Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of a Text Messaging–Based Intervention Targeting Alcohol Consumption Among University Students: Randomized Controlled Trial

Background Excessive drinking among university students is a global challenge, leading to significant health risks. However, heavy drinking among students is widely accepted and socially normalized. Mobile phone interventions have attempted to reach students who engage in excessive drinking. A growing number of studies suggest that text message–based interventions could potentially reach many students and, if effective, such an intervention might help reduce heavy drinking in the student community. Objective The objective of this study was to test the effectiveness of a behavior change theory–based 6-week text message intervention among university students. Methods This study was a two-arm, randomized controlled trial with an intervention group receiving a 6-week text message intervention and a control group that was referred to treatment as usual at the local student health care center. Outcome measures were collected at baseline and at 3 months after the initial invitation to participate in the intervention. The primary outcome was total weekly alcohol consumption. Secondary outcomes were frequency of heavy episodic drinking, highest estimated blood alcohol concentration, and number of negative consequences attributable to excessive drinking. Results A total of 896 students were randomized to either the intervention or control group. The primary outcome analysis included 92.0% of the participants in the intervention group and 90.1% of the control group. At follow-up, total weekly alcohol consumption decreased in both groups, but no significant between-group difference was seen. Data on the secondary outcomes included 49.1% of the participants in the intervention group and 41.3% of the control group. No significant between-group difference was seen for any of the secondary outcomes. Conclusions The present study was under-powered, which could partly explain the lack of significance. However, the intervention, although theory-based, needs to be re-assessed and refined to better support the target group. Apart from establishing which content forms an effective intervention, the optimal length of an alcohol intervention targeting students also needs to be addressed in future studies. Trial Registration International Standard Randomised Controlled Trial Number ISRCTN95054707; http://www.isrctn. com/ISRCTN95054707 (Archived by WebCite at http://www.webcitation.org/70Ax4vXhd)
Pathological cyclic strain promotes proliferation of vascular smooth muscle cells via the ACTH/ERK/STAT3 pathway

Abnormal proliferation of vascular smooth muscle cells (VSMCs) is closely related to hyperplasia in hypertension. Our previous study suggested that adrenocorticotropic hormone (ACTH) is mechano‐responsive and may regulate VSMC proliferation. However, the molecular mechanism of VSMC abnormal proliferation induced by conditions of high cyclic strain, especially the role of ACTH in this process, is unclear. Our results revealed that ACTH and its specific receptor melanocortin receptor type 2 (MC2R) were highly expressed in hypertensive rat models. Furthermore, it was demonstrated that the expression of ACTH and MC2R was up‐regulated when exposed to high cyclic strain in vitro, accompanied by abnormal proliferation of VSMCs. Next, it was proved that ACTH‐dependent cell proliferation was related to the phosphorylation of extracellular regulated protein kinases (ERK) and signal transducer and activator of transcription 3 (STAT3). The study also found that ACTH could promote dimerization and glycosylation of melanocortin 2 receptor accessory protein (MRAP), which had a significant effect on MC2R membrane localization and signal activation. When VSMCs were treated with PD98059, a mitogen‐activated protein kinase (MAP kinase) cascade antagonist, it was determined that phosphorylation of STAT3 at Ser727 was dependent on ERK phosphorylation. In summary, these data demonstrated that the abnormal proliferation of VSMCs induced by conditions of high cyclic strain is in part attributed to ACTH and its receptor MC2R. Identifying the mechanism of ACTH‐dependent proliferation of VSMCs may help to provide new therapeutic targets for hypertension.
Balance Control of the Pendubot via the Polynomial Matrix Approach

We present a procedure for the computation of a stabilizing compensator for a double inverted pendulum known as the Pendubot. The procedure relies on a computational algorithm based on various results of “the polynomial matrix approach” and in particular results for the solution of polynomial matrix Diophantine equations required for the computation and parametrization of proper “denominator assigning” and internally stabilizing compensators for linear time invariant multivariable (LTI) systems. The underlying theory regarding the solution of polynomial matrix Diophantine equations leading to proper denominator (pole) assigning stabilizing compensators is reviewed and the computational algorithm emerging from this theory is presented and applied as a case study for the computation to a stabilizing controller for the Pendubot.
Named entity recognition in the domain of geographical subject

Aiming at Named Entity Recognition (NER) in the domain of geographical subject, two different methods are proposed to recognize two categories of entity: geographical core terms and geographical location. In this paper, conditional random field (CRF) model combining commonly used features and geographical domain features is employed. We additionally perform an extensive number of experiments to verify the effectiveness of geographical domain features and achieve 78.51% and 83.10% in F1 for two categories of entity respectively. We also propose multi-channel convolutional neural network (MCCNN) which utilizes word embedding features and word base features. A simple MCCNN with little hyperparameter tuning and one layer of convolution achieves excellent results on NER task and achieves 83.15% and 89.92% in F1 for two categories of entity respectively.
Voltage control of distribution network with photovoltaic power generation systems

