Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Effects of Metformin and Glibenclamide on Aortic Reactivity to Vasodilator and Vasoconstrictor Agents in STZ-Induced Diabetic Rats

Background: Diabetes is an important risk factor for cardiovascular events. Endothelial dysfunction is the main reason cause of disability and death in diabetic patients and death. The present study investigates the effects of metformin and glibenclamide on vasoconstrictive and vasodilative responses in the diabetic rat aorta. Methods: Rats were divided into four experimental groups (control, STZ-diabetic, metformin and glibenclamide treated diabetic rats). Treated rats received metformin (300 mg/kg) or glibenclamide (5 mg/kg) daily by gavage for 6 weeks. Thoracic aortic rings were mounted in an organ bath system, then contractile and dilatation responses induced by acetylcholine (ACh), phenylephrine (PE), potassium chloride (KCl) and sodium nitroprusside (SNP) were evaluated in different situations. Results: Blood glucose level in glibenclamide group at in days 24 and 45 was were significantly lower than diabetic group. Metformin and glibenclamide significantly reduced the contractile responses to higher concentrations of PE (10-6 - 10-5 M) compared to diabetic group. The mMetformin and glibenclamide significantly reduced the contractile responses to concentrations of KCl (50 and 60 mM) compared to diabetic group. The relaxation responses to Ach 10-8 M, was increased in metformin and glibenclamide groups than compared to the diabetic group. The relaxation responses to Ach 10-7 - 10-5 M were significantly higher in both treated groups compared to diabetic group. Conclusion: The chronic administration of metformin or glibenclamide has a significant hypoglycemic effect and improves aortic reactivity to vasoconstrictor and vasodilator agents in STZ-induced diabetic rats. No significant difference was found regarding thein effects of metformin and glibenclamide on vasoconstrictive and vasodilative responses in aorta.
UNEMPLOYMENT INSURANCE TAXES

States raise tax rates on firms that lay off workers, a practice that pays for unemployment (UI) benefits. Since rates respond to layoffs, taxes are highest for troubled firms after downturns, potentially reducing labor demand during recoveries. I assess the effect of UI tax hikes among distressed firms leveraging a kink in the tax formula with full-population records from Florida. Higher taxes reduce hiring and employment, with larger effects for firms in duress. In contrast, I find little evidence that penalties deter layoffs. These responses are consistent with a model of cashconstrained firms and suggest unanticipated costs of UI provision. JEL Codes: D22, H21, H22, H25, H71, J01, J08, J23, J32, J38, J64, J65, J68, L20 Johnston: Department of Economics, University of California, Merced; email: acjohnston@ucmerced.edu. I am grateful to many, including Santosh Anagol, Patricia Anderson, Peter Blair, Leah Boustan, Julie Berry Cullen, Gordon Dahl, Mark Duggan, Pascaline Dupas, Fernando Ferreira, Alexander Gelber, Todd Gormley, Jessie Handbury, Joseph Harrington, Ann Harrison, Robert Jensen, Carla Johnston, Judd Kessler, Camille Landais, Corinne Low, Alexandre Mas, Bruce Meyer, Olivia S. Mitchell, Alex Rees-Jones, Michael Sinkinson, Kent Smetters, Jeremy Tobacman, Arthur van Benthem, Christopher Walters, Stephan Woodbury, and Gregory Wright, with particular debt owed to Alexandre Mas, Mark Duggan, Olivia S. Mitchell, Alex Rees-Jones, and Robert Jensen. Data were provided by the Florida Departments of Labor. Financial support from the U.S. Department of Labor’s Employment and Training Administration and the National Bureau of Economic Research is gratefully acknowledged. Views expressed are those of the author and should not be attributed to The University of California. © Johnston, 2017
Overview of Big Data platforms

The primary purpose of this paper is to present and provide main advantages and disadvantages of most popular big data platforms as well as their comparison in terms of ease of installation, work, performance and price, in order to find the most suitable solution to work with big sets of data. Nowadays, the data is largely analyzed by scientists not related to IT, so the ease of use and presentation of data is extremely important. The purpose of the assessment was to indicate the best IT tool for analyzing data from the point of view of a young analyst or scientist graduating and entering the labor market.
PRBM_A_230005 41..53

