Miracle-dz/UniFAR-FTUs huggingface dataset:
Sub-soil irrigation does not lower greenhouse gas emission from drained peat meadows

Abstract. Current water management in drained peatlands to facilitate agricultural use, leads to soil subsidence and strongly increases greenhouse gas (GHG) emission. High-density, sub-soil irrigation/drainage systems have been proposed as a potential climate mitigation measure, while maintaining high biomass production. In summer, sub-soil irrigation can potentially reduce peat decomposition by preventing groundwater tables to drop below −60 cm. In 2017–2018, we evaluated the effects of sub-soil irrigation on GHG emissions (CO2, CH4, N2O) for four dairy farms on drained peat meadows in the Netherlands. Each farm had a treatment site with perforated pipes at 70 cm below soil level spacing 5–6 m to improve both drainage (winter- spring) and irrigation (summer) of the subsoil, and a control site drained only by ditches (ditch water level −60/−90 cm, 100 m distance between ditches). GHG emissions were measured using closed chambers (0.8 x 0.8 m) every 2–4 weeks. C inputs by manure and C export by grass yields were accounted for. Unexpectedly, sub-soil irrigation hardly affected ecosystem respiration (Reco) despite raising summer groundwater tables (GWT) by 6–18 cm, and even up to 50 cm during drought. Only when the groundwater table of sub-soil irrigation sites was substantially higher than the control value (> 20 cm), Reco was significantly lower (p 3 %) between sub-soil irrigation and control sites on an annual base. CO2 fluxes were high at all locations, exceeding 45 t CO2 ha−1a−1, even where peat was covered by clay (25–40 cm). Despite extended drought episodes and lower water levels in 2018, we found lower annual CO2 fluxes than in 2017 indicating drought stress for microbial respiration. Contrary to our expectation, there was no difference between the yearly greenhouse balance of the sub-soil irrigated (64 t CO2–eq ha−1yr−1 in 2017, 53 in 2018) and control sites (61 t CO2–eq ha−1 yr−1 in 2017, 51 in 2018). Emissions of N2O were lower (3 ± 1 t CO2–eq ha−1 yr−1) in 2017 than in 2018 (5 ± 2 t CO2–eq ha−1 yr−1), without treatment effects. The contribution of CH4 to the total GHG budget was negligible (
Beamforming Networks for antenna array, useful inWiFi applications

In this paper several types of beamforming networks have been presented, this proposal can be used for an array antenna, which are used in several types of WiFi applications, IEEE 802.11b/g/draft-n. The first design corresponds to a 4x4 Butler Matrix for a resonance frequency at 2.45 GHz, depending on the port through which the signal is excited, it will be possible to obtain several variations in phase at the output ports (45°, 135°, -45° and -135°), the proposed Butler matrix has been optimized from a generalized matrix scheme, reducing the size through a simple overlap of multiple transmission lines to l/4 at the central frequency. The second design corresponds to a Rotman lens with a characteristic resonance frequency of 5.8 GHz, with a maximum focal angle of 30°, for this design has been used geometric optics concept, and the last design has been made for a frequency of 5.8 GHz using a slot array designed with Substrate Integrated Waveguide technology.
A comparative study of statistical inference from an educational point of view

Inferential statistics is the scientific method for evidence-based knowledge acquisition. The underlying logic is difficult and the mathematical methods created for this purpose are based on advanced concepts of probability, combined with different epistemological positions. Many different approaches have been developed over the years. Following the classical significance tests of Fisher and the statistical tests by Neyman and Pearson, and decision theory, two more approaches are considered here using qualitative scientific argument: the Bayesian approach, which is linked to a contested conception of probability, and the rerandomization and bootstrap strand, which is bound to simulation. While Barnett (1982) analysed statistical inference from a mathematical/philosophical perspective to shed light on the various approaches, we analyse from the grand scenario of statistics education and investigate the relative merits of each approach. Some thoughts are developed to reconsider informal inference, which bases teaching on rerandomization and bootstrap and reduces probability to a frequentist concept. The ideas are designed to initiate a deeper discussion about learning paths towards
TRANSFER LEARNING OF COGNITIVE CONTROL USING MOBILE APPLICATIONS

This research aims to explore the transfer learning of cognitive control skills using mobile applications. We explored whether mobile application can serve as a viable platform in training interference control, specifically the color Stroop task. Mobile phone’s advantage lies in its portability and its ability to reach many people. Forty undergraduates from the authors’ university were involved in the experiment. We introduced the use of a mobile application to present training exercises as well as to record answers and reaction times. Half of the participants were given standard logical thinking questions whereas the other half was given different types of Stroop task as a form of training, followed by the color Stroop test as a test of transference. The results showed that mobile app training using different types of Stroop task significantly improved the performance on the color Stroop Task in reaction time. Future studies can explore transfer learning of other types of cognitive control as well as explore our findings for other age groups.
Analyses of Visualization Methods of the Earth- quake Catalog Mapping for Educational Pur- poses