A voltage control system composed of grid-connected photovoltaic (PV) inverters and on-load tap changing transformer (OLTC) is proposed to solve the voltage limit problem arisen from high-penetration distributed PV power generation system integrated into the distribution network. PV inverters, as a part of grid voltage control system, are able to control the reactive power based on the voltage amplitude. In the case when PV inverters failed to normalize the grid voltage, OLTC tap controller is used as an alternative. In this case, the tap of OLTC tap controller will change accordingly based on the maximum and minimum voltage estimated from phasor measurement unit (PMU) placed at key nodes in the grid. The validity of the voltage control system proposed in this paper is verified by detailed simulation and solid results.
Fast and accurate yield rate prediction of PCB embedded common-mode filter with artificial neural network

A fast and accurate yield rate prediction method of PCB embedded Common-Mode Filter (CMF) is proposed and implemented by applying artificial neural network (ANN), one of machine learning techniques. The proposed CMF is a mushroom-like structure which has four metal layers. The frequency responses of full-wave simulation and the outputs of trained ANN have good consistency. Furthermore, a substantial number of data sets with specific deviations can be applied with trained ANN to predict yield rate and find out sensitive parameters quickly.
Application of BP neural network optimization algorithm based on genetic algorithm in credit risk early-warning of commercial bank

In order to study the early warning system of commercial bank's credit risk, a new early-warning model is established by optimizing the weights and thresholds of BP neural network using genetic algorithm based on nonlinear combinatorial forecasting principle. Finally, the training set and test set errors of BP neural network under stochastic weights and thresholds and optimal weights and thresholds have been compared. The BP neural network optimization algorithm based on genetic algorithm can predict the credit risk of commercial banks more effectively.
The Role of the Internet of Things in the Improvement and Expansion of Business

The Internet of Things (IOT) has been examined in several perspectives based on the goals and visions of the present report. First, in this report based on previous studies, the general framework of business models in the Internet of Things has been analyzed, and then the suggested framework is presented. In the following, the business models of the Internet of Things and the impact of Internet of Things on the existing business models are studied. Thus, first of all, recognizing the elements of business models related to the Internet of Things is essential which has been addressed in the report. The results of different studies led to recognition of almost all business models in this field and to stating the most important components of the business models affected by the Internet of Things. The authors are still in the beginning of the process of Internet of Things. Most of the companies are willing to offer and sell the connected products and in near future, the majority of organizations will adopt more complex and ecosystem models. The purpose of this report is to offers social and business ideas to innovators so they can improve business models using the Internet of Things. This report examines the business models and proposed existing frameworks along with the economic energy of Internet of Things according to the successful projects in this scope, and offers the general logic for services or products that can be used as the basis of specific elements and patterns of business models of IOT.
Minimizing Movement to Establish the Connectivity of Randomly Deployed Robots

We study the following connectivity formation problem: Robots equipped with radio transmitters with a bounded communication range are scattered over a large area. They would like to relocate so as to form a connected network as soon as possible. Where should each robot move? We present an $O(\sqrt{n})$-factor approximation algorithm for this problem when $n$ robots are initially distributed uniformly at random in a bounded area. In addition to analytical proofs, we verify the performance of our algorithm through simulations.
Bacterial Memory of Persisters: Bacterial Persister Cells Can Retain Their Phenotype for Days or Weeks After Withdrawal From Colony–Biofilm Culture

Persister cells, or persisters, are a specific subpopulation of bacterial cells that have acquired temporary antibiotic-resistant phenotypes. In this study, we showed that Escherichia coli produces many more persister cells in colony–biofilm culture than in the usual liquid culture and that these persisters can be maintained in higher numbers than those from liquid culture for up to 4 weeks at 37°C in a fresh, nutrient-rich, antibiotic-containing medium, even after complete withdrawal from the colony–biofilm culture. This suggests the presence of a long-retention effect, or “memory effect”, in the persister cell state of E. coli cells. We also discovered that such increases in persisters during colony–biofilm culture and their memory effects are common, to a greater or lesser degree, in other bacterial species. This is true not only for gram-negative bacteria (Acinetobacter and Salmonella) but also for gram-positive bacteria (Staphylococcus and Bacillus). This is the first report to suggest the presence of a common memory mechanism for the persister cell state, which is inscribed during colony–biofilm culture, in a wide variety of bacteria.
Perceptions of Censorship and Moderation Bias in Political Debate Forums

