Miracle-dz/UniFAR-FTUs huggingface dataset:
Laboratory Experiment and Numerical Analysis on the Precursory Hydraulic Process of Rainfall-Induced Slope Failure

A combined analysis involving a laboratory test and numerical modeling was performed to investigate the hydraulic processes leading to slope failure during rainfall. Through a laboratory landslide test in which artificial rainfall was applied to a homogeneous sandy slope, the timing and configurations of multiple slides were identified. In addition, volumetric water content was measured in real time through the use of monitoring sensors. The measured volumetric water content data were then used to validate the relevance of the numerical modeling results. The validated numerical modeling of the laboratory-scale slope failures provided insight into the hydraulic conditions that trigger landslides. According to the numerical modeling results, the miniaturized slope in the laboratory test was saturated in a manner so that the wetting front initially progresses downward and then the accumulated rainwater at the toe of the slope creates a water table that advances toward the crest. Furthermore, each of the five sequential failures that occurred during this experiment created slip surfaces where the pore-water pressure had achieved full saturation and an excessive pore-water pressure state. The findings of this study are expected to help understand the hydraulic prerequisites of landslide phenomena.
Preventive Effect of Hydrazinocurcumin on DEN-induced Hepatocarcinogenesis in Male SD Rats

Hepatocarcinoma is one of the major malignant tumors in humans, representing the fifth most prevalent malignancy worldwide and the third leading causes more than 250000 deaths annually (Farazi et al., 2006). Owing to advances in diagnostics and therapeutics, hepatocarcinoma can be curatively treated when detected at an early stage by applying therapies including surgical resection, radiofrequency ablation (RFA) and transcatheter arterial chemoembolization (TACE). However, curative treatment are often hampered by frequent recurrence of hepatocarcinoma (Okuda, 2007), because the remaining liver retains the potential for carcinogenesis (Kumada et al., 1997). Effective measures of chemoprevention represent an important hope for people living in these areas. To our disappointment, no chemotherapy agent has better benefit in controlled clinical trials and treatment outcome due to different kinds of reasons including drug resistance and toxicity to normal cells (Nagano, 2010). Curcumin (CUR), the active component of turmeric has potent anti-tumor effects to hepatocarcinoma in clinical trials (Dai et al., 2013) and several other cancers, such as colon cancer (Lee et al., 2009) and breast cancer (Mehta et al., 1997). Although CUR is remarkably non-
Land Use Changes Affecting Soil Organic Matter Accumulation in Topsoil and Subsoil in Northeast Thailand

The objectives of this study were to investigate effects of land use on accumulation of soil organic matter (SOM) in the soil profile (0–100 cm) and to determine pattern of SOM stock distribution in soil profiles. Soil samples were collected from five soil depths at 20 cm intervals from 0 to 100 cm under four adjacent land uses including forest, cassava, sugarcane, and paddy lands located in six districts of Maha Sarakham province in the Northeast of Thailand. When considering SOM stock among different land uses in all locations, forest soils had significantly higher total SOM stocks in 0–100 cm (193 Mg·C·ha−1) than those in cassava, sugarcane, and paddy soils in all locations. Leaf litter and remaining rice stover on soil surfaces resulted in a higher amount of SOM stocks in topsoil (0–20 cm) than subsoil (20–100 cm) in some forest and paddy land uses. General pattern of SOM stock distribution in soil profiles was such that the SOM stock declined with soil depth. Although SOM stocks decreased with depth, the subsoil stock contributes to longer term storage of C than topsoils as they are more stabilized through adsorption onto clay fraction in finer textured subsoil than those of the topsoils. Agricultural practices, notably applications of organic materials, such as cattle manure, could increase subsoil SOM stock as found in some agricultural land uses (cassava and sugarcane) in some location in our study. Upland agricultural land uses, notably cassava, caused high rate of soil degradation. To restore soil fertility of these agricultural lands, appropriate agronomic practices including application of organic soil amendments, return of crop residues, and reduction of soil disturbance to increase and maintain SOM stock, should be practiced.
Test of the analysis of competing hypotheses in legaldecision‐making

