising positive interactions, sentiment cues, and socially balanced triads for detecting implicit negative links. We also present an online variant of it for streaming data tasks. We show the effectiveness of both frameworks with experiments on two annotated datasets of politician Twitter accounts. Our experiments show that the proposed frameworks outperform other well-known and proposed baselines. To illustrate the detected implicit negative links' contribution, we compare the community detection accuracies using unsigned and signed networks. Experimental results using detected negative links show superiority on the three datasets where the camps are known a priori. We also present qualitative evaluations of polarisation patterns between communities which are only possible in the presence of negative links.
Experimental Case Design of Digital Logic Based on Through-Type Teaching

According to the connotation of through-type teaching, this paper presents the experimental teaching reform ideas of Digital Logic. An experimental case of digital system design is proposed based on the three-axis accelerometer of Xilinx Nexys4 DDR Artix-7 FPGA development board. This experimental case can be used in practical teaching of Digital Logic class and independent development after class. Through teaching guidance, the students' enthusiasm, design and creativity will be effectively stimulated, which will lay a solid foundation for students designing the CPU in the following computer composition and architecture courses.
A Triple-Band Dual-Polarized Indoor Base Station Antenna for 2G, 3G, 4G and Sub-6 GHz 5G Applications

This paper proposes a new design of a triple-band dual-polarized indoor base station antenna for mobile communication systems serving the 2G, 3G, 4G, and the new sub-6 GHz 5G applications. The design consists of two orthogonal dipole antennas to provide dual polarization. Each dipole consists of three different radiator types—elliptical dipole, bowtie dipole, and cat-ear shaped arms for different bands. The proposed antenna offers three broad bands with the fractional bandwidths of 31.3% (0.7–0.96 GHz), 55.3% (1.7–3 GHz), and 14% (3.3–3.8 GHz) which can be controlled independently. The design also offers stable radiation patterns within the desired frequency bands, high polarization purity and, a simple feeding structure with a compact size and low profile which make this new design an ideal candidate for indoor mobile base stations serving the 2G, 3G, 4G, and the new sub-6GHz 5G applications.
Claudin1 promotes the proliferation, invasion and migration of nasopharyngeal carcinoma cells by upregulating the expression and nuclear entry of β-catenin

The aim of the present study was to measure the expression of Claudin (CLDN) 1 in nasopharyngeal carcinoma (NPC) and to determine its biological function and mechanism of action. Reverse transcription-quantitative polymerase chain reaction and western blotting were performed to measure the expression of CLDN1 mRNA and protein, respectively, in the immortalized human nasopharyngeal epithelial cell line NP69 and NPC-TW01 cells. Subsequently, small interfering RNA against CLDN1 and the LV-GFP-PURO-CLDN1 lentivirus were transfected into NPC-TW01 cells. Western blotting was used to determine the effects of CLDN1 down- and upregulation on the expression of the epithelial mesenchymal transition (EMT) markers E-cadherin and vimentin. In addition, the effect of CLDN1 on the expression of β-Catenin was determined. The results demonstrated that levels of CLDN1 mRNA and protein in NPC cells were significantly higher than in NP69 cells. Furthermore, the downregulation of CLDN1 inhibited the proliferation, invasion and migration of NPC-TW01 cells. The results of western blotting demonstrated that the downregulation of CLDN1 resulted in the upregulation of E-cadherin and inhibition of vimentin in NPC-TW01 cells. By contrast, the overexpression of CLDN1 resulted in the downregulation of E-cadherin and upregulation of vimentin in NPC-TW01 cells. The downregulation of β-catenin attenuated the cancer-promoting effect of CLDN1 on NPC-TW01 cells, whereas the upregulation of β-catenin reversed the tumor-suppressing effect of CLDN1 downregulation on NPC-TW01 cells. The results of the present study therefore demonstrate that CLDN1 expression is elevated in NPC cells. As an oncogene, CLDN1 promotes the proliferation, invasion and migration of NPC cells by upregulating the expression and nuclear entry of β-catenin.
Medication adherence improvement on Rheumatoid Arthritis patients based on past medical records

