Miracle-dz/UniFAR-FTUs huggingface dataset:
Public Behavior as an Output of E-Government Service: The Role of New Technology Integrated in E-Government and Antecedent of Relationship Quality

Electronic government has played an essential role in citizen policy strategy, especially during the COVID-19 pandemic. As part of citizen and government interaction, companies can utilize electronic government facilities to establish direct communication and maintain service quality by integrating new technologies and facilities in electronic government which can provide service via online, embedded with social media integration, implemented with mobile service, using personalized user accounts, e-participation service integration, in combination with ongoing citizen information. Therefore, electronic government facilities must adapt and choose the right content to promote strong citizen relations, leading to citizen behavior to make long-term use of electronic government facilities. This study considered and integrated the latest technology from electronic government and associated it with connection quality. Sustainable motives and faithfulness were used to quantify the quality of citizen relations to electronic government facilities, which can influence the results of citizen behavior. The SmartPLS 2 software was used to quantify and estimate 425 online questionnaire surveys. The results showed that of the 12 hypotheses, eight hypotheses were declared to have a significant effect, consisting of H1, H2, H6–H8, and H10–H12, while the other four hypotheses were stated to have no significant effect, namely H3–H5, and H9. This study was used to guide the government as a provider of electronic government facilities to adapt and provide content following the dimensions of the latest technology to achieve goals and produce sustainable implementation.
ZnO Films Incorporation Study on Macroporous Silicon Structure

In the present work, we developed hybrid nanostructures based on ZnO films deposited on macroporous silicon substrates using the sol–gel spin coating and ultrasonic spray pyrolysis (USP) techniques. The changes in the growth of ZnO films on macroporous silicon were studied using a UV-visible spectrometer, an X-ray diffractometer (XRD), scanning electron microscopy (SEM) and atomic force microscopy (AFM). XRD analysis revealed the beneficial influence of macroporous silicon on the structural properties of ZnO films. SEM micrographs showed the growth and coverage of ZnO granular and flake-like crystals inside the pores of the substrate. The root mean square roughness (RMS) measured by AFM in the ZnO grown on the macroporous silicon substrate was up to one order of magnitude higher than reference samples. These results prove that the methods used in this work are effective to cover porous and obtain nano-morphologies of ZnO. These morphologies could be useful for making highly sensitive gas sensors.
The Negative Effect of Protein Phosphatase Z1 Deletion on the Oxidative Stress Tolerance of Candida albicans Is Synergistic with Betamethasone Exposure

The glucocorticoid betamethasone (BM) has potent anti-inflammatory and immunosuppressive effects; however, it increases the susceptibility of patients to superficial Candida infections. Previously we found that this disadvantageous side effect can be counteracted by menadione sodium bisulfite (MSB) induced oxidative stress treatment. The fungus specific protein phosphatase Z1 (CaPpz1) has a pivotal role in oxidative stress response of Candida albicans and was proposed as a potential antifungal drug target. The aim of this study was to investigate the combined effects of CaPPZ1 gene deletion and MSB treatment in BM pre-treated C. albicans cultures. We found that the combined treatment increased redox imbalance, enhanced the specific activities of antioxidant enzymes, and reduced the growth in cappz1 mutant (KO) strain. RNASeq data demonstrated that the presence of BM markedly elevated the number of differentially expressed genes in the MSB treated KO cultures. Accumulation of reactive oxygen species, increased iron content and fatty acid oxidation, as well as the inhibiting ergosterol biosynthesis and RNA metabolic processes explain, at least in part, the fungistatic effect caused by the combined stress exposure. We suggest that the synergism between MSB treatment and CaPpz1 inhibition could be considered in developing of a novel combinatorial antifungal strategy accompanying steroid therapy.
Accelerating Cluster Assignment for SeqClu

