Implementation of Landslide Susceptibility Model Using Machine Learning for Semi Detailed Map Scale in Mountainous Region of Java Island

Landslide susceptibility modeling using neural network (ANN) are applied to semi detailed volcanic-sedimentary water catchment. Annually landslide occurred in catchment area frequently in unconsolidated and weathered material combined with uncertainty in rainfall pattern that complicated landslide occurrence. Data used for analysis including landslide inventory, geology, digital elevation related data, distance to stream, and several other available data. Results show that machine learning method yield fair result data based on evaluation on Area under Curve (AUC). Thus, it can be suggested that machine learning methods for landslide susceptibility model could still be develop to produce robust prediction model with different characterization of parameter data and machine learning parameters.

Joint inversion of strain and tilt data using the Akaike’s Bayesian information criterion to map detailed slip distributions of short-term slow slip events

Along the Nankai Trough subduction zone, southwest Japan, short-term slow slip events (SSEs) are commonly detected in strain and tilt records. These observational data have been used in rectangular fault models with uniform slip to analyze SSEs; however, the assumption of uniform slip precludes the possibility of mapping the slip distribution in detail. We report here an inversion method, based on the joint use of strain and tilt data and evaluated in terms of the Akaike’s Bayesian information criterion (ABIC), to estimate the slip distributions of short-term SSEs on the plate interface. Tests of this method yield slip distributions with smaller errors than are possible with the use of strain or tilt data alone. This method provides detailed spatial slip distributions of short-term SSEs including probability estimates, enabling improved monitoring of their locations and amounts of slip.

Impact of creative accounting on the company value: Empirical study for Slovakia

Research background: Procedures and methods for determining the value of a company are different. The purpose of determining the value of the company, what results the company reports and also who performs the valuation has a significant influence on the choice of the method. Purpose of the article: The determination of the final value of the company is influenced by many factors, economic, technical, specifics of the company and also the date on which the value is calculated and who performs the evaluation. Methods: In expert practice in determining the value of the company, we work mainly with methods based on property and income principles (asset method, yield method). The basic material for calculating the value of a company is the company’s accounting, which, however, can often be influenced. There can be several purposes for distorting accounting information (financial statements). The most common reason is the reduction of the tax base, or artificial improvement of the achieved results. Creative accounting practices significantly affect the structure of the company as well as its financial results. Findings & Value added: The main goal of the paper is to quantify a few examples, which will contribute to reducing the economic result. Subsequently, we analyse these interventions how they can affect the resulting general value of the company determined by the expert and whether the expert is able to detect them.

Does the “surprisingly popular” method yield accurate crowdsourced predictions?

The “surprisingly popular” method (SP) of aggregating individual judgments has shown promise in overcoming a weakness of other crowdsourcing methods—situations in which the majority is incorrect. This method relies on participants’ estimates of other participants’ judgments; when an option is chosen more often than the average metacognitive judgments of that option, it is “surprisingly popular” and is selected by the method. Although SP has been shown to improve group decision making about factual propositions (e.g., state capitals), its application to future outcomes has been limited. In three preregistered studies, we compared SP to other methods of aggregating individual predictions about future events. Study 1 examined predictions of football games, Study 2 examined predictions of the 2018 US midterm elections, and Study 3 examined predictions of basketball games. When applied to judgments made by objectively assessed experts, SP performed slightly better than other aggregation methods. Although there is still more to learn about the conditions under which SP is effective, it shows promise as a means of crowdsourcing predictions of future outcomes.

Whether the Pairwise Rating Method and the Spatial Arrangement Method Yield Comparable Dimensionalities Depends on the Dimensionality Choice Procedure

We investigate whether two methods for obtaining similarity data yield multidimensional scaling (MDS) solutions of comparable dimensionality. In the Pairwise Rating Method (PRaM), participants rate the (dis)similarity of all pairs of stimuli on a Likert scale. In the Spatial Arrangement Method (SpAM), participants organize stimuli on a computer screen so that the distance between stimuli represents their perceived dissimilarity. Across two studies that included eight semantic categories with varying numbers of both pictorial and verbal exemplars, we did not find consistent dimensionality differences between the two similarity measurement methods. The results alleviate the concern that because of its two-dimensional nature, SpAM might underestimate the dimensionality of high-dimensional stimuli compared to PRaM. Aggregating the SpAM similarity data from a sufficient number of participants can yield spatial representations with more than two dimensions. However, the resulting number of dimensions was found to be highly dependent on the dimensionality choice procedure. Even for specific combinations of a single category and similarity measurement method, different dimensionalities were obtained depending on whether the reliability of the similarity data, Monte Carlo simulations, or predictive correlations were used to establish the number of dimensions, indicating the need for a more systematic investigation into dimensionality selection for MDS.

Tuning the size and morphology of P3HT/PCBM composite nanoparticles: towards optimized water-processable organic solar cells

Different process parameters in miniemulsion method yield different particle size and morphology of P3HT/ PCBM composite nanoparticles.

Classification Models and Hybrid Feature Selection Method to Improve Crop Performance

In this paper classification models and hybrid feature selection methods are implanted on benchmark dataset on the Mango and Maize. Particle Swarm Optimization–Support Vector Machine (PSO-SVM) classification algorithm for the selection of important features from the Mango and Maize datasets to analysis and also compare with the novel classification techniques. Various experiments conducted on these datasets, provide more generated rules and high selection of features using PSO-SVM algorithm and Fuzzy Decision Tree. The proposed method yield high accuracy output as compared to the existing methods with minimum Error Rate and Maximum Positive Rate.

The effect of seeding rates and sowing methodson yield and quality seeds of chickpea

The results of three-year studies of the influence of seed sowing rates and sowing methods on the formation of yield capacity and quality of chickpea seeds are highlighted. The results of the research indicate a significant influence of the studied factors on the level of yield capacity of chickpea seeds. On average, for three years of research, the highest seed yield capacity in the experiment has been observed in the variety Odyssey – 2,16 t/ha. Among the studied variants of the sowing method, the highest rates of chickpea seed yield capacity have been provided by the row method with a row spacing of 30 cm (with a nutrition area of one plant of 120 cm2) at the average sowing rate of 0,7 million units/ha. In particular, the yield capacity of chickpea seeds of Budjak and Odyssey varieties under this combination of the studied factors made up 2,37 and 2,49 t/ha, respectively. The maximum protein content in chickpea seeds in both studied varieties has been obtained on the variants with a minimum seeding rate of 0,5 million units/ha, but the highest protein yield – 0,415 t/ha of Odyssey variety and 0,435 t/ha of Budjak variety - was noted on the variants with a seeding rate of 0,7 million units/ha. Key words: chickpeas, seeding rate, sowing method, yield capacity, protein content.