Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Behavior of U.S. Farms to Climate and Risk

We analyze the effects of climate conditions and crop insurance on farm-level land allocation decisions among corn, soybeans, winter wheat, and hay in 10 Midwest states. Based on ARMS data, we estimate farmers' land allocation equations that control for market conditions, climate and soil variables, and insurance. A multivariate sample selection model is used for estimation. We find that: 1) beneficial heat has positive effects on corn and soybean acreage but negative effects on winter wheat acreage, 2) excessive heat has negative effects on corn and winter wheat acreage but have positive effects on soybean acreage, 3) an increase in precipitation by 1% increases corn acreage by 0.6% but decrease soybean and winter wheat acreage by 1.0% and 1.6%, 4) soybean acreage is more sensitive to summer drought, and 5) crop insurance alters farmers land allocation.
Effect Of Behavioural Family Therapy On Behaviour Correction And Recidivism Rate Of Delinquents In Ahvaz, Iran

Behavioural family therapy was successfully applied in the treatment of delinquency with age and socio economic status being the mediating variables. The sample, n=124 from Ahvaz Correction Centre comprised of three subgroups involved with stealing, wickedness and sexual crimes. The study assessed the treatment effects on the subjects' school adaptation, drug abuse tendency and recidivism rate. Results indicated a significant difference between the experimental and control groups among the three variables. Nevertheless, all the subgroups were affected by the intervention regardless of the type of the crime. Keywords-delinquents; behavioural family therapy; school adaptation; drug abuse; recidivism
Quasi-local Energy in 3D Gravity with Torsion

We show that for generic stationary spacetime and a series of Killing fields, Wald's approach for quasi-local energy could always be generalized to the first order formalism straightforwardly without introducing the Lorentz-Lie derivative. Via this approach, we derive the general formula for the black hole entropy in the three dimensional torsional Mielke-Baekler gravity, reproducing correctly the total energy, the angular momentum as well as the black hole entropy for the BTZ-like solutions.
How far India has gone down the road towards financial integration with US since subprime crisis? An Econometric Analysis

The present paper aims to study the causal relationship between the US and Indian equity markets using Johansen’s cointegration and variance decomposition analyses. Since the opening up of the economy and subsequent economic and political reforms, India has made tremendous strides in the global equity markets and also been impinged on by the recent happenings. Eviews 7 package program has been used for arranging the data and conducting econometric analyses. The ADF test shows that the time series data used for the study are stationary and integrated of order one. The Johansen’s co-integration test reveals that there exists long run equilibrium relation between the selected variables. The Granger causality test in the vector error correction model suggests the evidence of feedback causality running between the six stock exchanges. However, there is no dependence of any of the individual exchange over the other.
THE EXTRAORDINARY GAMMA-RAY FLARE OF THE BLAZAR 3C 454.3

We present the gamma-ray data of the extraordinary flaring activity above 100 MeV from the flat spectrum radio quasar 3C 454.3 detected by AGILE during the month of 2009 December. 3C 454.3, which has been among the most active blazars of the FSRQ type since 2007, has been detected in the gamma-ray range with a progressively rising flux since 2009 November 10. The gamma-ray flux reached a value comparable with that of the Vela pulsar on 2009 December 2. Remarkably, between 2009 December 2 and 3, the source more than doubled its gamma-ray emission and became the brightest gamma-ray source in the sky with a peak flux of Fγ,p = (2000 ± 400) × 10−8 ph cm−2 s−1 for a 1 day integration above 100 MeV. The gamma-ray intensity decreased in the following days with the source flux remaining at large values near Fγ ≃ (1000 ± 200) × 10−8 ph cm−2 s−1 for more than a week. This exceptional gamma-ray flare dissipated among the largest ever detected intrinsic radiated power in gamma-rays above 100 MeV (Lγ,source,peak ≃ 3 × 1046 erg s−1, for a relativistic Doppler factor of δ ≃ 30). The total isotropic irradiated energy of the month-long episode in the range 100 MeV–3 GeV is Eγ,iso ≃ 1056 erg. We report the intensity and spectral evolution of the gamma-ray emission across the flaring episode. We briefly discuss the important theoretical implications of our detection.
Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time.
On the hook for impaired bank lending: Do sovereign-bank inter-linkages affect the fiscal multiplier?