Clustering is a group of (unsupervised) machine learning algorithms used to categorize data into clusters. The most popular clustering algorithm is k-means clustering. K-means clustering clusters the data into k clusters where a cluster is represented by the mean of the data points called a centroid. Instead of using the mean as a centroid, a data point (medoid) can be used instead. This algorithm is called k-medoids algorithm. Both the algorithms work in an offline setting where all the data is known beforehand and usually use Euclidean distance to calculate the distance between any two points. SeqClu is another clustering algorithm for sequential data that works in an online setting and uses Dynamic Time Warping as its distance measure. It is based on k-medoids where it uses p sequences called prototypes, to represent a cluster. It assigns an incoming sequence to the cluster that has the lowest average distance between its prototypes and the incoming sequence. The issue in this approach is that many Dynamic Time Warping distance calculations need to be made which affects the clustering speed and using the average distance affects the clustering accuracy due to outliers being assigned as prototypes. This paper proposes an alternative algorithm with three variants for the cluster assignment process. This algorithm iterates through the prototypes in search for the closest prototype while excluding clusters that are deemed too far. It assigns the incoming sequence to the cluster of the closest prototype that it has found. Experiments on the UJI Pen Characters and UCR Synthetic Control datasets show an improvement in clustering speed and accuracy.
Validation and Adjustment of the Patient Experience Questionnaire (PEQ): A Regional Hospital Study in Norway

This paper assesses the psychometric qualities of the Patient Experience Questionnaire (PEQ), thereby validating a patient-oriented measurement model in a hospital environment, and modifies the model based on empirical results. This study employed survey data gathered by the Norwegian Institute of Public Health from adult inpatients at somatic hospitals in the Health South-East RHF in Norway. The survey engaged 4603 patients out of 8381 from five main hospitals in the region. The study found that an eight-factor model of the PEQ generally showed good fitness to the data, but assessment of discriminant validity showed that this was not the optimal factor solution among four of the eight dimensions. After comparing models, the study proposed a model with a second-order factor for four of the factors: “nurse services”, “doctor services”, “information”, and “organization”, collectively named “treatment services”. The proposed model demonstrated good validity and reliability results. The results present theoretical and practical implications. The study recommends that inferential analyses on the PEQ should be done with the second-order factor. Furthermore, a revision of the PEQ is recommended subject to more confirmatory studies with larger samples in different regions. The study indicates a second-order factor structure for assessing and understanding patient experiences—a finding which has both theoretical and managerial implications.
4-Phenethyl-1-Propargylpiperidine-Derived Dual Inhibitors of Butyrylcholinesterase and Monoamine Oxidase B

The multi-target-directed ligands (MTDLs) strategy is encouraged for the development of novel modulators targeting multiple pathways in the neurodegenerative cascade typical for Alzheimer’s disease (AD). Based on the structure of an in-house irreversible monoamine oxidase B (MAO-B) inhibitor, we aimed to introduce a carbamate moiety on the aromatic ring to impart cholinesterase (ChE) inhibition, and to furnish multifunctional ligands targeting two enzymes that are intricately involved in AD pathobiology. In this study, we synthesized three dual hMAO-B/hBChE inhibitors 13–15, with compound 15 exhibiting balanced, low micromolar inhibition of hMAO-B (IC50 of 4.3 µM) and hBChE (IC50 of 8.5 µM). The docking studies and time-dependent inhibition of hBChE confirmed the initial expectation that the introduced carbamate moiety is responsible for covalent inhibition. Therefore, dual-acting compound 15 represents an excellent starting point for further optimization of balanced MTDLs
Mapping landmark cases in the U.S. legal system

