Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing a Knowledge Test to Measure the Knowledge Level of Farmers about Climate Change in Arid Ecosystem of India

Climate change has brought widespread misery and economic loss to farming community of India, adversely affecting agriculture, public health, food security, biodiversity and water resources. Climate change is a very complex issue and most of the farmers did not have any clear cut understanding about it. So it is very important to measure the knowledge level of farmers for successful adaptation. The present study is aimed to construct a knowledge test to measure the knowledge level of farmers in India. One pilot study was conducted to develop the test in Lechura village of Uttar Pradesh. The test was developed with reliability score of 0.709 and a sufficient degree of validity. The final study was conducted in Jodhpur and Jaisalmer district of Rajasthan. The study revealed that only 34% farmers knew about climate change and only 31% farmers knew that paddy cultivation also leads to emission of green house gasses. Cumulatively 45% respondents come under low level of knowledge category on climate change. The correlation analysis revealed that different social variables like education (0.800*), perception (0.638*), social participation (0.471*) and psychological variable like attitude (0.815*), value (0.820) and awareness (0.743*) were correlated positively with knowledge score of the respondents. However age (−0.257*), income (−0.003), area (−0.006), pessimism (−0.621*) and stress (−0.451) were correlated negatively. So policy maker should take into consideration these social and psychological dimensions of farmers’ behavior for successful implementation of any adaptation strategy to climate change.
Hydrophilic oxygen radical absorbance capacity values of low-molecular-weight phenolic compounds containing carbon, hydrogen, and oxygen

The antioxidant capacity of an antioxidant reflects its ability to remove reactive oxygen species (ROS). In this study, the hydrophilic oxygen radical absorbance capacity (H-ORAC) method was used to quantitatively evaluate the antioxidant capacities of natural phenols and their derivatives against peroxyl radicals. This method was comprehensively applied to low-molecular-weight phenols to construct a database. Although no macroscopic correlation was observed for values related to the antioxidant capacity expression, we observed a difference in the trend of the H-ORAC values for each functional group. Thus, this database will serve as a new benchmark and tool for molecular design.
Why Growth in Emerging Economies Is Likely to Fall

Emerging-market growth from 2000 to 2012 was extraordinarily high. Aslund cites several factors to explain why emerging-economy growth is likely to be lower in the future. Having caught up with advanced economies in many respects, these countries face limitations on their future catch-up potential. The extraordinary credit and commodity booms are over, and many large emerging economies are financially fragile. They have governance problems and need to carry out structural reforms. The advanced economies, by contrast, have undertaken fiscal consolidation and structural reforms following the recent financial crisis and should experience higher growth rates.
The Personal Globe Inventory: The structure of vocational interest in Vietnam

The Personal Globe Inventory (PGI) proposes a unified model of vocational interests, integrating Holland’s established interest dimensions, with occupational prestige dimensions. The current study evaluates the structural validity of the PGI short form in Vietnam (N =1140). The results of the randomized tests of hypothetical order relations support the inclusion of prestige in the PGI spherical model, as a means to better calibrate the assessment of vocational interest across cultural contexts, specifically to Vietnam. Furthermore the results endorse the application of the abbreviated form of the PGI in a career counseling context as a time-efficient, screening method.
Modified Histone Peptides Linked to Magnetic Beads Reduce Binding Specificity

Histone post-translational modifications are small chemical changes to the histone protein structure that have cascading effects on diverse cellular functions. Detecting histone modifications and characterizing their binding partners are critical steps in understanding chromatin biochemistry and have been accessed using common reagents such as antibodies, recombinant assays, and FRET-based systems. High-throughput platforms could accelerate work in this field, and also could be used to engineer de novo histone affinity reagents; yet, published studies on their use with histones have been noticeably sparse. Here, we describe specific experimental conditions that affect binding specificities of post-translationally modified histones in classic protein engineering platforms and likely explain the relative difficulty with histone targets in these platforms. We also show that manipulating avidity of binding interactions may improve specificity of binding.
Gait regulation control techniques for robust legged locomotion