Current drug design and medical treatment guidelines are defined by generic procedures on which each patient that suffers from a disease is treated equally and without any distinction based on any previous genetic/epigenetic study. Most of the time, this generic treatment approach is prone to failure, due to each individual’s distinctive genetic characteristics that block the activation pathway of a treatment drug. As a consequence, general treatment guides increases a patient’s disease remission time, reducing their quality of life and increasing the financial treatment costs for all parties involved. Until now, the effectiveness of a drug on a specific patient is evaluated based on trial and error over a sequence of periodical medical evaluations that include several clinical tests that are logged on each patient’s medical record; this information is used to select suitable drug guidelines until the most effective one is found. This process occurs for every patient that suffers a disease, specially on rheumatic diseases like Arthritis. To tackle this problem, we propose a medical information system (TiMed) that takes as input a collection of medical records and generalizes the temporal sequence effective variables that determine the most suitable drugs for Arthritis patients, using a Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) units, achieving a 97.4% accuracy over a 150 real data input dataset.
Clinical value of carcinoembryonic antigen for predicting the incidence of brain metastases and survival in small cell lung cancer patients treated with prophylactic cranial irradiation

Background Although the role of prophylactic cranial irradiation (PCI) in the treatment of small cell lung cancer (SCLC) has been confirmed, the occurrence of brain metastases (BM) in patients remains a major problem. We designed this study to evaluate the clinical value of carcinoembryonic antigen (CEA) for predicting the incidence of BM and survival in SCLC patients who received PCI. Materials and methods The records of 128 consecutive SCLC patients, who underwent PCI in our institute between 2005 and 2015, were analyzed. The collected data included clinicopathological features and the levels of CEA, neuron-specific enolase (NSE), cytokeratin 19 fragments (CYFRA21-1), and albumin. Kaplan–Meier and Cox regression analyses were used to determine the factors that affect BM and survival in SCLC patients after PCI. Results In total, 128 patients were identified, with a median (range) age of 62 (30–83) years. Thirty-two patients developed BM at some time during follow-up. The median levels of CEA, NSE, CYFRA21-1, and albumin were 7.6 ng/mL, 44 ng/mL, 4.6 ng/mL, and 42.1 g/L, respectively. In the multivariate analysis, CEA level (HR: 2.479, 95% CI: 1.101–5.581; P=0.028), advanced clinical stage (HR: 2.929, 95% CI: 1.338–6.413; P=0.007), and NSE level (HR: 3.021, 95% CI: 1.226–7.442; P=0.016) were significantly correlated with BM. CEA (HR: 1.903, 95% CI: 1.133–3.195; P=0.015) and advanced clinical stage (HR: 2.002, 95% CI: 1.227–3.267; P=0.005) were independently associated with worse overall survival in SCLC patients. Conclusion CEA is an independent predictive factor for the incidence of BM after PCI in SCLC and can be used as a predictor of BM in SCLC. In addition, a high level of CEA indicates a poor prognosis in SCLC patients after PCI. Prospective randomized clinical studies are required to confirm these findings.
Collaborative Geolocation Based on Imprecise Initial Coordinates for Internet of Things

Non-linear minimum optimization methods, like maximum likelihood estimation method, have been heavily employed in range-based localization problems in plenty of research. However, conventional optimization methods require precise initial values. When the initial coordinate is imprecise or even significantly different from feasible solution, these methods can easily get stuck in local optimum solutions. For localization applications in open outdoor areas, these initial values can be provided by Global Navigation Satellite System (GNSS) with acceptable error. On the contrary, in areas with poor satellite signal reception, no precise initial value is available. To solve this problem, we proposed a generally effective approach, degrading the sensitivity of non-linear optimization algorithms on initial value accuracy, which can be applied for collaborative localization in areas with poor or no GNSS signal coverage. Penalty strategy was involved to restrict imprecise initial values to get close to both feasible region and global optimum solution. Both simulations and field tests were carried out to verify the feasibility and efficiency of the proposed approach. The results show that the sensitivity of algorithms on initial value accuracy is reduced efficiently, and accurate location estimation can be obtained even with random values as initial coordinates.
Secrecy Performance Analysis for Fixed-Gain Energy Harvesting in an Internet of Things With Untrusted Relays