Correspondence Enide Maegherman, Department of Criminal Law and Criminology, Faculty of Law, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands. Email: enide.maegherman@ maastrichtuniversity.nl Summary The analysis of competing hypotheses (ACH) has been suggested to be a method that can protect against confirmation bias in the context of intelligence analysis. In the current study, we aimed to determine whether ACH could counter confirmation bias in the reasoning with evidence in the context of criminal law proceedings. Law students (N = 191) received information about the ACH method or general information about biases. They were given a case vignette with a main suspect and a list of 24 questions, 6 of which they could ask about the case. Half of the questions related to incriminating information, whereas the other half related to exonerating information. Contrary to our expectations, participants in both conditions favoured questions relating to exonerating information and rated the exonerating evidence as being more important for their decision. Despite the lack of bias observed, it seems participants failed to properly apply the ACH method.
The interaction between Foreign Direct Investment and Domestic Investment: Is there crowding out effect?

After the fall of communism, Central and Eastern European countries have experienced a transition process in which a remarkable increase is observed in foreign direct investment flows into the region. During this process, when transition countries tried to adopt a free-market economy instead of a closed centrally structured economy, funds obtained through FDI constituted an essential way of financing for these countries that were trying to restructure their economy. Study questions the existence of the crowding-out effect by using data from Eastern European Countries, including Romania, the Russian Federation, Moldova, Poland, Bulgaria, Hungary, Slovak Republic, Ukraine. With this aim, PANIC Bai and Ng (2004), the bias-corrected PANIC Westerlund and Larsson (2009) unit root tests, and panel data analysis are implemented. Results obtained were consistent with theoretical expectations and showed that FDI had a crowding-out effect in the short run but, in the long run, a crowding-in impact on domestic investment.
Distribution-free Algorithms for Learning Enabled Optimization with Non-parametric Estimation

This paper studies a fusion of concepts from stochastic optimization and non-parametric statistical learning, in which data is available in the form of covariates interpreted as predictors and responses. Such models are designed to impart greater agility, allowing decisions under uncertainty to adapt to the knowledge of the predictors (leading indicators). Specialized algorithms can be looked upon as learning enabled optimization (LEO) algorithms. This paper focuses on equipping LEO with non-parametric estimation approaches (LEON) which provide asymptotically optimal decisions without requiring the speciﬁcation of a distribution. In particular, our framework accommodates several non-parametric estimation schemes, including k nearest neighbors ( k NN), and other standard kernel estimators under one uniﬁed framework. Several techniques to improve the quality of decisions are discussed. Finally, we demonstrate the computational performance of Robust LEON-k NN and Robust LEON-kernel for a well-known instance arising in logistics.
English Language Teachers’ Oral Corrective Preferences and Practices Across Proficiency Groups

Studies on oral error correction in second language acquisition have been tilted towards cognitive aspects ignoring the affective and practical dimensions. This study attempted to fill this gap by investigating the role of students’ proficiency levels in five English language teachers’ corrective behavior. Follow-up interviews were conducted with the observed teachers. The results showed that the teachers provided more corrections to less proficient learners though they preferred more correction for advanced learners and used mainly recast for both groups, avoiding explicit forms of correction. They were mainly concerned with the affective aspects of oral error correction and acted on their own value system and teaching experience. The findings carry important implications for teacher education programs and the studies in this regard.
Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach

Deep Neural Networks (DNNs) have gained significant popularity in various Natural Language Processing tasks. However, the lack of interpretability of DNNs induces challenges to evaluate the robustness of DNNs. In this paper, we particularly focus on DNNs on sentiment analysis and conduct an empirical investigation on the sensitivity of DNNs. Specifically, we apply a scoring function to rank words importance without depending on the parameters or structure of the deep neural model. Then, we scan characteristics of these words to identify the model’s weakness and perturb words to craft targeted attacks that exploit this weakness. We conduct extensive experiments on different neural network models across several real-world datasets. We report four intriguing findings: i) modern deep learning models for sentiment analysis ignore important sentiment terms such as opinion adjectives (i.e., amazing or terrible), ii) adjective words contribute to fooling sentiment analysis models more than other Parts-of-Speech (POS) categories, iii) changing or removing up to 10 adjectives words in a review text only decreases the accuracy up to 2%, and iv) modern models are unable to recognize the difference between an objective and a subjective review text1.
The Role of Farmer to Farmer Extension for Rice Farmer Independence in Bogor

Farmer to farmer extension has considerable potential role in growing rice farmer independence because it has a strong social capital. The purpose of this study is to analyze the role of farmer to farmer extensionin improving rice farmer independence, analyze the factors that influence the farmer perception on the role of farmer to farmer extension and formulate strategies to strengthen the role of farmer to farmer extension to be more involved in counseling activities. The design of this study was a survey of 75 respondents representing rice farmers which become self-help agricultural extension. Data collection was conducted from September to November 2016. Data processing used descriptive technique and Path Analysis. Based on the study result, it concludes that overall, the role of farmer to farmer extensionin medium category is to enhance rice farmer independence in Pasir Madang Village, Sukajaya Subdistrict, Bogor. Factors which have a strong influence on the implementation to the role of farmer to farmer extensionin increasing rice farmer independence of the internal characteristic of the farmer to farmer extension itself, that is the position in the farmer community groups, counseling, ability to communicate/counsel, ability to motivate and have a business in agriculture. Strategies to strengthen the role of farmer to farmer extension are conducted through capacity building in extension methodology.
Quaternary stratigraphy of the northern Gulf of Cadiz continental margin : 1 New insights into controlling factors and global implications 2

Thomas MESTDAGH , Francisco J. LOBO , Estefanía LLAVE , F. Javier HERNÁNDEZ-MOLINA , David 3 VAN ROOIJ a 4 5 a Renard Centre of Marine Geology, Department of Geology, Ghent University, Krijgslaan 281 (S8), 9000 Gent, 6 Belgium 7 b Instituto Andaluz de Ciencias de la Tierra, CSIC-Universidad de Granada, Av. de las Palmeras 4, 18100 Armilla, 8 Spain 9 c Instituto Geológico y Minero de España (IGME), Ríos Rosas 23, 28003 Madrid, Spain 10 d Department of Earth Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UK 11 12 (*) Corresponding author: Thomas.Mestdagh@UGent.be 13 14 15 16 17 18 Highlights 19 A revised late Quaternary seismic and sequence stratigraphic framework for the northern Gulf of 20
Reﬁnements on the Complementary PDB Construction Mechanism Anonymous

Pattern database (PDB) is one of the most popular automated heuristic generation techniques. A PDB maps states in a planning task to abstract states by considering a subset of variables and stores their optimal costs to the abstract goal in a look up table. As the result of the progress made on symbolic search over recent years, symbolic-PDB-based planners achieved impressive results in the International Planning Competition (IPC) 2018. Among them, Complementary 1 (CPC1) tied as the second best planners and the best non-portfolio planners in the cost optimal track, only 2 tasks behind the winner. It uses a combination of different pattern generation algorithms to construct PDBs that are complementary to existing ones. As shown in the post contest experiments, there is room for improvement. In this paper, we would like to present our work on reﬁning the PDB construction mechanism of CPC1. By testing on IPC 2018 benchmarks, the results show that a signiﬁcant improvement is made on our modiﬁed planner over the original version.
Plant Leaf Recognition Using Texture Features and Semi-Supervised Spherical K-means Clustering