This thesis develops methods of control that allow a multi-legged robot to vary its stepping pattern, the gait of a robot, during locomotion. By constructing feedback control around the gaits a robot may use, we produce behaviors allowing a robot to switch amongst or return to certain gaits while performing feedback control during locomotion. Gait regulation is one specific aspect of gait-based control, and pertains to the use of a control system to monitor and regulate the desired gaits a robot may use. While some gait-based control laws may force a robot to deviate from a nominal gait, gait regulation seeks to return to—or switch amongst—desired gaits as required. After discussing the necessary topological effects of gait regulation control, as well as noting specific constraints that are unique to legged systems, this thesis proposes methods of gait regulation control that place attractors and repellors on a high-dimensional toroidal space, a space relevant to gait timings, in order to converge upon desired gaits. The primary contribution of this thesis is an efficient algorithmic approach to gait regulation that avoids dangerous leg timings while converging to desired gaits, as specified. The system actively manages the basins of convergence for various controllers to achieve a global vector policy directing a robot to certain desired gaits. This work is particularly applicable to fourand six-legged robots, on which a variety of interesting and useful gait timings exist. Specifically, we apply gait regulation to a climbing hexapod, on which we design a climbing behavior based upon a collection of reactive force control techniques, causing the robot to deviate from its desired gait. With gait regulation, the robot maintains use of its desired gaits, with the additional ability to actively transition amongst gaits while climbing. c © 2008 Galen Clark Haynes i
Chemical Bonding Origin of Glass Formation in Metal-Organic Frameworks

Glass-forming metal organic frameworks (MOFs) have novel applications, but the origin of their peculiar melting behavior is unclear. Here, we report synchrotron X-ray diffraction electron densities of two zeolitic imidazolate frameworks (ZIFs), the glass-forming Zn-ZIF-zni and the isostructural thermally decomposing Co-ZIF-zni. Electron density analysis shows that the Zn‒N bonds are more ionic than Co‒N bond, which have distinct covalent features. Variable temperature Raman spectra reveal onset of significant imidazolate bond weakening in Co-ZIF-zni above 673 K. Melting can be controlled by tuning the metal–ligand and imidazole bonding strength as shown from nine solid solution structures of CoxZn1-x-ZIF-zni (x = 0.3 to 0.003) were synthesized, and a mere 4% Co-doping into Zn-ZIF-zni results in thermal decomposition instead of melting. These findings advocate significant chemical space for de-signing new MOF glasses either by engineering the chemical bonding with ligand functionalization or by avoiding defects at the metal site in the crystal lattice.
Cocaine-Induced Changes in Tonic Dopamine Concentrations Measured Using Multiple-Cyclic Square Wave Voltammetry in vivo

For over 40 years, in vivo microdialysis techniques have been at the forefront in measuring the effects of illicit substances on brain tonic extracellular levels of dopamine that underlie many aspects of drug addiction. However, the size of microdialysis probes and sampling rate may limit this technique’s ability to provide an accurate assessment of drug effects in microneural environments. A novel electrochemical method known as multiple-cyclic square wave voltammetry (M-CSWV), was recently developed to measure second-to-second changes in tonic dopamine levels at microelectrodes, providing spatiotemporal resolution superior to microdialysis. Here, we utilized M-CSWV and fast-scan cyclic voltammetry (FSCV) to measure changes in tonic or phasic dopamine release in the nucleus accumbens core (NAcc) after acute cocaine administration. Carbon-fiber microelectrodes (CFM) and stimulating electrodes were implanted into the NAcc and medial forebrain bundle (MFB) of urethane anesthetized (1.5 g/kg i.p.) Sprague-Dawley rats, respectively. Using FSCV, depths of each electrode were optimized by determining maximal MFB electrical stimulation-evoked phasic dopamine release. Changes in phasic responses were measured after a single dose of intravenous saline or cocaine hydrochloride (3 mg/kg; n = 4). In a separate group, changes in tonic dopamine levels were measured using M-CSWV after intravenous saline and after cocaine hydrochloride (3 mg/kg; n = 5). Both the phasic and tonic dopamine responses in the NAcc were augmented by the injection of cocaine compared to saline control. The phasic and tonic levels changed by approximately x2.4 and x1.9, respectively. These increases were largely consistent with previous studies using FSCV and microdialysis. However, the minimal disruption/disturbance of neuronal tissue by the CFM may explain why the baseline tonic dopamine values (134 ± 32 nM) measured by M-CSWV were found to be 10-fold higher when compared to conventional microdialysis. In this study, we demonstrated phasic dopamine dynamics in the NAcc with acute cocaine administration. M-CSWV was able to record rapid changes in tonic levels of dopamine, which cannot be achieved with other current voltammetric techniques. Taken together, M-CSWV has the potential to provide an unprecedented level of physiologic insight into dopamine signaling, both in vitro and in vivo, which will significantly enhance our understanding of neurochemical mechanisms underlying psychiatric conditions.
Ransomware Family Classification With Ensemble Model Based On Behavior Analysis