Court decisions and emblematic legal cases are central elements of Law. They influence practitioners, scholars and public officers at the same time they define and shape the legal reality and its boundaries. Despite being an overlooked area, understanding how legal cases establishes connections and relationships can provide important insights not only about how influence and impact are built, but also to identify influential cases that are not listed as landmarks. Here, we explore data from the U.S. legal system by modeling it as a citation network fed with 360 years of legal cases. We characterize the probability distributions for degree-centrality measures of the network and find a power-law behavior for the in-degree probability distribution with an exponent α ≈ 2.66. We also obtain the probability distribution for landmarks according to their in-degree and out-degree in order to find the region in the in-degree×out-degree space where landmarks are more likely to be found. Finally, we highlight some extreme special cases and make some considerations about the ratio between the number of landmarks and the total number of legal cases in a given spot of the in-degree×out-degree space.
Construction and preliminary evidence of validity of the Scale of Perceived Threat from the Mentally Ill Elaboração e evidências preliminares de validade da Escala de Percepção de Ameaça frente ao Doente Mental

The perception that mental patients represent a threat may support attitudes and behaviours of social exclusion and a contrary position to the precepts of the Psychiatric Reform. In this sense, this article has the objective of elaborating the Scale of Perceived Threat from the Mentally Ill and reporting the preliminary evidences of its factorial validity and internal consistence. For this purpose, two studies were performed: Study 1 performed an exploratory factor analysis composed of 244 university students aged 17 to 56 years old (M = 22.98; SD = 5.61); and study 2 performed a confirmation factor analysis from a sample of 247 university students aged between 17 and 51 years old (M = 23.08; SD = 5.75). The final scale was composed of nine items grouped into two factors: dangerousness and unpredictability, and presented good psychometric properties, being able to be used adequately to evaluate the perception of threat regarding the mentally Ill.
James Croll and geological archives: testing astronomical theories of ice ages

James Croll's Physical Theory of Secular Changes of Climate emerged during an age of revolution in geology that included the rise of the glacial theory and the search for its underlying causes. According to Croll, periods of high eccentricity are associated with the persistence of long glacial epochs, within which glaciations occur in alternate hemispheres when winter is at aphelion every ~11,000 years; however, astronomical forcing is only able to produce glaciation by means of physical agencies (climate feedbacks) that amplify the small effects of varying seasonal irradiation. Croll understood the importance of interglacial deposits because they provided evidence for the occurrence of multiple glaciations within his long glacial epochs. He was aware of the limitations of the terrestrial record and suggested that deep-sea sediments would contain a continuous succession of glacial-interglacial cycles. Contrary to a widespread view, however, Croll was not envisaging the advent of palaeoceanographic exploration avant la lettre, but instead was drawing attention to the inadequacy of the land record as a testbed of his astronomical theory. Yet, the marine record did eventually deliver a test of astronomical theories almost exactly 100 years after the publication of his 1875 book Climate and Time in their Geological Relations. Here, we provide an historical account of the technological and scientific developments that led to this and a summary of insights on astronomically paced climate changes from marine, terrestrial and ice core records. We finally assess Croll's ideas in the context of our current understanding of the theory of ice ages.
A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations

We present a hybrid model combining cellular automata (CA) and agent-based modeling (ABM) to analyze the deployment of electric vehicle charging stations through microscopic traffic simulations. This model is implemented in a simulation tool called SIMTRAVEL, which allows combining electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) that navigate in a city composed of streets, avenues, intersections, roundabouts, and including charging stations (CSs). Each EV is modeled as an agent that incorporates complex behaviors, such as decisions about the route to destination or CS, when to drive to a CS, or which CS to choose. We studied three different CS arrangements for a synthetic city: a single large central CS, four medium sized distributed CSs or multiple small distributed CSs, with diverse amounts of traffic and proportions of EVs. The simulator output is found to be robust and meaningful and allows one to extract a first useful conclusion: traffic conditions that create bottlenecks around the CSs play a crucial role, leading to a deadlock in the city when the traffic density is above a certain critical level. Our results show that the best disposition is a distributed network, but it is fundamental to introduce smart routing measures to balance the distribution of EVs among CSs.
Counterfactual Explanations for Student Outcome Prediction with Moodle Footprints