Kristen M McCabe 1,2 May Yeh Argero A Zerr 1Department of Psychological Sciences, University of San Diego, San Diego, CA, USA; 2Child and Adolescent Services Research Center, San Diego, CA, USA; 3Department of Psychology, San Diego State University, San Diego, CA, USA; 4Department of Psychiatry, University of California, San Diego, CA, USA; 5Department of Psychology, California State University Channel Islands, Camarillo, CA, USA Abstract: Behavioral Parent Training (BPT) interventions are efficacious for young children with externalizing behavior problems. However, not all families benefit, and ethnic minority families in particular are less likely to enroll, engage, and improve in BPT. Versions of BPT interventions tailored for specific ethnic groups have been successful at improving engagement and outcomes for ethnic minorities; however, the specificity of these models presents challenges for broad dissemination. This article presents a personalization approach (PersIn) that utilizes cultural assessment results to tailor treatment protocols to the characteristics of individual families. We believe this approach has the potential to maximize cultural sensitivity while preserving generalizability to both minority and non-minority ethnic groups. We further propose that personalization on Parent Explanatory Model (PEM) parameters that have been found to vary across ethnic groups and to impact treatment engagement and/or outcomes is a promising approach to decreasing disparities in BPTs. We describe examples of evidence-supported PEMs that present good targets for personalization and provide examples from MY PCIT to illustrate how PersIn can be applied to Parent-Child Interaction Therapy (PCIT).
Prediksi Penyakit Jantung Menggunakan Metode-Metode Machine Learning Berbasis Ensemble – Weighted Vote

Abstrak— Kematian yang disebabkan penyakit jantung masih sangat tinggi, sehingga perlu peningkatan upaya-upaya pencegahannya, misalnya dengan meningkatkan capaian model prediksinya. Penerapan metode-metode machine learning pada dataset publik (Cleveland, Hungary, Switzerland, VA Long Beach, & Statlog) yang umumnya digunakan oleh para peneliti untuk prediksi penyakit jantung, termasuk pengembangan alat bantunya, masih belum menangani missing value, noisy data, unbalanced class, dan bahkan data validation secara efisien. Oleh karena itu, pendekatan imputasi mean/mode diusulkan untuk menangani missing value replacement, Min-Max Normalization untuk menangani smoothing noisy data, K-Fold Cross Validation untuk menangani data validation, dan pendekatan ensemble menggunakan metode Weighted Vote (WV) yang dapat menyatukan kinerja tiap-tiap metode machine learning untuk mengambil keputusan klasifikasi sekaligus untuk mereduksi unbalanced class. Hasil penelitian ini menunjukkan bahwa metode yang diusulkan tersebut memberikan akurasi sebesar 85,21%, sehingga mampu meningkatkan kinerja akurasi metode-metode machine learning, selisih 7,14% dengan Artificial Neural Network, 2,77% dengan Support Vector Machine, 0,34% dengan C4.5, 2,94% dengan Naïve Bayes, dan 3,95% dengan k-Nearest Neighbor.
The Application of Manure (Poultry Wests) and Bio-Formulations of Trichoderma harzianum and T. viride and Their Interaction to Control Root-knot Disease on Radish and Chard