Ransomware is one of the most dangerous types of malware, which is frequently intended to spread through a network to damage the designated client by encrypting the client’s vulnerable data. Conventional signature-based ransomware detection technique falls behind because it can only detect known anomalies. When it comes to new and non-familiar ransomware traditional system unveils huge shortcomings. For detecting unknown patterns and sorts of new ransomware families,behavior-based anomaly detection approaches are likely to be the most efficient approach. In the wake of this alarming condition, this paper presents an ensemble classification model consisting of three widely used machine learning techniques that include Decision Tree (DT), RandomForest (RF), and K-nearest neighbor (KNN). To achieve the best outcome ensemble soft voting and hard voting techniques are used while classifying ransomware families based on attack attributes. Performance analysis is done by comparing our proposed ensemble models with standalone models on behavioral attributes based ransomware dataset.
DATA REQUIREMENTS FOR LONG-TERM MONITORING OF PAVEMENTS AS A BASIS FOR DEVELOPMENT OF MULTIPLE REGRESSION RELATIONS

A discussion is presented concerning how data from a broad, long-term pavement monitoring study can be used to provide improved models for predicting damage to pavements, maintenance requirements, costs, etc., as functions of significant variables defining the pavement structure, its environment, and the traffic loadings imposed on it. Specific items of data are recommended for collection, and sampling techniques and sample sizes are discussed. Special considerations related to pilot studies or other studies of limited size are discussed, and specific recommendations are offered. Results are presented for a study that had the following goals: (a) to identify distress types that either generate maintenance or cause loss of performance or safety; (b) to identify environmental and traffic factors, material and geometric properties, and other properties and conditions of a pavement structure that significantly affect performance and distress; (c) to describe multiple regression techniques for developing empirical relations and the characteristics of the data banks required for successful models; (d) to develop recommended approaches to establishing sample sizes for long-term pavement monitoring studies; and (e) to develop recommendations for a planned pilot study. (Author)
A Coupled Surface-Water and Ground-Water Model

In areas with dynamic and hydraulically well connected ground-water and surface-water systems, it is desirable that stream-aquifer interaction be simulated with models of equal sophistication and accuracy. Accordingly, a new, coupled ground-water and surface-water model was developed by combining the U.S. Geological Survey models MODFLOW and BRANCH. MODFLOW is the widely used modular three-dimensional, finite-difference, ground-water model and BRANCH is a one-dimensional numerical model commonly used to simulate flow in open-channel networks. Because time steps used in ground-water modeling commonly are much longer than those used in surface-water simulations, provision has been made for handling multiple BRANCH time steps within one MODFLOW time step. Verification testing of the coupled model was done using data from previous studies and by comparing results with output from a simpler four-point implicit open-channel flow model linked with MODFLOW.
Short Text Topic Learning Using Heterogeneous Information Network