Moderators are believed to play a crucial role in ensuring the quality of discussion in online political debate forums. The line between moderation and illegitimate censorship, where certain views or individuals are unfairly suppressed, however, is often difficult to define. To better understand the relationship between moderation and censorship, we investigate whether users' perception of moderator bias is supported by how moderators act, using the Big Issues Debate (BID) group on Ravelry as our platform of study. We present our method for measuring bias while taking into account the posting behavior of a user, then apply our method to investigate whether moderators make decisions biased against viewpoints that they may have the incentive to suppress. We find evidence to suggest that while moderators may make decisions biased against individuals with unpopular viewpoints, the effect of this bias is small and often overblown by the users experiencing bias.We argue that the perception of bias by itself is an issue in online political discussions and suggest technological interventions to counteract the discrepancy between perceived and actual censorship in moderation.
How do simple evolutionary strategies and investment optimizations affect ecological patterns? The case of generalized Taylor's Law

Taylor's Law (TL) relates the variance to the mean of a random variable via power law. In ecology it applies to populationsand it is a common empirical pattern shared among different ecosystems. Measurements give power law exponent to be between 1 and 2, and more often to cluster around 2, whereas theoretical models predict TL exponent can assume any real value. In this paper, adopting the framework of multiplicative growth models in a Markovian environment, we investigate the possibility of evolutionary strategies to be responsible for TL exponent to be in a finite range. We implement three different strategies the individuals can follow and for each strategy set two different optimization investment objectives. In all the studied cases we find TL exponent can assume any real value due to the existence of regions of the model parameters in which the exponent can diverge. Furthermore, under natural hypothesis on the dynamics of the environment, the shapes of these regions do not depend on different strategies adopted and nor on the optimization objective. Thus the introduction of strategies dose not affect the range of TL exponent in the model. In our theoretical framework rare events are shaping the value of the TL exponent, suggesting, as hinted by previous works, that empirical values may be a statistical artifact following from under sampling.
Sex-Based Differences in Gut Microbiota Composition in Response to Tuna Oil and Algae Oil Supplementation in a D-galactose-Induced Aging Mouse Model

Our previous work indicated that a mixture of tuna oil and algae oil treatment in male mice effectively relieved D-galactose (D-gal)-induced aging and resulted in gut microbiota alterations, and that the best anti-aging effects were observed for a tuna oil to algae oil ratio of 1:2. However, the possibility of a sex-based difference in the anti-aging effect of the tuna oil and algae oil mixture or gut microbiota variation, has rarely been investigated. In this study, the anti-aging effect of an oil mixture (1:2) in male and female mice was measured, and oil treatment improved the learning and cognition of mice that were damaged by D-gal, increased the activities of anti-oxidative enzymes, and decreased the level of MDA, which acted as a hallmark of oxidative damage to lipids. Male mice showed better anti-aging effects than female mice with a specific oil mixture ratio, and the clinical drug donepezil showed a similar or better effect on aging alleviation than oil treatments in both sexes. On the other hand, the same oil treatment led to different gut microbiota composition alterations in male and female mice. Redundancy analysis (RDA) identified 31 and 30 key operational taxonomic units (OTUs) in the male and female mice, respectively, and only three of these OTUs overlapped. Moreover, the abundance of Lactobacillus and several probiotic-like butyric acid producers was higher in male mice than in female mice, whereas the abundance of some inflammation-related genera, such as Clostridium XlVa, was lower in male mice. In conclusion, this study indicated the sex-based differences related to the anti-aging effects of tuna oil and algae oil treatment are accompanied by sex-based differences in gut microbiota modulation.
Batch Random Walk for GPU-Based Classical Planning

Graphical processing units (GPUs) have become ubiquitous because they offer the ability to perform cost and energy efficient massively parallel computation. We investigate forward search classical planning on GPUs based on Monte Carlo Random Walk (MRW). We first show experimentally that straightforward parallelizations of MRW perform poorly. Next, we propose Batch MRW (BMRW), a generalization of MRW which performs random walks starting with many seed states, in contrast to traditional MRW which used a single seed state. We evaluate a sequential implementation of BMRW on a single CPU core, and show that a sequential, satisficing planner based on BMRW performs comparably with previous state-of-the-art MRW-based planners. Then, we propose BMRWG, which uses a GPU to perform random walks. We show that BMRWG achieves significant speedup compared to BMRW and achieves competitive performance on a number of IPC benchmark domains.
Storage and retrieval of massive heterogeneous IoT data based on hybrid storage

With the rapid development of the Internet of Things (IoT), the IoT is characterized by a wide variety of data sources, large scale and heterogeneous structure. But those characteristics bring great difficulties to the storage and rapid retrieval of IoT data. By considering the common attributes of IoT data, based on plug-in ideas, combined with Redis and HBase, the paper proposes a framework named HSFRH-IoT, which solves the problem of efficient storage and retrieval of massive heterogeneous IOT. Finally, the insertion and query performance of the proposed HSFRH-IoT framework is tested in detail, and the results shows that it has better performance than other RDBMS based solutions.
Evaluating the training transfer of Head-Mounted Display based training for assembly tasks