Automatic plant leave recognition using digital images and machine learning techniques is an important task. The disadvantage of supervised learning techniques is that they are limited to learn from labelled datasets which are often expensive to obtain. In this paper, a novel decision fusion framework is proposed by combining semi-supervised clustering with the well known image features analysis methods in computer vision. Initially the leave image features are generated by applying the Grey Level Co-occurrence Matrix analysis to the processed leave images transformed by Gabor or Laplacian of Gaussian filters. Then an on-line spherical k-means clustering technique, guided by a minimum number of labelled leaves, is used to train the base classifiers. The final decision of classification is produced by selecting classifier which produces the max-cosine value amongst the baseline classifiers. Comparative experiments have been carried out to demonstrate that proposed approaches are suited for automatic leave type recognition.
Comparative Evaluation of Dexmedetomedine versus Clonidine as an Adjuvant in Supraclavicular Brachial Plexus Block

Background: Supraclavicular block is considered to be the ideal block for upper extremity surgeries as it provides ideal operative conditions by complete muscle relaxation and stable intra operative hemodynamics. Aim and Objectives: The aim of the study was to evaluate the efficacy and safety of Clonidine and Dexmedetomedine as an adjuvant to Ropivacaine in supraclavicular brachial plexus block. Material and Methods: Ninety patients were divided randomly into 3 groups of 30 each. Group CL: Patients received injection Ropivacaine 0.5% (30 ml) + normal saline (1 ml), Group CD: Patients received injection Ropivacaine 0.5% (30 ml) + injection Clonidine (150 μg), Group DX: Patients received injection Ropivacaine 0.5% (30 ml) + injection Dexmedetomidine (100 μg). Onset of sensory and motor block, duration of analgesia and motor block, complications and side effects of the study drugs were compared among the three groups. Results: The onset of sensory and motor block was earliest in Group DX, followed by Group CD, followed by Group CL. Complete sensory and motor block were earliest in Group DX, followed by Group CD, followed by Group CL. Similarly duration of sensory and motor block was maximum in Group DX, followed by Group CD, followed by Group CL (p<0.05). Conclusion: Dexmedetomedine and Clonidine when added to Ropivacaine for supraclavicular brachial plexus block prolong the duration of analgesia. But Dexmedetomedine is a better adjuvant as compared to Clonidine.
Crop residue effect evaluation on soil physical and Chemecal properties after harvest of wheat

A field experiment was carried out during rabbi season 2018-19 to finnd out the effect of crop residue on physical and chemical properties of soil at Agronomy Instructional Farm, Chimanbhai Patel College of Agriculture, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar. Total eight treatments of different crop residue were tested randomized complet block design. An application of castor shell @ 1.00t/ha + mustard husk @ 1.00t/ha + cumin straw @ 1.00 t/ha improved water holding capacity of soil and reduced bulk density and their by improved soil physical property, where as an application of castor shell @ 2.50 t/ha (T2) registered significantly highest value of organic carbon, N, P2O5 and K2O.
Adaptation of a wheel loader automatic bucket filling neural network using reinforcement learning

Bucket-filling is a repetitive task in earth-moving operations with wheel-loaders, which needs to be automated to enable efficient remote control and autonomous operation. Ideally, an automated bucket-filling solution should work for different machine-pile environments, with a minimum of manual retraining. It has been shown that for a given machine-pile environment, a time-delay neural network can efficiently fill the bucket after imitation-based learning from 100 examples by one expert operator. Can such a bucket-filling network be automatically adapted to different machine-pile environments without further imitation learning by optimization of a utility or reward function? This paper investigates the use of a deterministic actor-critic reinforcement learning algorithm for automatic adaptation of a neural network in a new pile environment. The algorithm is used to automatically adapt a bucket-filling network for medium coarse gravel to a cobble-gravel pile environment. The experiments presented are performed with a Volvo L180H wheel-loader in a real-world setting. We found that the bucket-weights in the novel pile environment can improve by five to ten percent within one hour of reinforcement learning with less than 40 bucket-filling trials. This result was obtained after investigating two different reward functions motivated by domain knowledge.
The maximal hyperrings of quotients