In this paper, the radio frequency energy harvesting (EH) and security issues in Internet of Things (IoT) sensor networks with multiple untrusted relays are considered. In particular, the communication protocol is divided into two phases. The first phase is used for EH, in which the IoT sensor nodes (SNs) and relays harvest energy from multiple power transfer stations. The second phase is used for information transmission in two steps: 1) the selected SN uses the harvested energy to broadcast information to the controller and the relays, and 2) the selected relay forwards information to the controller by applying the amplify-and-forward protocol to improve the quality of the communication between the SN and the controller. During information transmission, the controller is at risk of losing information because the relay may act as an eavesdropper (namely, an untrusted relay). Thus, to improve the secrecy performance of the considered system, we propose an optimal scheme, namely, best-sensor-best-untrusted-relay (BSBR) and compare this scheme with random-sensor-random-untrusted-relay and a threshold-based scheme. The closed-form expressions for the secrecy outage probability (SOP) and secrecy throughput (ST) are obtained and verified through Monte Carlo simulations to confirm the superior performance of our approach. EH time optimization and the target secrecy rate optimization algorithms are also proposed. In addition, the impacts of the EH time, the EH efficiency coefficient, the numbers of SNs and untrusted relays, and the target secrecy rate on the SOP and the ST are investigated. The results indicate that the BSBR generally outperforms the two baseline schemes in terms of the SOP and ST.
To reappraise or not to reappraise? Emotion regulation choice and cognitive energetics.

Research shows that cognitive reappraisal is an effective emotion regulation (ER) strategy that often has clear benefits. Yet, surprisingly, recent findings demonstrate that people use cognitive reappraisal less frequently than might be expected (Suri, Whittaker, & Gross, 2015). We employ cognitive energetics theory (CET) to explain this puzzling behavior. CET posits that the likelihood of launching any cognitive process is a function of two opposing forces: the driving force (i.e., the motivation to launch the process) and the restraining force (i.e., task difficulty). We thus hypothesized that people choose to use cognitive reappraisal relatively rarely because of the difficulty of implementing it. We also postulated that the decision to reappraise (or not) does not simply depend on stimuli emotional intensity because the latter is associated with both the driving and the restraining forces. In support of our hypotheses, we found that when the images' emotional intensity posed difficulty for reappraisal (i.e., highly intense images), reducing this difficulty by asking participants to merely predict others' (Study 1) or their own choices (Study 2) increased reappraisal choice. Finally, in Study 3, we show that a relatively easy to implement reappraisal strategy was chosen more often than the more difficult one for high (but not low) intensity images. These findings illustrate the relevance of a CET-based motivational analysis to emotion regulation choice. (PsycINFO Database Record
Producing 3D Biomimetic Nanomaterials for Musculoskeletal System Regeneration

The human musculoskeletal system is comprised mainly of connective tissues such as cartilage, tendon, ligaments, skeletal muscle, and skeletal bone. These tissues support the structure of the body, hold and protect the organs, and are responsible of movement. Since it is subjected to continuous strain, the musculoskeletal system is prone to injury by excessive loading forces or aging, whereas currently available treatments are usually invasive and not always effective. Most of the musculoskeletal injuries require surgical intervention facing a limited post-surgery tissue regeneration, especially for widespread lesions. Therefore, many tissue engineering approaches have been developed tackling musculoskeletal tissue regeneration. Materials are designed to meet the chemical and mechanical requirements of the native tissue three-dimensional (3D) environment, thus facilitating implant integration while providing a good reabsorption rate. With biological systems operating at the nanoscale, nanoengineered materials have been developed to support and promote regeneration at the interprotein communication level. Such materials call for a great precision and architectural control in the production process fostering the development of new fabrication techniques. In this mini review, we would like to summarize the most recent advances in 3D nanoengineered biomaterials for musculoskeletal tissue regeneration, with especial emphasis on the different techniques used to produce them.
Calcium-sensitive pyruvate dehydrogenase phosphatase is required for energy metabolism, growth, differentiation, and infectivity of Trypanosoma cruzi

In vertebrate cells, mitochondrial Ca2+ uptake by the mitochondrial calcium uniporter (MCU) leads to Ca2+-mediated stimulation of an intramitochondrial pyruvate dehydrogenase phosphatase (PDP). This enzyme dephosphorylates serine residues in the E1α subunit of pyruvate dehydrogenase (PDH), thereby activating PDH and resulting in increased ATP production. Although a phosphorylation/dephosphorylation cycle for the E1α subunit of PDH from nonvertebrate organisms has been described, the Ca2+-mediated PDP activation has not been studied. In this work, we investigated the Ca2+ sensitivity of two recombinant PDPs from the protozoan human parasites Trypanosoma cruzi (TcPDP) and T. brucei (TbPDP) and generated a TcPDP-KO cell line to establish TcPDP's role in cell bioenergetics and survival. Moreover, the mitochondrial localization of the TcPDP was studied by CRISPR/Cas9-mediated endogenous tagging. Our results indicate that TcPDP and TbPDP both are Ca2+-sensitive phosphatases. Of note, TcPDP-KO epimastigotes exhibited increased levels of phosphorylated TcPDH, slower growth and lower oxygen consumption rates than control cells, an increased AMP/ATP ratio and autophagy under starvation conditions, and reduced differentiation into infective metacyclic forms. Furthermore, TcPDP-KO trypomastigotes were impaired in infecting cultured host cells. We conclude that TcPDP is a Ca2+-stimulated mitochondrial phosphatase that dephosphorylates TcPDH and is required for normal growth, differentiation, infectivity, and energy metabolism in T. cruzi. Our results support the view that one of the main roles of the MCU is linked to the regulation of intramitochondrial dehydrogenases.
Soft-Switched Nonisolated High Step-Up Three-Port DC–DC Converter for Hybrid Energy Systems