Counterfactual explanations focus on “actionable knowledge” to help end-users understand how a machine learning outcome could be changed to one that is more desirable. For this purpose a counterfactual explainer needs to be able to reason with similarity knowledge in order to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes to action a change in the decision is an interesting challenge for counterfactual explainers. In this paper we show how feature relevance based explainers (such as LIME), can be combined with a counterfactual explainer to identify the minimum subset of “actionable features”. We demonstrate our hybrid approach on a real-world use case on student outcome prediction using data from the CampusMoodle Virtual Learning environment. Our preliminary results demonstrate that counterfactual feature weighting to be a viable strategy that should be adopted to minimise the number of actionable changes.
Sex Differences and the Role of Gaming Experience in Spatial Cognition Performance in Primary School Children: An Exploratory Study

Sex differences are repeatedly observed in spatial cognition tasks. However, the role of environmental factors such as gaming experience remains unclear. In this exploratory study, navigation and object-relocation were combined in a naturalistic virtual reality-based spatial task. The sample consisted of n = 53 Dutch children aged 9–11 years. Overall, girls (n = 24) and boys (n = 29) performed equally accurately, although there was an increase in accuracy with age for boys (ηp2 = 0.09). Boys navigated faster than girls (ηp2 = 0.29), and this difference increased with age (ηp2 = 0.07). More gaming experience in boys versus girls (Cohen’s d = 0.88) did not explain any result observed. We encourage future confirmatory studies to use the paradigm presented here to investigate the current results in a larger sample. These findings could be beneficial for optimizing spatial cognition training interventions.
FEM and Analytical Modeling of the Incipient Chip Formation for the Generation of Micro-Features

This paper explores the modeling of incipient cutting by Abaqus, LS-Dyna, and Ansys Finite Element Methods (FEMs), by comparing also experimentally the results on different material classes, including common aluminum and steel alloys and an acetal polymer. The target application is the sustainable manufacturing of gecko adhesives by micromachining a durable mold for injection molding. The challenges posed by the mold shape include undercuts and sharp tips, which can be machined by a special diamond blade, which enters the material, forms a chip, and exits. An analytical model to predict the shape of the incipient chip and of the formed grove as a function of the material properties and of the cutting parameters is provided. The main scientific merit of the current work is to approach theoretically, numerically, and experimentally the very early phase of the cutting tool penetration for new sustainable machining and micro-machining processes.
Novel Purine Chemotypes with Activity against Plasmodium falciparum and Trypanosoma cruzi

Malaria and Chagas disease, caused by Plasmodium spp. and Trypanosoma cruzi parasites, remain important global health problems. Available treatments for those diseases present several limitations, such as lack of efficacy, toxic side effects, and drug resistance. Thus, new drugs are urgently needed. The discovery of new drugs may be benefited by considering the significant biological differences between hosts and parasites. One of the most striking differences is found in the purine metabolism, because most of the parasites are incapable of de novo purine biosynthesis. Herein, we have analyzed the in vitro anti-P. falciparum and anti-T. cruzi activity of a collection of 81 purine derivatives and pyrimidine analogs. We firstly used a primary screening at three fixed concentrations (100, 10, and 1 µM) and progressed those compounds that kept the growth of the parasites < 30% at 100 µM to dose–response assays. Then, we performed two different cytotoxicity assays on Vero cells and human HepG2 cells. Finally, compounds specifically active against T. cruzi were tested against intracellular amastigote forms. Purines 33 (IC50 = 19.19 µM) and 76 (IC50 = 18.27 µM) were the most potent against P. falciparum. On the other hand, 6D (IC50 = 3.78 µM) and 34 (IC50 = 4.24 µM) were identified as hit purines against T. cruzi amastigotes. Moreover, an in silico docking study revealed that P. falciparum and T. cruzi hypoxanthine guanine phosphoribosyltransferase enzymes could be the potential targets of those compounds. Our study identified two novel, purine-based chemotypes that could be further optimized to generate potent and diversified anti-parasitic drugs against both parasites.
J ul 2 02 1 Treatment Effects Estimation by Uniform Transformer