The study was designed to examine the effect of manure (poultry wastes) and bio-formulations of Trichoderma harzianum and T. viride separately or with some, to control root-knot disease on radish (Raphanus sativus L.) and chard (Beta vulagaris var.cicla (L.)). The study included the isolation of pathogenic nematode of both plant roots, morphological and molecular identification, examination of the pathogenicity in vitro and in vivo and green house experiments involved application of manure and fungal bio-formulations treatments. The morphological and molecular identification confirmed the identity of root-knot nematode, as Meloidogyne javanica, which was pathogenic to radish and chard. The recorded infection severity was 89 and 95% respectively. The green house experiment results revealed that MThTv treatment was significantly reduced infection severity to 0% for radish and chard in contaminated soil (CS) compared with control positive treatment (55.17 and 40%) respectively. MThTv treatment also showed a highest plant height for Radish in non-nematode-contaminated soil (NCS) and CS treatment (17.85 and 16.50 cm) respectively compared with control positive treatment (5.00 cm), while the highest plant height of Chard was 24.5 cm in MThTv-NCS. The wet weight index in Radish showed a superiority of MThTv and MTh in NC on other treatments (201.75 and 189.5 g.plant-1) respectively followed by MThTv-NCS treatment (184.5 g.plant-1) compared with 19.25 gm.plant-1 in control treatment. In Chard the results showed similar pattern represented by superiority of MThTv-NC treatment (255.25 gm.plant-1) followed by MThTv-NCS (190.75 gm.plant-1) compared with 37.50 gm/plant for positive control.
How do Firms React to Minimum Wage Changes?

This paper provides the first systematic study of how minimum wage policies in China affected firm employment over the 2001–06 period. Using a novel administrative dataset of minimum wage regulations across more than 2,800 counties matched with firm and household data, we find that minimum wage hikes have significantly negative effects on employment. We investigate the heterogeneous effect of the minimum wage on firm employment in terms of firm, industry, spatial characteristics, and labor market conditions. We show that employment falls more in response to minimum wage hikes in lower-wage firms with lower profit margins. The same is true for firms in labor-intensive industries and in labor markets with high mobility. Furthermore, a major reform in the minimum wage policy was conducted by the central government around 2004. We find that the response of firm employment growth to minimum wage hikes has become larger compared with the pre-reform period. Similar results hold for the numerous specifications and robustness checks with the attrition, implementation, and placebo tests. JEL classifications: J24; J31; 014, F10, F14
DNA barcoding of razor clam Solen spp. (Solinidae, Bivalva) in Indonesian beaches

Trisyani N, Rahayu DA. 2020. DNA barcoding of razor clam Solen spp. (Solinidae, Bivalva) in Indonesian beaches. Biodiversitas 21: 478-484. Solen spp. are shells with various morphological characteristics with a wide distribution of tropical and subtropical beaches, including Indonesia. The identification of Solen spp. is generally based on its morphological characteristics. This method is very problematic due to specimens share similarity in morphology and color. This study was using DNA barcode as a molecular identification tool. The bivalve COI sequence was amplified using PCR and molecular phylogenetic analysis using the Neighbor-Joining method. The amplified COI gene has a length of about 665 bp. The purpose of this study was to evaluate genetic variation and compare the phylogenetic Solen spp. in Indonesian waters. The composition of the nucleotide bases of Solen spp. the comparative species are A = 26.79%, C = 23.16%, G = 19.17% and T = 30.93%. The total nucleotide base A + T was 57.72%, while G + C was 42.33%. The results of phylogenetic analysis showed that Solen spp. Cirebon and Jambi are in one clade with Solen regularis with genetic distance 0.000 0.002. Solen spp. Surabaya, Bangkalan, Pamekasan, and Sumenep are in separate clades and are related to Solen grandis, Solen stricus and Solen lamarckii with genetic distance from 0.146 0.156. The diversity of nucleotide was 0.9780 and was divided into 12 haplotypes. Keyword: Phylogenetic, Solen spp. haplotype. genetic distance, COI gene
STRIPS Action Discovery

The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of an initial and final state. In this paper, we propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown. In addition, we contribute with a compilation to classical planning that mitigates the problem of learning static predicates in the action model preconditions, exploits the capabilities of SAT planners with parallel encodings to compute action schemas and validate all instances. Our system is flexible in that it supports the inclusion of partial input information that may speed up the search. We show through several experiments how learned action models generalize over unseen planning instances.
A Graph-Based Approach for Active Learning in Regression

Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human-machine interaction. Though active learning has been extensively researched for classification and ranking problems, it is relatively understudied for regression problems. Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query. This introduces several challenges such as handling noisy labels, parameter uncertainty and overcoming initially biased training data. Instead, we propose a feature-focused approach that formulates both sequential and batch-mode active regression as a novel bipartite graph optimization problem. We conduct experiments on both noise-free and noisy settings. Our experimental results on benchmark data sets demonstrate the effectiveness of our proposed approach.
Vehicles Charging in the Unbalanced Radial Distribution System

This paper presents the modeling and analysis of plug-in electric vehicles (PEVs) charging in an unbalanced radial distribution system. The objective of the study is to evaluate a PEV simulation and the impact of PEV charging on the distribution system. The energy use of a plug-in electric vehicle for a cycle was calculated to plan the charging method of PEVs at a charging station. The constant voltage, constant current, and constant current – voltage charging methods were modeled using voltage and current curves. The PEV charging characteristics were derived from the charging power and queueing analysis. The system power quality according to the load of PEVs charging was analyzed using the unbalanced radial distribution load flow analysis in the Udon Thani Municipality, Thailand. Using this analysis and comparison, the simulation results produced optimal scheduling for charging PEVs improving the unbalance, total power loss, and reducing the number of overloaded feeders.
Semantic Adversarial Perturbations using Learnt Representations

Adversarial examples for image classifiers are typically created by searching for a suitable norm-constrained perturbation to the pixels of an image. However, such perturbations represent only a small and rather contrived subset of possible adversarial inputs; robustness to norm-constrained pixel perturbations alone is insufficient. We introduce a novel method for the construction of a rich new class of semantic adversarial examples. Leveraging the hierarchical feature representations learnt by generative models, our procedure makes adversarial but realistic changes at different levels of semantic granularity. Unlike prior work, this is not an ad-hoc algorithm targeting a fixed category of semantic property. For instance, our approach perturbs the pose, location, size, shape, colour and texture of the objects in an image without manual encoding of these concepts. We demonstrate this new attack by creating semantic adversarial examples that fool state-of-the-art classifiers on the MNIST and ImageNet datasets.
Correlation of Mental Status and Social Support in Post-operative Patients with Knee Joint Replacement

Objective To explore the correlation between mental status and social support in post-operative patients with knee joint replacement.Methods Fifty-eight post-operative patients with knee joint replacement for one week were investigated with questionnaires of social support rating scale(SSRS),self-rating anxiety scale(SAS) and self-rating depression scale(SDS). Results Thiry-two patients had the SAS score≥50,and thirty-four patients had the SDS score≥50.Patients had higher scores of SAS and SDS than the norm(P0.01). There was statistical difference between age,educational level and post-operative anxiety and depression(P0.05). Social support was negative associated with post-operative anxiety and depression(P0.01). The results of multielement stepwise regression analysis showed that the influencing factors on mental status of patients were age,educational level,total score of social support.Conclusion Effective social support can reduce the anxiety and depression in post-operative patients with knee joint replacement and promote the disease’s rehabilitation.
Consensus on Matrix-weighted Time-varying Networks

This paper examines the consensus problem on time-varying matrix-weighed undirected networks. First, we introduce the matrix-weighted integral network for the analysis of such networks. Under mild assumptions on the switching pattern of the time-varying network, necessary and/or sufficient conditions for which average consensus can be achieved are then provided in terms of the null space of matrix-valued Laplacian of the corresponding integral network. In particular, for periodic matrix-weighted time-varying networks, necessary and sufficient conditions for reaching average consensus is obtained from an algebraic perspective. Moreover, we show that if the integral network with period $T>0$ has a positive spanning tree over the time span $[0,T)$, average consensus for the node states is achieved. Simulation results are provided to demonstrate the theoretical analysis.
Towards Power-Efficient Aerial Communications via Dynamic Multi-UAV Cooperation