We show that the maximal quotient hyperring Qmr(R) of a semiprime hyperring R can be obtained in a similar way to a maximal quotient ring. In this regard we introduce and study some basic notions of hyperrings such as dense hyperideal, essential hyperideal, singular hyperideal and prove some results satisfying them. Finally, we show that if R is a semiprime hyperring and Q = Qmr(R), then Q is regular (in the sense of von Neumann) if and only if R has a zero right singular hyperideal.
Is the Phillips curve framework still useful for understanding inflation dynamics in South Africa?

The ‘Phillips curve’ is a commonly used benchmark for modelling inflation, based on the intuition that greater economic slack (i.e. a larger output gap) should be associated with decelerating inflation. But there is an ongoing debate about the usefulness of the Phillips curve to explain inflation. Explanations for an observed weakening between measures of slack and inflation have included: mismeasurement of economic slack, the influence of other factors (such as the exchange rate, foreign inflation or wages) or whether some components of the inflation basket are more sensitive to the business cycle than other. To advance this discussion, we consider an augmented Phillips curve specification that accounts for various drivers of inflation and test different measures of economic slack in forecasting inflation and GDP outcomes. We show that a Phillips curve relationship continues to exist in South Africa. We find the slack measure that performs best is one that includes labour market indicators and indicated low capacity pressure at the end of the sample (2019Q3). While the output gap-inflation channel continues to operate, the contributions to inflationary pressures of factors such as past inflation and inflation expectations have been much more important over recent years. JEL Classification:E27,E31,E37
Discriminative Feature Pyramid Network For Object Detection In Remote Sensing Images

Multi-class geospatial object detection in remote sensing images suffer great challenges, such as large scales variability and complex background. Although feature pyramid network (FPN) can alleviate the problem of scale variation to some extent, it causes the loss of spatial and semantic information which is not conducive to object location. To address the above problem, this paper proposes a discriminative feature pyramid network (DFPN) by introducing a global guidance module (GGM) and a feature aggregation module (FAM). Specifically, the global guidance module delivers the high-level semantic information to lower layers, so as to obtain feature maps with stronger semantic information to eliminate the interference caused by complex background. The feature aggregation module enhances the interflow of information between different layers and better captures the discrimination information at each layer. We validate the effectiveness of our method on the NWPU VHR-10 and RSOD datasets, the results outperform baseline by 2.06 and 3.88 points respectively.
Effect of rainfall on growth parameters and yield of four varieties of maize (Zea Mays), grown in Korhogo Commune, northern Cote d'Ivoire

Maize stands out as one of the largest and most cultivated cereals in the Korhogo region of northern Côte d'Ivoire. This activity is potentially dependent on climate, which is increasingly subject to variations and decreases in rainfall. The study focused on the analysis of the influence of rainfall on the growth parameters and yield of four maize varieties grown in northern Côte d'Ivoire. Study was conducted at experimental site of Saint Camille, located in commune of Korhogo. The experimental device consisted of Fisher block, completely randomized and comprised 4 blocks and 4 varieties (Benin 7, Benin 13, Ferké and Komsaya), compared to four different planting dates. Growth and performance parameters were measured. The results showed that over the 3 years (2015 to 2017) of data, rainfall as a whole indicated a weaker trend in northern Côte d'Ivoire, with an annual average of 1060 mm of water. The average annual rain number ranged from 54 to 68 days. Ferké and Komsaya, with average yields of 3.01 t/ha and 2.86 t/ha at first seeding and 1.02 t/ha and 1.07 t/ha at the fourth seedlings, were the most productive during different planting periods. These two varieties were the most adapted to climatic conditions of the north of country and are the best to be recommended to maize producers in the region, especially for late planting.