Recently, some notable contributions suggest, that discretionary fiscal policy can be an effective and self- financing policy option in the presence of extreme macroeconomic conditions. Given the special relationship between the Irish sovereign and its main financial institutions, this paper assesses the implications for the Irish fiscal accounts of certain macroeconomic policy responses. Using a comprehensive empirical framework, the paper examines the relationship between house prices, unemployment and mortgage arrears. Loan loss forecasts over the period 2012-2014 are then generated for the mortgage book of the main Irish financial institutions under two different scenarios. It is shown that macroeconomic policies, which alleviate levels of mortgage distress, improve the solvency position of the guaranteed Irish institutions thereby reducing the sovereign’s future capital obligations. Thus, the unique situation the sovereign finds itself in vis-a-vis its main financial institutions, may have significant implications for the fiscal multiplier.
The Importance of Increasing Returns to Scale in the Process of Agglomeration in Portugal: A Non-linear Empirical Analysis

With this work we try to analyse the agglomeration process in the Portuguese regions, using the New Economic Geography models. In these models the base idea is that where has increasing returns to scale in the manufactured industry and low transport costs, there is agglomeration. Of referring, as summary conclusion, that with this work the existence of increasing returns to scale and low transport cost, in the Portuguese regions, was proven and as such the existence of agglomeration in Portugal.
Investigating the causal relationship between economic growth, inflation, and unemployment in South Korea

Policymakers and economists consistently implement monetary and fiscal policy to control economic growth, inflation, and unemployment due to the fact that these three factors directly influence people’s living standards. Every country has a different economic characteristic structure. Economic growth and inflation have a strong correlation in some countries, while other countries have a strong correlation between economic growth and unemployment. Therefore, investigating the causal relationship among economic factors can provide us with a better understanding of how economic phenomena affect each other. In South Korea, economic growth, inflation, unemployment have been in balance since the 1998 Korean economic crisis. Hence, investigating the economic growth, inflation, and unemployment of South Korea will enlighten how these three economic indicators affect each other in a country that developed rapidly and had several economic crises. To investigate such a model, the Vector Autoregressive Model (VAR) is used with the data between the years 1980 and 2019 in order to verify whether Okun’s law or/and the Philips curve hold in South Korea. The research also determines if there is either a bi-directional or uni-directional relationship if economic growth, inflation, and unemployment have a causal relationship. The research demonstrates that GDP is the main factor in South Korea that influences the other economic factors. This research paper can contribute to academia, since it has a vital outcome which shows that the mobility of the unemployment rate in South Korea is directly correlated to the movement of GDP.
Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

In order to improve their power efﬁciency and computational capacity, modern servers are adopting hardware accelerators, especially GPUs. Modern analytical DMBS engines have been highly optimized for multi-core multi-CPU query execution, but lack the necessary abstractions to support concurrent hardware-conscious query execution over multiple heterogeneous devices and take full advantage of the available accelerators. In this work, we present a Heterogeneity-conscious Analytical query Processing Engine (HAPE), a hardware-conscious analytical engines that targets efﬁcient concurrent multi-CPU multi-GPU query execution. HAPE decomposes heterogeneous query execution into: 1) efﬁcient single-device and 2) concurrent multi-device query execution. It uses hardware-conscious algorithms designed for single-device execution and combines them into efﬁcient intra-device hardware-conscious execution modules, via code generation. HAPE combines these modules to achieve concurrent multi-device execution by handling data and control transfers. We validate our design by building a prototype and evaluate its performance using radix-join co-processing and the TPC-H benchmark [11]. We show that it achieves up to 10x and 3.5x speed-up on the join against CPU and GPU alternatives and 1.6x-8x against state-of-the-art CPU-and GPU-based commercial DBMS on the queries.
K-Means, Mean Shift, and SLIC Clustering Algorithms: A Comparison of Performance in Color-based Skin Segmentation

Commonly used in computer vision, segmentation is grouping pixels into meaningful or perceptually similar regions. In this work, we are going to evaluate the performance of three popular data-clustering algorithms, the K-means, mean shift and SLIC algorithms, in the segmentation of human skin based on color. The K-means algorithm Iteratively aims to group data samples into K clusters, where each sample belongs to the cluster with the nearest mean. The mean shift algorithm is a non- parametric algorithm that clusters data iteratively by finding the densest regions (clusters) in a feature space. An enhanced version of the classic K-means algorithm, the SLIC limits the search region to a small area around the cluster reducing the algorithm complexity to be only dependent on the number of pixels in the image. It also provides control over the compactness of the clusters. Color-based skin segmentation algorithms depend on both a color space at which segmentation is performed and a classification method used to determine whether a pixel is skin or non-skin. We have implemented the K-means, mean shift and SLIC algorithms in the RGB color space to detect human skin. Our method begins by clustering images using these algorithms and then segmenting the clustered regions occupied by skin. Pixels in the clusters are classified as skin or non-skin using the Kovac model. We have evaluated the algorithms' performance on the SFA database (controlled environ- ment) and on another database created for testing on an uncontrolled environment. The performance has been evaluated using time complexity, F1 score, recall, and precision. We have found that on average the mean shift algorithm triumphs over the three algorithms in terms of performance while the SLIC algorithms holds an advantage being the fastest.The K-means algorithm has a good performance when the number of clusters K is between 10 and 15, whereas the mean shift algorithm has good performance when the bandwidth h is between 0.03 and 0.06. The SLIC algorithm maxes out its performance at around k = 100 and the number of clusters can be increased to K = 300 without remarkably increasing the complexity.
Is there an impact of labor market freedom on the elderly female labor force participation rate in the U.S.? An exploratory study