Aerial base stations (BSs) attached to unmanned aerial vehicles (UAVs) constitute a new paradigm for next-generation cellular communications. However, the flight range and communication capacity of aerial BSs are usually limited due to the UAVs’ size, weight, and power (SWAP) constraints. To address this challenge, in this paper, we consider dynamic cooperative transmission among multiple aerial BSs for power-efficient aerial communications. Thereby, a central controller intelligently selects the aerial BSs navigating in the air for cooperation. Consequently, the large virtual array of moving antennas formed by the cooperating aerial BSs can be exploited for low-power information transmission and navigation, taking into account the channel conditions, energy availability, and user demands. Considering both the fronthauling and the data transmission links, we jointly optimize the trajectories, cooperation decisions, and transmit beamformers of the aerial BSs for minimization of the weighted sum of the power consumptions required by all BSs. Since obtaining the global optimal solution of the formulated problem is difficult, we propose a low-complexity iterative algorithm that can efficiently find a Karush-Kuhn-Tucker (KKT) solution to the problem. Simulation results show that, compared with several baseline schemes, dynamic multi-UAV cooperation can significantly reduce the communication and navigation powers of the UAVs to overcome the SWAP limitations, while requiring only a small increase of the transmit power over the fronthauling links.
A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks

We develop a mathematically rigorous framework for multilayer neural networks in the mean field regime. As the network's width increases, the network's learning trajectory is shown to be well captured by a meaningful and dynamically nonlinear limit (the \textit{mean field} limit), which is characterized by a system of ODEs. Our framework applies to a broad range of network architectures, learning dynamics and network initializations. Central to the framework is the new idea of a \textit{neuronal embedding}, which comprises of a non-evolving probability space that allows to embed neural networks of arbitrary widths. We demonstrate two applications of our framework. Firstly the framework gives a principled way to study the simplifying effects that independent and identically distributed initializations have on the mean field limit. Secondly we prove a global convergence guarantee for two-layer and three-layer networks. Unlike previous works that rely on convexity, our result requires a certain universal approximation property, which is a distinctive feature of infinite-width neural networks. To the best of our knowledge, this is the first time global convergence is established for neural networks of more than two layers in the mean field regime.
Visual Exploration of Movement Relatedness for Multi-species Ecology Analysis

Advances in GPS telemetry technology have enabled analysis of animal movement in open areas. Ecologists today are utilizing modern analytic tools to study animal behaviors from large quantity of GPS coordinates. Analytic tools with automatic event extraction functionality can be used to investigate potential interactions between animals by locating relevant segments in movement trajectories. However, such automation can easily overlook the spatial, temporal, social context as well as potential data problems. To this end, this paper explores the visual presentations that also clarify the spatial-temporal contexts, social surroudings, as well as underlying data uncertainties of multi-species animal interactions. The outcome system presents the proximity-based, time-varying relatedness between animal entities through pairwise (PW) or individual-to-group (i-G) perspectives. Focusing on the relational aspects, we employ both static depictions and animations to communicate the travelling of individuals. Our contributions are a novel visualization system that helps investigate the subtle variations of long term spatial-temporal relatedness while considering small group patterns. Our evaluation with movement ecologists shows that the system gives them quick access to valuable clues in discovering insights into multi-species movements and signs of potential interactions.
INFLUENCE OF THE ‘NEWS FINDS ME’ PERCEPTION ON NEWS SHARING AND NEWS CONSUMPTION ON SOCIAL MEDIA

The study focuses on ‘News Finds Me’ perception, i.e. the belief that users can stay informed enough without actively following traditional news media while relying on social media and personal contacts. The text analyses the influence between this perception and news consumption (“news internalising”) and sharing (“news externalising”). Uses & Gratifications sought (“information seeking”, “social interaction” and “impression management”) were considered as moderating variables. Survey based results (n = 96) show that ‘News Finds Me’ perception is positively correlated with news internalising but not with news externalising. It is thus concluded that the mere presence of the ‘News Finds Me’ perception does not encourage news sharing attitudes. The study also brings evidence to support a relationship between news internalising and news externalising habits, which influence each other mutually and can moderate other factors in news consumption and dissemination on social media. It is concluded that the presence of the ‘News Finds Me’ perception does not encourage news sharing attitudes.