In this paper, a nonisolated high step-up three-port converter is proposed which provides two separate power flow paths from each input source to the output load. In order to reduce the number of converter components, some components play multiple roles. Accordingly, the energy storage device is charged with the same components which are used in transferring power to the load. In this converter, the coupled inductors technique is used to increase the voltage gain, and to mitigate the leakage inductance effect and to provide soft-switching condition, two active clamp circuits are employed. Since the voltages across the switches are clamped, switches with low voltage stress and consequently low conduction loss can be used. Various converter operating modes are discussed and design considerations are presented. A converter prototype to supply a 150 W–400 V load is implemented and the theoretical analysis is validated by the experimental results.
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance

The serum half-life and clearance of therapeutic monoclonal antibodies (mAbs) are critical factors that impact their efficacy and optimal dosing regimen. The pH-dependent binding of an mAb to the neonatal Fc receptor (FcRn) has long been recognized as an important determinant of its pharmacokinetics. However, FcRn affinity alone is not a reliable predictor of mAb half-life, suggesting that other biologic or biophysical mechanisms must be accounted for. mAb thermal stability, which reflects its unfolding and aggregation propensities, may also relate to its pharmacokinetic properties. However, no rigorous statistical regression methods have been used to identify combinations of physical parameters that best predict biologic properties. In this work, a panel of eight mAbs with published human pharmacokinetic data were selected for biophysical analyses of FcRn binding and thermal stability. Biolayer interferometry was used to characterize FcRn/mAb binding at acidic and neutral pH, while differential scanning calorimetry was used to determine thermodynamic unfolding parameters. Individual binding or stability parameters were generally weakly correlated with half-life and clearance values. Least absolute shrinkage and selection operator regression was used to identify the combination of two parameters with the best correlation to half-life and clearance as being the FcRn binding response at pH 7.0 and the change in heat capacity. Leave-one-out subsampling yielded a root mean square difference between observed and predicted half-life of just 2.7 days (16%). Thus, the incorporation of multiple biophysical parameters into a cohesive model may facilitate early-stage prediction of in vivo half-life and clearance based on simple in vitro experiments.
Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection

An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data. This can become a burden for condition monitoring and fault detection systems. This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection. This paper first proposes a variable selection algorithm based on principal component analysis with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset. With the selected variables, this paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity. The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, Supervisory control and data acquisition data from an operational wind farm, and experimental data from a wind turbine test rig. Results show that the proposed methods can select a reduced set of variables with minimal information lost whilst detecting faults efficiently and effectively.
A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform

Denoising is a key step in diagnosis and evaluation of partial discharge (PD) signals in power transformers. In this paper, a new PD signal denoising algorithm is presented, which is based on the combination of dual-tree complex wavelet transform (DTCWT) and adaptive singular value decomposition (ASVD). This new algorithm, which is introduced as adaptive DTCWT (ADTCWT), was evaluated through simulations and experimental tests. ADTCWT was employed in denoising from PD signals based on the selection of best singular values in each DTCWT level decomposition, corresponding to PD signal and noise. The superior performance of the ADTCWT algorithm was demonstrated using various indices in comparison with those of DTCWT and ASVD methods in noise reduction, besides preserving time of arrival of PD signals and PD localization.
Microblog Label Recommendation Based on Matrix Decomposition Model

Recent years, with the development and popularization of mobile devices and network technologies, Microblog has become a socialized media, users not only participate in self-expression, interpersonal relationships, but also access to a variety of news and hot events. Label is to help the system understand the user, thereby pushing content and friends to the user may be interested in. This paper proposes to get information such as mutual attention, one-way attention, existing labels and so on from user data, use the matrix decomposition model to recommend the label to the user after analysis.