This exploratory empirical study investigates the hypothesis that greater labor market freedom should elevate the female labor force participation rate of women age 65 years and older. Strong and consistent empirical support for this hypothesis is provided in this study. For example, a one unit increase in the labor market freedom index appears to induce a 5.3%-6.88% increase in the female labor force participation rate of women age 65 years and older.
Machine Vision System for Industrial Parts Recognition

These days in different kinds of product manufacturing industries (e.g. locks, tools containers, alarm clocks, engines and motors and many others.) and packaging industries with huge scale production devices automatic assembly structures are broadly used. They used mechanized devices including image taking unit, conveyor, element recognition unit, component feeder, element selection unit, and smart robots that observe constant sequence of steps to gather the product. The performance of general product production and inspection method can be elevated with commercial automation and it also minimizes resources and saves time. This paper affords a computationally efficient 3-D computer vision based approach to apprehend the machine CAD parts. In this paper features based totally commercial object detection techniques are applied in MATLAB to recognize the presence of the commercial CAD parts inside the question image. In the end the actual industrial tool images also are used to expose the accuracy and robustness of the proposed smart machine vision system for industrial production automation.
EVALUATION OF IN-VITRO ANTI-INFLAMMATORY ACTIVITY OF HERBAL PREPARATION , A COMBINATION OF FOUR MEDICINAL PLANTS

Inflammation is initiated as a healing process by the tissue in response to an injury by pathogens, irritants or cell damage. Anti-inflammatory drugs do have their side-effects. The focus of research studies is upon the use of medicinal plants as anti-inflammatory agents over the past years. In the present study, 80% alcoholic extract of leaves of Aloe vera, Bacopa monnieri, Moringa oleifera and rhizome of Zingiber officinale was used to prepare a Herbal Preparation (HP-4 of 100 mg /10ml methanol concentration). Different concentrations ofHP-4 was used to study the in-vitro anti-inflammatory activities in terms of effect of hypotonic solution –induced haemolysis on RBC membrane stabilization and effect of inhibition of protein denaturation activity . Acetylsalicylic acid was used as reference drug which showed comparable anti-inflammatory activity as compared to control sample in which no drug was used. It was found that HP-4 has dose dependent RBC membrane stabilization and inhibition of protein denaturation activity.
A New Assessment of Hydrological Change in the Source Region of the Yellow River

Hydrological responses to climate change are a widely concerning question, particularly for the source region of the Yellow River (SRYR), which is sensitive to climate change and is widely underlain by frozen ground. In considering climate change impacts on catchment properties, the traditional separation approach based on the Budyko framework was modified to identify and quantify the climatic causes of discharge changes. On the basis of the decomposition method, the traditional separation method and the modified separation method were used to analyse the discharge change in the SRYR. Using the observed annual maximum frozen depth (MFD) to indicate the frozen ground level, the impacts of frozen-ground degradation on the discharge change were further considered using the modified separation method. Our results show that the traditional separation approach underestimated climate-induced discharge change; over the past half-century, the discharge change in the SRYR has been primarily controlled by climate change. Increasing air temperature is generally a negative force on discharge generation; however, it also causes frozen ground to degrade—a positive factor for discharge generation. Such conflicting effects enhance the uncertainty in assessments of hydrological responses to climate change in the sub-basins with widely distributed permafrost.
Running Head: BUFFERING EGO-DEPLETION 1 PREVENTING AND REPAIRING EGO DEPLETION THROUGH HUMOR by DAVID WELDEN

1 PREVENTING AND REPAIRING EGO DEPLETION THROUGH HUMOR by DAVID WELDEN (Under the Direction of Janie Wilson) ABSTRACT The theory of ego depletion views the capacity for self-control as a finite internal resource that declines with continual use. As the levels of this resource decline, performance within a host of domains is impaired, resulting in negative behavioral outcomes. Previous studies have sought to find ways to address the decline in the self-control resource and the accompanying reduction in performance in a variety of different ways. One method that has been found to be effective in reducing ego depletion is the use of humor, specifically the viewing of humorous videos following an ego-depleting task. However, prevention of ego depletion using humor prior to an ego-depleting task has not been examined. In the current study, a humorous video was used in an attempt to reduce the effects of ego depletion relative to a control condition viewing a neutral video. The video was seen either before the ego-depletion task (prevention) or after the egodepletion task (reparation). It was expected that the humorous video would buffer against ego depletion when shown regardless of whether participants viewed the video before or after the ego-depletion task. Although the humorous video did not alter ego depletion, mood was enhanced relative to the neutral-video condition.
Application of logistic regression model for predicting the association of climate change resilient cultural practices with early blight of tomato (Alternaria solani) epidemics in the East Shewa, Central Ethiopia