The automotive industry is growing constantly and more and more assembly workers are needed to negotiate the production volume. The training of new employees is essential to ensure premium quality products and processes. New technologies for training such as head-mounted displays (HMDs) receive a growing amount of attention by the scientific community, especially in the industrial domain. Due to its possibility to work hands-free while providing users with necessary augmented information, HMDs can enhance the quality and efficiency of assembly training tasks. However, comprehensive evaluations in industrial environments regarding the training transfer using augmented reality (AR) technologies are still very limited. In this paper, we aim to close this gap by conducting a user study with two groups and 30 participants, measuring the training transfer. We compare the effects of two slightly different HMD-based training applications. The first group complete a tutorial, beginner, intermediate and expert training level, while the second group received an additional quiz level. Results show that group two needed 17% more time to complete the training but made 79% less sequence mistakes compared to the first group. Additionally, we compare the user satisfaction by using the system usability scale (SUS) and the perceived workload by measuring the NASA-TLX.
Learning dynamical systems with particle stochastic approximation EM

We present the particle stochastic approximation EM (PSAEM) algorithm for learning of dynamical systems. The method builds on the EM algorithm, an iterative procedure for maximum likelihood inference in latent variable models. By combining stochastic approximation EM and particle Gibbs with ancestor sampling (PGAS), PSAEM obtains superior computational performance and convergence properties compared to plain particle-smoothing-based approximations of the EM algorithm. PSAEM can be used for plain maximum likelihood inference as well as for empirical Bayes learning of hyperparameters. Specifically, the latter point means that existing PGAS implementations easily can be extended with PSAEM to estimate hyperparameters at almost no extra computational cost. We discuss the convergence properties of the algorithm, and demonstrate it on several signal processing applications.
An optimal design of printed log-periodic antenna for L-band EMC applications

This paper proposes a 12-dipole printed log-periodic dipole array antenna (PLPDA) for L-band electromagnetic compatibility (EMC) applications. The antenna proposed is designed to operate in the frequency range of 0.8 GHz to 2.5 GHz. The proposed antenna design is the result of optimization performed using the Trusted Region Framework (TRF) algorithm. The low values of the S-parameter (S) of the optimized PLPDA antenna design suggest good matching. The antenna also provides satisfactory realized gain between 4.5 dBi to 6.3 dBi along with good directional characteristics in its operating frequency range.
Gaussian Process Classification as Metric Learning for Forensic Writer Identification

In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed. An unsupervised feature learning approach, based on dense contour descriptor sampling, was combined with a novel way of learning a general space for clustering writer hands, in a forensic setting. The metric learning inference was based on multiclass Gaussian process classification. Using the popular datasets IAM and CVL combined, the evaluation was performed on close to 1000 writer hands. This paper builds on earlier work from our group on building a system for estimating the production dates of medieval manuscripts, and act as a foundation for future use of writer identification techniques on our historical data.
Childhood adiposity, adult adiposity, and the ACE gene insertion/deletion polymorphism: evidence of gene–environment interaction effects on adult blood pressure and hypertension status in adulthood

Background: Genetic variants may modify the associations of adiposity measures with blood pressure (BP) and hypertension. The insertion/deletion (I/D) polymorphism in the angiotensin-converting enzyme (ACE) gene is an attractive candidate. Aims: To examine interaction effects between I/D polymorphism and adiposity measures (BMI, waist circumference, waist-to-hip ratio, and skinfold thickness) during childhood and adulthood in relation to adult BP and hypertension. Methods: Data were available for 4835 participants from three prospective cohort studies. Multivariable linear regression models for adult SBP and DBP, and multivariable logistic regression models for hypertension were fit that included interaction effects between child or adult adiposity and I/D polymorphism. Results: Evidence for interaction effects on BP/hypertension were found across the three studies. Compared with childhood measures, the effect modification appeared to be more consistent when using adult adiposity. In particular, the adverse effects of greater adult waist circumference on increasing adult SBP and DBP appeared to be larger among carriers of ACE DD (or GG) [adjusted linear regression coefficients 0.26, 95% CI (0.21–0.31) and 0.28 (0.24–0.32) for SBP and DBP, respectively] and ID (or AG) genotypes [0.25 (0.21–0.29) and 0.25 (0.21–0.28), respectively], whereas those with II (or AA) genotypes had smaller effects [0.15 (0.09–0.21) and 0.19 (0.13–0.23)]. Conclusion: ACE genetic variation may modify the effect of adult adiposity on increasing BP and risk of hypertension in adulthood. Individuals with ACE DD (or GG) and/or ID (or AG) genotypes, compared with those with II (or AA) genotype, appear more vulnerable to the impact of excess adiposity.