The aim of the study is to find possible solutions to represent earthquake catalog data and design maps which can help students to identify those places where earthquakes occurred frequently. The goal is to visualize all available catalog data sets in a complex way on a single map, displaying the long-term recurrence times of earthquakes. Therefore, raw data and aggregated data were combined with different cartographic visualization techniques to test the applicability of earthquake maps. Preliminary research demonstrates that aggregation can improve the process of retrieving information from earthquake maps and 3D visualization is useful to define the largest earthquakes. A second result is that 3D visualization is not effective in the comparison of quantities of released energy and the number of earthquakes.
Comparison of Levant differentiator and GPI observer for the position/force control robotic manipulator interacting with rigid surfaces in presence of friction

The problem of hybrid position/force control over rigid surfaces in presence of friction when only joint position and force measurements are available is considered. To achieve position tracking in this scheme it is commonly assumed that the contact force on the robot and the angular velocity are measured. Nevertheless, in some applications it is convenient to remove sensors for a variety of reasons: to reduce costs, the weight of the robot, the size, etc. In this work, a Levant–differentiator approach is used to estimate velocity in order to achieve position tracking for the non–delayed scenario but with force measurement. To achieve this objective, a comparison with a dynamic extension and a highgain observer were employed, which jointly are known in the literature as Generalized Proportional Integral (GPI) observers. The GPI observer allows, besides simultaneous estimation of the robot joint velocity and the contact force over the environment. In other words, the proposed algorithm achieves movement of the robot over the surface and simultaneous application of a force desired, while at the same time it performs an estimation of the velocity and force signals. Another interesting problem is the force control over a known rigid surface, even in the case when position and force are measured. There are some advantages in using the Levant differentiator such as improved tracking position without the knowledge of the robot dynamic model for implementation. Simulation results are presented to illustrate the effectiveness of the proposed approach.
Agrobacterium tumefaciens Mediated Transformation of RNAi CP Gene into Soybean (Glycine max L.)

Soybean mosaic virus(SMV) causes a severe disease in soybean,which can be efficiently prevented by planting resistant cultivars.In order to improve the SMV resistance of soybean,an agrobacterium-tumefaciens mediated gene transformation was conducted in this study by using RNAi soybean mosaic virus coat protein(CP) gene as the expression vector,bar gene as the selective marker gene,and cotyledonary-node as the explant to get RNAi transformation soybean.As results,22 putative transgenic soybean plants were obtained and 18 positive transgenic soybean plants were identified by coating with leaves herbicide,using bar protein quick dip stick and PCR analysis.The segregation ratio of T1 transgenic progeny showed that the transformed gene could be inherited according to the Mendel's law.The T1 southern blot analysis showed that the imported interference fragment was one copy.After SMV friction inoculation,the RNAi CP transgenic soybean plants showed good resistance to SMV.DAS-ELISA analysis at three weeks after SMV inoculation revealed that 100% of the non-transgenic lines SMV while only 7.69% of the RNAi plants were infected by SMV.The results demonstrated that the RNAi CP transgenic soybean plants obtained in the study are valuable resources for improving the SMV resistance of soybean.
A Review of Dispersion Models Generated by Tilting

We review recent results on dispersion models generated by a tilting operator acting on a generating function, similar to exponential dispersion models, which are generated by exponential tilting acting on the cumulant generating function. Other cases include extreme, geometric and factorial dispersion models. These models are important for modelling non-normal response variables in generalized linear regression models, but here we concentrate on their distributional properties. A key ingredient for each type of dispersion model is the associated dispersion function, which is a useful characterization and convergence tool, much like the variance function for exponential dispersion models. In particular, dispersion models corresponding to power dispersion functions appear as scaling or dilation limits, similar to the Tweedie convergence theorem for exponential dispersion models.
Human-like communication in conversational agents: a literature review and research agenda