With the explosive growth of short texts on users’ interests and preferences, learning discriminative and coherent latent topics from short texts is a critical and significative work, since many practical applications, such as e-commerce and recommendations, require semantic understandings that short texts convey explicitly and implicitly. However, existing short text topic learning methods face the challenge of fully capturing semantically related co-occurrence phrases. Therefore, this paper proposes a novel <underline>H</underline>eterogeneous <underline>I</underline>nformation <underline>N</underline>etwork-based <underline>Sho</underline>rt <underline>T</underline>ext <underline>T</underline>opic learning approach (HIN-ShoTT) in terms of parts of speech, without depending on any auxiliary information. Specifically, HIN-ShoTT can be decomposed into three phases: <inline-formula><tex-math notation="LaTeX">${{i}}$</tex-math><alternatives><mml:math><mml:mi>i</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-3147766.gif"/></alternatives></inline-formula>) seeking semantic relations among words with different parts of speech, where HIN-ShoTT models multiple explicit and implicit semantic relations among words based on a Heterogeneous Information Network (HIN) in terms of parts of speech; <inline-formula><tex-math notation="LaTeX">${{ii}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>i</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq2-3147766.gif"/></alternatives></inline-formula>) extracting co-occurrence phrases and filtering noises, where HIN-ShoTT defines parts-of-speech meta structures to guide co-occurrence phrase extraction and a self-adapting threshold filtering module is proposed for discarding noises; and <inline-formula><tex-math notation="LaTeX">${{iii}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>i</mml:mi><mml:mi>i</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq3-3147766.gif"/></alternatives></inline-formula>) inferring topics, where HIN-ShoTT directly models the generative process of co-occurrence phrases to make topic learning effective with the abundant corpus-level information. Our experimental results on three real-world datasets not only show that HIN-ShoTT performs well, but also demonstrate that it is feasible to incorporate HIN into short text topic learning for accuracy improvement.
Development of a Prognostic Five-Gene Signature for Diffuse Lower-Grade Glioma Patients

Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein-coding genes (PCGs) is widely used as a tumor marker. This study aimed to construct a multi-PCG signature to predict survival for LGG patients. Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis, and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature. Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with a significantly different overall survival (log rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found that PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways. Conclusions: The new five-PCG signature is a reliable prognostic marker for LGG patients and has a good prospect in clinical application.
Modeling and Targeting Neuroglial Interactions with Human Pluripotent Stem Cell Models

Generation of relevant and robust models for neurological disorders is of main importance for both target identification and drug discovery. The non-cell autonomous effects of glial cells on neurons have been described in a broad range of neurodegenerative and neurodevelopmental disorders, pointing to neuroglial interactions as novel alternative targets for therapeutics development. Interestingly, the recent breakthrough discovery of human induced pluripotent stem cells (hiPSCs) has opened a new road for studying neurological and neurodevelopmental disorders “in a dish”. Here, we provide an overview of the generation and modeling of both neuronal and glial cells from human iPSCs and a brief synthesis of recent work investigating neuroglial interactions using hiPSCs in a pathophysiological context.
Influence of ear canal occlusion and air-conduction feedback on speech production in noise

Millions of workers are exposed to high noise levels on a daily basis. The primary concern for these individuals is the prevention of noise-induced hearing loss, which is typically accomplished by wearing of some type of personal hearing protector. However, many workers complain they cannot adequately hear their co-workers when hearing protectors are worn. There are many aspects related to fully understanding verbal communication between noise-exposed workers that are wearing hearing protection. One topic that has received limited attention is the overall voice level a person uses to communicate in a noisy environment. Quantifying this component provides a starting point for understanding how communication may be improved in such situations. While blocking out external sounds, hearing protectors also induce changes in the wearer’s self-perception of his/her own voice, which is known as the occlusion effect. The occlusion effect and attenuation provided by hearing protectors generally produce opposite effects on that individual’s vocal output. A controlled laboratory study was devised to systematically examine the effect on a talker’s voice level caused by wearing a hearing protector and while being subjected to high noise levels. To test whether differences between occluded and unoccluded vocal characteristics are due solely to the occlusion effect, speech produced while subjects’ ear canals were occluded was measured without the subject effectively receiving any attenuation from the hearing protectors. To test whether vocal output differences are due to the reduction in the talker’s self-perceived voice level, the amount of occlusion was held constant while varying the effective hearing protector attenuation. Results show the occlusion effect, hearing protector attenuation, and ambient noise level all to have an effect on the talker’s voice output level, and all three must be known to fully understand and/or predict the effect in a particular situation. The results of this study may be used to begin an effort to quantify metrics in addition to the basic noise reduction rating that may be used to evaluate a hearing protector’s practical usability/wearability. By developing such performance metrics, workers will have information to make informed decisions about which hearing protector they should use for their particular work environment.