Miracle-dz/UniFAR-FTUs huggingface dataset:
Applied usage and performance of statistical matching in bibliometrics: The comparison of milestone and regular papers with multiple measurements of disruptiveness as an empirical example

Abstract Controlling for confounding factors is one of the central aspects of quantitative research. Although methods such as linear regression models are common, their results can be misleading under certain conditions. We demonstrate how statistical matching can be utilized as an alternative that enables the inspection of post-matching balancing. This contribution serves as an empirical demonstration of matching in bibliometrics and discusses the advantages and potential pitfalls. We propose matching as an easy-to-use approach in bibliometrics to estimate effects and remove bias. To exemplify matching, we use data about papers published in Physical Review E and a selection classified as milestone papers. We analyze whether milestone papers score higher in terms of a proposed class of indicators for measuring disruptiveness than nonmilestone papers. We consider disruption indicators DI1, DI5, DI1n, DI5n, and DEP and test which of the disruption indicators performs best, based on the assumption that milestone papers should have higher disruption indicator values than nonmilestone papers. Four matching algorithms (propensity score matching (PSM), coarsened exact matching (CEM), entropy balancing (EB), and inverse probability weighting (IPTW)) are compared. We find that CEM and EB perform best regarding covariate balancing and DI5 and DEP performing well to evaluate disruptiveness of published papers.
The secreted proteome of necrotrophic
 Ciborinia camelliae
 causes nonhost‐specific virulence

The family Sclerotiniaceae in the phylum Ascomycota includes two important plant pathogens, Botrytis cinerea and Sclerotinia sclerotiorum, that infect numerous phylogenetically unrelated plant species and significantly affect crop production (Navaud et al., 2018). In the same family, Ciborinia camelliae causes a similar but distinct disease because it infects only the floral organs of ornamental plants of the genus Camellia, causing petal blight disease. Infection starts with a short asymptomatic stage, followed by the rapid browning and fall of Camellia blooms (DentonGiles et al., 2013). The disease affects many ornamental Camellia cultivars (e.g., C. japonica, C. reticulata) and can also infect the commercially important tea crop, C. sinensis (DentonGiles et al., 2013). Regardless of their lifestyle, fungi produce a wide range of secreted proteins. Proteins produced by pathogenic fungi help to circumvent physical barriers in their plant hosts, degrade complex organic compounds to acquire nutrients (e.g., cutinases, cellulases, endopolygalacturonases; Kubicek et al., 2014), and manipulate host responses (e.g., toxins and immune suppressors; Frías et al., 2011; Zhu et al., 2013). The composition of proteinaceous secretomes is linked to fungal lifestyle. Symbiotic and biotrophic fungi, which Received: 24 May 2021 | Accepted: 31 August 2021 DOI: 10.1111/ppa.13477
A 60 GHz Millimeter-wave Antenna Array for 3D Antenna-in-Package Applications

This paper presents a 60 GHz millimeter-wave (mm-wave) antenna array using standard printed circuit board (PCB) for 3D Antenna-in-package (AiP) implementation. The array consists of a 4 microstrip patch elements, differentially fed with an open stub matching feed network to enable 3D integration. The 1×4 finite antenna array with ball grid array (BGA) and silicon (Si) interposer operates from 58.46 to 62.14 GHz with 3.6 GHz instantaneous bandwidth, low mutual coupling of about <-25 dB and achieves a realized gain of about 10.51 dBi. The array is capable of scanning down to ±45° and provides low cross polarization levels of -40 dB. The fabricated multilayer 1×4 array consists of two substrates and one bondply layer with antennas, via-to-open stub matching network, and a differential to single-ended corporate feed network for the measurement. A prototype with a differential to single-ended corporate feed network was fabricated and tested showing a gain of about 10.02 dBi at the operating frequency with ≥90% radiation efficiency. Such a gain and efficiency make the presented design a leading candidate for 3D AiP applications.
EVALUATION OF MAIZE WATER PRODUCTIVITY BY FAO-AQUACROP MODEL USING INTEGRATION OF MULCHING, NITROGEN FERTILIZATION AND IRRIGATION IN SALINE AND NON-SALINE SOILS