PurposeConversational agents (chatbots, avatars and robots) are increasingly substituting human employees in service encounters. Their presence offers many potential benefits, but customers are reluctant to engage with them. A possible explanation is that conversational agents do not make optimal use of communicative behaviors that enhance relational outcomes. The purpose of this paper is to identify which human-like communicative behaviors used by conversational agents have positive effects on relational outcomes and which additional behaviors could be investigated in future research.Design/methodology/approachThis paper presents a systematic review of 61 articles that investigated the effects of communicative behaviors used by conversational agents on relational outcomes. A taxonomy is created of all behaviors investigated in these studies, and a research agenda is constructed on the basis of an analysis of their effects and a comparison with the literature on human-to-human service encounters.FindingsThe communicative behaviors can be classified along two dimensions: modality (verbal, nonverbal, appearance) and footing (similarity, responsiveness). Regarding the research agenda, it is noteworthy that some categories of behaviors show mixed results and some behaviors that are effective in human-to-human interactions have not yet been investigated in conversational agents.Practical implicationsBy identifying potentially effective communicative behaviors in conversational agents, this study assists managers in optimizing encounters between conversational agents and customers.Originality/valueThis is the first study that develops a taxonomy of communicative behaviors in conversational agents and uses it to identify avenues for future research.
Growing Like Germany: Local Public Debt, Local Banks, Low Private Investment

Using a firm-bank panel of more than 1m German firms over 2010-2016, we document that local public bank lending to municipalities crowds out private investment. Our results show how crowding-out can happen in a developed economy characterized by low interest rates and fiscal austerity. Our mechanism relies on two structural features of Germany’s banking landscape: First, the geographical segmentation of credit markets for small and medium firms (SME) which are dominated by local banks. Secondly, a special statutory mandate requiring local public banks to lend to municipalities. With yields on local government debt declining to all-time lows, local public banks tried to alleviate stress on their balance sheets by using their local market power to charge higher rates on their SME customers. This crowded out firm investment. Perversely, fiscal consolidation at the state and federal levels contributed to this effect by putting pressure on the budgets of municipal governments which increasingly borrowed from local public banks. Crowding-out lowered aggregate private investment by around 30-40 bio euros per year (or 1 percent of GDP). Thus, we identify a novel channel through which low interest rates can adversely affect bank lending and firm performance. Our results also illustrate how segmented credit markets can amplify negative multiplier effects from fiscal austerity.
Statistical Inference for Integrated Diffusion Processes

We present estimation methods for data that are modelled as integrals of a diffusion process over disjoint time-intervals. An example of an application is realized volatility in financial econometrics, see e.g. Bollerslev & Zhou (2002). Such models are also used in fields of engineering and of the sciences. The analysis of the records of the concentration of oxygen isotopes in ice-core data from Greenland and Antarctica in an example, see e.g. Ditlevsen, Ditlevsen & Andersen (2002). Ice-core data are used to investigate the paleo-climate. Let X be a d-dimensional diffusion process given as the solution to the stochastic differential equation
Stochastic Differential Equations General Models of Individual Growth in Uncertain Environments and Application to Profit Optimization in Livestock Production

Individual animal growth in a randomly varying environment is modeled using stochastic differential equation models. These models are generalizations of the classical deterministic growth models used in regression methods, but incorporate a random dynamical term describing the effects of environmental and other random fluctuations on the growth process. We describe parameter estimation and prediction methods, illustrating with data on cow growth of the Mertolengo breed raised in Alentejo (Portugal) under natural conditions. We first show that these models outperform the traditional regression models in predictive power for they take into account the dynamical nature of the growth process and its interaction with environmental fluctuations. We then apply the models to profit optimization in livestock production, taking into account production costs and sales revenues. Assuming the animal is to be sold when it reaches some prescribed age, we determine the optimal age at which an animal should be sold in order to maximize profit. Another possibility is to sell the animal when it reaches a prescribed size. The first passage time distribution through a prescribed size is studied and used to determine the optimal size at which the animal should be sold. We then determine which policy (selling at a fixed age or selling at a fixed size) is preferable in terms of profit.
Current Scenario of SME sector in Bangladesh: Performance, Problems and Prospects

Nowadays, small and Medium Enterprises (SMEs) have become an essential sector that is supposed to play a significant role in enhancing the economy of Bangladesh. The role of the SME sector can be immense in poverty reduction from the country. However, the SME sector is still facing significant challenges that reduced its potential in developing the economy. Such situations can be tackled by introducing Proper Implementation and Monitoring of Policy Measures, government initiatives and Enhancing Access to SME Finance to accelerate the further development of SMEs. This study mainly aims to assess the present scenario and performance of SME sector in Bangladesh especially in terms of employee turnover rate, quality assurance, allocation of funds, marketing activities, etc. and also identify the problems of SME sector in Bangladesh and suggest recommendations to overcome the problems. This study uses various literature, Bangladesh Bank data for FY 2010-18, and publications from Bangladesh Economic Review 2017, Ministry of Finance, Export Promotion Bureau (EPB), Bangladesh AgroProcessors’ Association (BAPA) and Bangladesh Bureau of statistics.
A Rapid and Easy-handling Procedure for Isolation of DNA from Rice, Arabidopsis and Tobacco

