Content Quality Assurance on Media Platforms with User-Generated Content

This paper develops a duopoly model of user-generated content (UGC) platforms that compete for consumers and content producers in two-sided markets with network externalities. Each platform can choose the level of investment into a content quality assurance (CQA) system and the level of advertising. Our model shows that network effects are crucial in determining the platforms' optimal strategy and the behavior (single vs. multi-homing) of their users. Specifically, we find that consumers are multi-homing and producers are single-homing when the network effects obtained by producers are weak, while the opposite is true if these network effects are strong. Moreover, our model shows that the user behavior and the network effects determine whether a platform has incentives to place ads and/or invest into CQA. In general, weak network effects induce a platform to invest into a CQA system except when consumers and producers are multi-homing. The results in our model suggests the need for platform companies to assess the magnitude of network effects on their platform to predict the behavior of their users, which in turn will determine the optimal CQA and advertising strategy

Adaptive Fault Diagnosis of Motors Using Comprehensive Learning Particle Swarm Optimizer with Fuzzy Petri Net

This study proposes and applies a comprehensive learning particle swarm optimization (CLPSO) fuzzy Petri net (FPN) algorithm, which is based on the CLPSO algorithm and FPN, to the fault diagnosis of a complex motor. First, the transition confidence is replaced by a Gaussian function to deal with the uncertainty of fault propagation. Then, according to the Petri net principle, a competition operator is introduced to improve the matrix reasoning. Finally, a CLPSO-FPN model for motor fault diagnosis is established based on the motor failure mechanism and fault characteristics. The CLPSO algorithm is used to generate the system parameters for fault diagnosis and to improve the adaptability and accuracy of fault diagnosis. This study considers the example of a three-phase asynchronous motor. The results show that the proposed algorithm can diagnose faults in this motor with satisfactory adaptability and accuracy compared with the traditional FPN algorithm. By establishing the system model, the fault propagation process of motors can be accurately and intuitively expressed, thus improving the fault treatment and equipment maintenance of motors

Electrogenic amino acid exchange via the rBAT transporter

AbstractA cDNA clone was isolated from rabbit renal cortex using DNA-mediated expression cloning, which caused alanine-dependent outward currents when expressed in Xenopus oocytes. The cDNA encodes rBAT, a Na-independent amino acid transporter previously cloned elsewhere. Exposure of cDNA-injected oocytes to neutral amino acids led to voltage-dependent outward currents, but inward currents were seen upon exposure to basic amino acids. Assuming one charge/alanine, the outward current represented 38% of the rate of uptake of radiolabelled alanine, and was significantly reduced by prolonged preincubation of oocytes in 5 mM alanine. The currents were shown to be due to countertransport of basic amino acids for external amino acids using the cut-open oocyte system. This transport represents a major mode of action of this protein, and may help in defining a physiological role for rBAT in the apical membrane of renal and intestinal cells

Prevalence and predictors of polypharmacy prescription among type 2 diabetes patients at a tertiary care department in Ningbo, China: A retrospective database study

ObjectivesTo determine the prevalence of polypharmacy prescription among type 2 diabetes (T2DM) patients at a tertiary care department in Ningbo, China, and to determine factors that independently predict this polypharmacy prescription.MethodsA retrospective cross-sectional study was conducted using an existing computerised medical records database. This database was screened from 2012 to 2017 for adult patients with T2DM and parameters like prescribed medicines and socio-demographic, behavioural and other medical information. Polypharmacy prescription was defined as the simultaneous prescription of ≥5 medicines by the clinician at the time of discharge for daily usage by the patient as part of his/her long-term treatment plan.ResultsThe study inclusion criteria were satisfied by 3370 T2DM patients. Over a 5-year period, 72.2% (n = 2432) of T2DM patients were prescribed polypharmacy. On an average, eight medicines were prescribed to them. The odds of polypharmacy prescription increased with patients’ age (18–39 years: 1; 40–59 years: OR 1.86, 95% CI 1.28–2.71; and ≥60 years: 2.42, 1.65–3.55), duration of T2DM (≤1 year: 1; >5–10 years: 1.70, 1.10–2.62; and >10 years: 2.55, 1.68–3.89), and length of hospital stay (≤5 days: 1; >5–10 days: 2.43, 1.86–3.17; and >10 days: 2.99, 2.24–3.99), and were higher in those with poor blood glucose level (2.09, 1.67–2.62) and with comorbidities like other endocrine, nutritional and metabolic diseases (2.24, 1.76–2.85), circulatory system diseases (4.35, 3.62–5.23), skin and subcutaneous tissue diseases (1.64, 1.04–2.59), and musculoskeletal system and connective tissue diseases (1.61, 1.27–2.03). The odds of polypharmacy prescription were lower in those with comorbidities like neoplasms (0.51, 0.36–0.70) and during pregnancy, childbirth and the puerperium (0.06, 0.01–0.49).ConclusionsAround three fourth of T2DM patients at the tertiary care department were prescribed polypharmacy, and the predictors were identified. The study findings could be taken into consideration in future interventional studies aimed at supporting medicines optimisation (and deprescribing) among these patients