In observational studies, balancing covariates in different treatment groups is essential to estimate treatment effects. One of the most commonly used methods for such purposes is weighting. The performance of this class of methods usually depends on strong regularity conditions for the underlying model, which might not hold in practice. In this paper, we investigate weighting methods from a functional estimation perspective and argue that the weights needed for covariate balancing could differ from those needed for treatment effects estimation under low regularity conditions. Motivated by this observation, we introduce a new framework of weighting that directly targets the treatment effects estimation. Unlike existing methods, the resulting estimator for a treatment effect under this new framework is a simple kernel-based U -statistic after applying a data-driven transformation to the observed covariates. We characterize the theoretical properties of the new estimators of treatment effects under a nonparametric setting and show that they are able to work robustly under low regularity conditions. The new framework is also applied to several numerical examples to demonstrate its practical merits. ∗Address for Correspondence: Department of Statistics, University of Illinois at Urbana-Champaign, 725 South Wright Street, Champaign, IL 61820 (Email: shuleiw@illinois.edu).
The Crystal Structure of the Spodoptera litura Chemosensory Protein CSP8

Simple Summary Worldwide, pest control involves extensive use of insecticides, which results in serious environmental pollution problems. On the other hand, insecticides can be recognized by proteins named CSPs in insects, which allow them to accurately respond to these environmental chemical signals for their survival, but the mechanism is poorly studied. Here, we report the crystal structure of the CSP8 protein from the tobacco cutworm Spodoptera litura, a major plant pest in Asia. We also studied its binding properties to compounds like rhodojaponin III, a non-volatile plant metabolite. Our studies showed that the protein binds to these molecules with different affinities and provided important insight into the molecular recognition mechanism of the sensory protein SlCSP8 and the CSP protein family in general. Abstract Spodoptera litura F. is a generalist herbivore and one of the most important economic pests feeding on about 300 host plants in many Asian countries. Specific insect behaviors can be stimulated after recognizing chemicals in the external environment through conserved chemosensory proteins (CSPs) in chemoreceptive organs, which are critical components of the olfactory systems. To explore its structural basis for ligand-recognizing capability, we solved the 2.3 Å crystal structure of the apoprotein of S. litura CSP8 (SlCSP8). The SlCSP8 protein displays a conserved spherical shape with a negatively charged surface. Our binding assays showed that SlCSP8 bound several candidate ligands with differential affinities, with rhodojaponin III being the most tightly bound ligand. Our crystallographic and biochemical studies provide important insight into the molecular recognition mechanism of the sensory protein SlCSP8 and the CSP family in general, and they suggest that CSP8 is critical for insects to identify rhodojaponin III, which may aid in the CSP-based rational drug design in the future.
The Impact of Government Policy on Macro Dynamic Innovation of the Creative Industries: Studies of the UK’s and China’s Animation Sectors

With digitalization and the support of policies, the creative industries have shown rapid growth in the last 20 years. Open forms of collective learning, user engagement and social networks have become popular to generate IPs and values. Meanwhile, government policy can support the sectors through subsidies, regulations, standardization, and protections at regional and national levels. This paper aims to explore the role of government policy in the innovation of creative industries from a macro dynamic perspective. The research method combines a structured literature review, a secondary document review of industry reports and government policy, and thematic content analysis. Through in-depth studies of the UK’s and China’s animation sectors, the paper identifies key elements of closed innovation, social innovation, and open innovation systems in the market. Comparisons of national government policies since 2000 reveal different approaches for countries where creative sectors are well-established, and for those starting with limited knowledge resources. A dynamic model is developed to address the evolution of macro dynamic innovation systems and the role of policies as interactive mechanisms. Practical implementation and future research areas are also suggested.