Why is the statistical revolution not progressing? Vicious cycle of the scientific reform

To analyze the results of the research, behavioral scientists widely use a statistical rule that sets the significance level to 0.05. Recently, two recommendations on how to improve statistical inference were published: to redefine statistical significance to 0.005, and to select and justify the alpha. We analyzed the empirical work that cited the original recommendation papers, as well as the papers published by the scientist that co-authored the publications. About half of the numerous papers citing these recommendations adhered to them already in the first year since their publication. What is striking, the original authors that proposed the recommendations followed their own recommendations only in 6% of their empirical work. We surveyed the authors asking them to identify major obstacles they experienced while trying to implement their own recommendations, and obstacles they think others could expect or experience.

A Stochastic Model for Nanoparticle Deposits Growth

Natural events and human activities are responsible for the generation and transport of large amounts of micro-sized particles, which could contaminate several engineering devices like solar panels, wind turbines, and aero-engines. In industrial processes, systems as heat exchangers, fans, and dust collectors are continuously affected by nanoparticles' interaction. For several applications, the adhesion of such nanoparticles is detrimental, generating safety and performance issues. Particle-to-particle and particle-to-surface interactions are well known, even if a general explanation of nanoparticle deposit growth is still unknown. In the present paper, an interpretation of deposit growth due to nanoparticle deposition can predict particle adhesion, and layer accretion is proposed. A statistical model and a set of coefficients are used to generalize nanoparticle deposits' growth by an S-shaped function. In particular, the nanoparticle deposits grow analogously to a typical autonomous population settlement in a virgin area following statistical rule, which includes the initial growth, the successive stable condition (development), and catastrophic events able to destroy the layer. This approach generalizes nanoparticle adhesion/deposition behavior, overpassing the constraints reported in common deposition models, mainly focused on the mechanical aspect of the nanoparticle impact event. The catastrophic events, such as layer detachment, are modeled with a Poisson's distribution, related to material characteristics and impact conditions. This innovative approach, analogies, and coefficients applied to common engineering applications may be the starting point for improving the prediction capability of nanoparticle deposition.

A Stochastic Model for Nanoparticle Deposits Growth

Natural events and human activities are responsible for the generation and transport of large amounts of micro-sized particles, which could contaminate several engineering devices like solar panels, wind turbines, and aero-engines. In industrial processes, systems as heat exchangers, fans, and dust collectors are continuously affected by nanoparticles’ interaction. For several applications, the adhesion of such nanoparticles is detrimental, generating safety and performance issues. Particle-to-particle and particle-to-surface interactions are well known, even if a general explanation of nanoparticle deposit growth is still unknown. In the present paper, an interpretation of deposit growth due to nanoparticle deposition can predict particle adhesion, and layer accretion is proposed. A statistical model and a set of coefficients are used to generalize nanoparticle deposits’ growth by an S-shaped function. In particular, the nanoparticle deposits grow analogously to a typical autonomous population settlement in a virgin area following statistical rule, which includes the initial growth, the successive stable condition (development), and catastrophic events able to destroy the layer. This approach generalizes nanoparticle adhesion/deposition behavior, overpassing the constraints reported in common deposition models, mainly focused on the mechanical aspect of the nanoparticle impact event. The catastrophic events, such as layer detachment, are modeled with a Poisson’s distribution, related to material characteristics and impact conditions. This innovative approach, analogies, and coefficients applied to common engineering applications may be the starting point for improving the prediction capability of nanoparticle deposition.

GNSS Positioning Security: Automatic Anomaly Detection on Reference Stations

The dependency of critical infrastructures on Global Navigation Satellite Systems (GNSS) keeps increasing over the years. This over-reliance brings concerns as those systems are vulnerable and consequently prone to human-made perturbations, such as jamming and spoofing attacks. Solutions for detecting such disturbances are therefore crucially needed to raise GNSS users’ awareness and protection. This paper suggests an approach for detecting anomalous events (i.e., potentially an attack attempt) based on measurements recorded by Continuously Operating GNSS Reference Stations (CORS). Precisely, the anomaly detection process first consists in modeling the normal behavior of a given signal thanks to a predictive model which combines the Seasonal and Trend decomposition using LOESS and ARIMA algorithms. This model can then be used to predict the upcoming measurement values. Finally, we compare the predictions to the actual observations with a statistical rule and assess if those are normal or anomalous. While our anomaly detection approach is intended for real-time use, we assess its effectiveness on historical data. For simplicity and independence, we also focus on the Carrier-to-Noise Ratio only, though similar methods could apply to other observables. Our results prove the sensitivity of the proposed detection on a reported case of unintentional disturbance. Other anomalies in the historical data are also uncovered using that methodology and presented in this paper.

Analisis Problem Solving Skills Siswa pada Materi Lingkungan

The purpose of the research was to find out the level of the students' problem solving skills on environmental material at X class of MAN Bintan. The researcher applied descriptive quantitative approach as method for analyzing the data. Sampling in this study using Total Sampling technique with the sampel consisted of 59 students. This instrument is used to collect students' problem solving skills through six aspects to Chang & Kelly with 6 items essay and feature interviews. The data were analyzed with statistical rule in percentage. Based on the results of data analyze shows that a 77% rate highly classified. The researcher concluded that student at X class MAN Bintan has good problem solving skills. However, it still needs training and guidance to develop problem solving skills.

A Supervised Hybrid Statistical Catch-Up System Built on GABECE Gambian Data

In this paper we want to find a statistical rule that assigns a passing or failing grade to students who undertook at least three exams out of five in a national exam, instead of completely dismissing these students. While it is cruel to declare them as failing, especially if the reason for their absence it not intentional, they should have demonstrated enough merit in the three exams taken to deserve a chance to be declared passing. We use a special classification method and nearest neighbors methods based on the average grade and on the most modal grade to build a statistical rule in a supervised learning process. The study is built on the national GABECE educational data which is a considerable data covering seven years and all the six regions of the Gambia.

A Supervised Hybrid Statistical Catch-Up System Built on GABECE Gambian Data

In this paper we want to find a statistical rule that assigns a passing or failing grade to students who undertook at least three exams out of five in a national exam, instead of completely dismissing these students. While it is cruel to declare them as failing, especially if the reason for their absence it not intentional, they should have demonstrated enough merit in the three exams taken to deserve a chance to be declared passing. We use a special classification method and nearest neighbors methods based on the average grade and on the most modal grade to build a statistical rule in a supervised learning process. The study is built on the national GABECE educational data which is a considerable data covering seven years and all the six regions of the Gambia.

New Combined Method to Improve Arabic POS Tagging

One of the important tasks in Natural language processing is the part of speech tagging. For the Arabic language we have a lot of works but their performances do not rise to the required level, due to the complexity of the task and the Arabic language characteristics. In this work we study a combination between twodifferent approaches for Arabic POS-Taggers. The first one isa maximum entropy-based one, and the second is a statistical/rule-based one. Furthermore, we add a knowledge-based method to annotate Arabic particles. Our idea improves the accuracy rate. We passed from almost 85% to almost 90% using our combined method, which seem promoter.