Enhancing Localization of Mobile Robots in Distributed Sensor Environments for Reliable Proximity Service Applications

Mobile robots can effectively coordinate information among sensor nodes in a distributed physical proximity. Accurately locating the mobile robots in such a distributed scenario is an essential requirement, such that the mobile robots can be instructed to coordinate with the appropriate sensor nodes. Packet loss is one of the prevailing issues on such wireless sensor network-based mobile robot localization applications. The packet loss might result from node failure, data transmission delay, and communication channel instability, which could significantly affect the transmission quality of the wireless signals. Such issues affect the localization accuracy of the mobile robot applications to an overwhelming margin, causing localization failures. To this end, this paper proposes an improved Unscented Kalman Filter-based localization algorithm to reduce the impacts of packet loss in the localization process. Rather than ignoring the missing measurements caused by packet loss, the proposed algorithm exploits the calculated measurement errors to estimate and compensate for the missing measurements. Some simulation experiments are conducted by subjecting the proposed algorithm with various packet loss rates, to evaluate its localization accuracy. The simulations demonstrate that the average localization error of the robot is 0.39 m when the packet loss rate is less than 90%, and the average running time of each iteration is 0.295 ms. The achieved results show that the proposed algorithm exhibits significant tolerance to packet loss while locating mobile robots in real-time, to achieve reliable localization accuracy and outperforms the existing UKF algorithm

Collaborative actuation of wireless sensor and actuator networks for the agriculture industry.

This paper investigates the deployment of collaborative estimation and actuation scheme of wireless sensor and actuator networks for the agriculture industry. In our proposed scheme, sensor nodes conduct a local estimation based on the Kalman filter for enhancing the estimation stability and further transmit data to the actuator nodes under a multi-rate transmission mode for enhancing the overall energy efficiency of the wireless network. Considering the mutual effect of related clusters, a collaborative actuation scheme of actuator nodes is integrated into our proposed scheme for improving the estimation accuracy and convergence speed. With an accurate estimation of the changes in the environmental parameters, combining the fuzzy neural network with the PID control algorithm, the actuator exerts reliable control over the environmental parameters. Performance evaluations and simulation analysis conducted based on the effects of temperature demonstrate the effectiveness of our proposed scheme in controlling the greenhouse environmental changes for in the agriculture industry.N/

Identification of Drought-Tolerance Genes in the Germination Stage of Soybean

Drought stress influences the vigor of plant seeds and inhibits seed germination, making it one of the primary environmental factors adversely affecting food security. The seed germination stage is critical to ensuring the growth and productivity of soybeans in soils prone to drought conditions. We here examined the genetic diversity and drought-tolerance phenotypes of 410 accessions of a germplasm diversity panel for soybean and conducted quantitative genetics analyses to identify loci associated with drought tolerance of seed germination. We uncovered significant differences among the diverse genotypes for four growth indices and five drought-tolerance indices, which revealed abundant variation among genotypes, upon drought stress, and for genotype × treatment effects. We also used 158,327 SNP markers and performed GWAS for the drought-related traits. Our data met the conditions (PCA + K) for using a mixed linear model in TASSEL, and we thus identified 26 SNPs associated with drought tolerance indices for germination stage distributed across 10 chromosomes. Nine SNP sites, including, for example, Gm20_34956219 and Gm20_36902659, were associated with two or more phenotypic indices, and there were nine SNP markers located in or adjacent to (within 500 kb) previously reported drought tolerance QTLs. These SNPs led to our identification of 41 candidate genes related to drought tolerance in the germination stage. The results of our study contribute to a deeper understanding of the genetic mechanisms underlying drought tolerance in soybeans at the germination stage, thereby providing a molecular basis for identifying useful soybean germplasm for breeding new drought-tolerant varieties