ABSTRACT A field survey was conducted to determine the distribution and importance of early blight of tomato in East Shewa, Central Ethiopia. A total of 140 tomato fields were inspected in 4 districts (Adama, Lome, Dugda, and Bora). The associations of early blight incidence and severity with independent variables were evaluated. Disease incidence was found higher in Dugda (72.19%) and Bora (62.28%) districts. The highest mean disease severity was 31.39% in Dugda and 26.09% in Bora district, while the lowest was recorded in Adama (14.71%) district. In reduced multiple variable models, early blight percentage severity index >25% showed a high probability of association with all parameters. Logistic regression analyses of disease severity revealed that sometraits were found the most significant variables. Overall, proper weed management practices, crop rotation with non-solanaceous hosts, and other related farm practices should be carried out to reduce the impact of early blight on tomatoes.
Biochemical and Molecular Roles of Nutrients Dietary Iron Intake Rapidly Influences Iron Regulatory Proteins , Ferritin Subunits and Mitochondrial Aconitase in Rat Liver 1 , 2 , 3

Iron regulatory protein 1 (IRP1) and IRP2 are cytoplasmic RNA binding proteins that are central regulators of mammalian iron homeostasis. We investigated the time-dependent effect of dietary iron deficiency on liver IRP activity in relation to the abundance of ferritin and the iron-sulfur protein mitochondrial aconitase (macon), which are targets of IRP action. Rats were fed a diet containing 2 or 34 mg iron/kg diet for 1–28 d. Liver IRP activity increased rapidly in rats fed the iron-deficient diet with IRP1 stimulated by d 1 and IRP2 by d 2. The maximal activation of IRP2 was five-fold (d 7) and three-fold (d 4) for IRP1. By d 4, liver ferritin subunits were undetectable and m-acon abundance eventually fell by 50% (P õ 0.05) in iron-deficient rats. m-Acon abundance declined most rapidly from d 1 to 11 and in a manner that was suggestive of a cause and effect type of relationship between IRP activity and m-acon abundance. In liver, iron deficiency did not decrease the activity of cytosolic aconitase, catalase or complex I of the electron transport chain nor was there an effect on the maximal rate of mitochondrial oxygen consumption with the use of malate and pyruvate as substrates. Thus, the decline in macon abundance in iron deficiency is not reflective of a global decrease in liver iron-sulfur proteins nor does it appear to limit ATP production. Our results suggest a novel role for m-acon in cellular iron metabolism. We conclude that, in liver, iron deficiency preferentially affects the activities of IRPs and the targets of IRP action. J. Nutr. 128: 525–535, 1998.
Learning Variable Impedance Control: A Model-Based Approach Using Gaussian Processes

Modern robotic systems are increasingly expected to interact with unstructured and unpredictable environments. This has reiterated the importance of sophisticated reasoning and adaptive motor skill learning. Although low-level methodologies for sensorimotor control have been relatively well studied, constrained motion for robotic manipulators in general environments still remains a challenge. In addition to viable kinematic trajectories, successful interaction requires a system to adjust the magnitude and direction of the force applied on an object or human being. One force control architecture that is stable under a wide range of conditions is impedance control. However the defining inertial, damping and stiffness parameters are highly task dependent and often difficult to deduce a priori. To this end, one promising strategy is through reinforcement learning. Several frameworks have already emerged that are capable of learning compliant behaviour in this fashion. However the complex and sometimes discontinuous nature of physical interaction in robotics provides additional challenges in designing algorithms capable of learning complex behaviours with minimal interaction time. This thesis details an extension to the PILCO algorithm for learning variable impedance control. The proposed method attempts to construct a Gaussian Process model of the robot-environment system through interaction. This approach permits long-term inference and planning in a fully Bayesian manner, reducing the required interaction time for convergence and allow for efficient analytical gradient-based policy updates. Two skill learning problems are investigated both in simulation and experiment on a 7-DOF `KUKA LBR iiwa 7 R800' robot. The first scenario entails learning state-conditioned variable stiffness parameters along predefined motion plans. In the second framework, the agent learns both stiffness parameters and kinematic trajectories simultaneously to complete a constrained motion task.