Introduction Genetic studies require the isolation of DNA from a large number of plants. In the process of positional cloning, one needs to analyze DNAs from hundreds of F2 or F3 plants. To map a gene or DNA fragment on the chromosome, it is usually required to analyze DNAs from several dozen plants of the recombinant inbred lines. Not only in genetics but also in molecular biology, a rapid and easy method to isolate DNA is a powerful tool to reveal the genomic organization of a gene of interest. The method using cetyltrimethylammonium bromide (CTAB) [1] has been developed to isolate partially purified plant DNA that contains fewer polysaccharides, which are rich in plant cells and inhibit enzyme reaction such as digestion or ligation of DNA. Although the CTAB method has been applied to various plant species, it is not practical to isolate DNA from a large number of plant materials because it requires many steps and is time-consuming. Moreover, the yield with the CTAB method is sometimes very low when DNAs are extracted from starch storage tissues. We describe here rapid and easily-handled procedures for the isolation of DNA from rice, Arabidopsis and tobacco. These methods do not require careful cautions, which are usually needed in the experiments with CTAB. Particular equipment is not needed. The DNA obtained by these methods can be subjected to enzymatic reactions, such as digestion with restriction enzymes, ligation, and PCR amplification. 1. Isolation of DNA from rice plants Reagents Lysis buffer 400 mM Tris-HCl (pH 8.0), 60 mM EDTA (pH 8. 0), 150 mM NaCI, 10 mM /3-mercaptoethanol (add 14.4 M soln. just before use), 1% polyvinylpyr-
Tolerance eigenmodes of optical systems

We describe an eigenmode analysis technique that can be used to determine how an optical system will react to tolerances. The input stimuli are the tolerances of the system, and the resulting eigenmodes are the aberration patterns that will be “driven” by them. The eigenmodes are orthogonal and include both pupil and field dependencies: field-uniform coma, field-uniform lateral color, and field-linear, field-asymmetric astigmatism are examples. The eigenvalues provide a means of rank-ordering the eigenmodes according to importance when considering a set of alignment compensators that might be included in the system to mitigate the effect of tolerances.
A new dynamic outgassing procedure to predict reemission and deposition values of contaminants

Prediction of the contamination deposits from the Total Mass Loss (TML) via a novel technique is presented. This predictive methodology, based on the multi-step dynamic outgassing test complemented with a thermogravimetric analysis (TGA), allows assigning outgassed species according to their sticking coefficients to a collector's surface. The proposed approach enables to identify major chemical species outgassed from the tested material and calculate sticking coefficients in a broad temperature range (-175 to+80°C). The preliminary results show that this method could be used as an improved procedure for predictions of deposition and reemission levels.
Adaptive Block Coordinate Descent for Distortion Optimization

We present a new algorithm for optimizing geometric energies and computing positively oriented simplicial mappings. Our major improvements over the state‐of‐the‐art are: (i) introduction of new energies for repairing inverted and collapsed simplices; (ii) adaptive partitioning of vertices into coordinate blocks with the blended local‐global strategy for more efficient optimization and (iii) introduction of the displacement norm for improving convergence criteria and for controlling block partitioning. Together these improvements form the basis for the Adaptive Block Coordinate Descent (ABCD) algorithm aimed at robust geometric optimization. ABCD achieves state‐of‐the‐art results in distortion minimization, even under hard positional constraints and highly distorted invalid initializations that contain thousands of collapsed and inverted elements. Starting with an invalid non‐injective initial map, ABCD behaves as a modified block coordinate descent up to the point where the current mapping is cleared of invalid simplices. Then, the algorithm converges rapidly into the chosen iterative solver. Our method is very general, fast‐converging and easily parallelizable. We show over a wide range of 2D and 3D problems that our algorithm is more robust than existing techniques for locally injective mapping.
A Survey of Image Synthesis Methods for Visual Machine Learning

Image synthesis designed for machine learning applications provides the means to efficiently generate large quantities of training data while controlling the generation process to provide the best distribution and content variety. With the demands of deep learning applications, synthetic data have the potential of becoming a vital component in the training pipeline. Over the last decade, a wide variety of training data generation methods has been demonstrated. The potential of future development calls to bring these together for comparison and categorization. This survey provides a comprehensive list of the existing image synthesis methods for visual machine learning. These are categorized in the context of image generation, using a taxonomy based on modelling and rendering, while a classification is also made concerning the computer vision applications they are used. We focus on the computer graphics aspects of the methods, to promote future image generation for machine learning. Finally, each method is assessed in terms of quality and reported performance, providing a hint on its expected learning potential. The report serves as a comprehensive reference, targeting both groups of the applications and data development sides. A list of all methods and papers reviewed herein can be found at https://computergraphics.on. liu.se/image_synthesis_methods_for_visual_machine_learning/.