: The AquaCrop model (version 5) was validated using data from two field experiments carried out during the summer seasons of 2019 and 2020 in the North Delta (Sakha and El-Hamoul Districts). To research the impact of deficit irrigation, nitrogen fertilization and soil mulching on the productivity of maize water. Then, using such results, the AquaCrop model was validated by various statistical indicators such as determination coefficient(R 2 ), normalized root means square error (NRMSE), degree of agreement (D) and efficiency (E). Results showed that under different irrigation regimes, nitrogen fertilization levels and mulching application in the North Delta, AquaCrop software was able to simulate well the crop water productivity (WP). Where R2, NRMSE, D and E were respectively0.88,0.36, 0.98 and 0.99 percent under non-saline soil conditions (Sakha location) values. While, under saline soil conditions, such values were 0.85, 16.5, 0.62 and 0.87 % for R 2 , NRMSE, D and E respectively. Data also, showed that, under non saline soil, the highest value of WP was obtained by irrigation at 40 days after post planting irrigation, then irrigation at 80 % depletion from soil available water, non-limiting nitrogen fertilization and using plastic mulching. While, under saline soil conditions, irrigation at 30 days after post planting irrigation, then irrigation at 60 % depletion from soil available water, near optimal nitrogen fertilization and plastic mulching gave the best value of WP.
Uncertainties and their interaction in flood hazard assessment with climate change

Abstract. The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We evaluate and quantify uncertainties in future flood quantiles associated with climate change for four catchments, incorporating within our modelling chain uncertainties associated with 12 global climate models contained in the Coupled Model Intercomparison Project Phase 6, five different bias correction approaches, hydrological model parameter uncertainty and the use of three different extreme value distributions for flood frequency analysis. Results indicate increased flood hazard in all catchments for different Shared Socioeconomic Pathways (SSPs), with changes in flooding consistent with changes in annual maximum precipitation. We use additive chains and analysis of variance (ANOVA) to quantify and decompose uncertainties and their interactions in estimating selected flood quantiles for each catchment. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis. While uncertainties in future projections are widely assumed to be dominated by the ensemble of climate models used, we find that in one of our catchments uncertainties associated with bias correction methods dominate, while in another the uncertainty associated with the use of different extreme value distributions outweighs the uncertainty associated with the ensemble of climate models. These findings highlight the inability to generalise a priori about the importance of different components of the cascade of uncertainty in future flood hazard at the catchment scale. Moreover, we find that the interaction of components of the modelling chain employed are substantial (> 20 % of overall uncertainty in two catchments). While our sample is small, there is evidence that the dominant components of the cascade of uncertainty may be linked to catchment characteristics and rainfall–runoff processes. Future work that seeks to further explore the characteristics of the uncertainty cascade as they relate to catchment characteristics may provide insight into a priori identifying the key components of modelling chains to be targeted in climate change impact assessments.
Co-, Cu- and Fe-Doped Ni/Al2O3 Catalysts for the Catalytic Decomposition of Methane into Hydrogen and Carbon Nanofibers

The catalytic decomposition of methane (CDM) process produces hydrogen in a single stage and avoids CO2 emission thanks to the formation of high added value carbon nanofilaments as a by-product. In this work, Ni monometallic and Ni–Co, Ni–Cu, and Ni–Fe bimetallic catalysts are tested in the CDM reaction for the obtention of fishbone carbon nanofibers (CNF). Catalysts, in which Al2O3 is used as textural promoter in their formulation, are based on Ni as main active phase for the carbon formation and on Co, Cu, or Fe as dopants in order to obtain alloys with improved catalytic behaviour. Characterization of bimetallic catalysts showed the formation of particles of Ni alloys with a bimodal size distribution. For the doping content studied (5 mol. %), only Cu formed an alloy with a lattice constant high enough to be able to favor the carbon diffusion through the catalytic particle against surface diffusion, resulting in higher carbon formations, longer activity times, and activity at 750 °C; whereas Ni, Ni–Co, and Ni–Fe catalysts were inactive. On the other hand, Fe also improved the undoped catalyst performance presenting a higher carbon formation at 700 °C and the obtention of narrow carbon nanofilaments from active Ni3Fe crystallites.
“We” are in This Pandemic, but “You” can get Through This: The Effects of Pronouns on Likelihood to Stay-at-Home During COVID-19

We examine how first-person plural and second-person singular pronouns used in coronavirus disease 2019 (COVID-19) communications impact people's likelihood to follow stay-at-home recommendations. A 2 (first-person plural [“we”] vs. second-person singular [“you”]) by continuous trait self-control between-subjects experiment (N = 223) was used to examine individuals’ adherence to stay-at-home recommendations. Results suggest that “you”-based appeals may be more broadly effective in garnering stay-at-home adherence, whereas low self-control individuals are less responsive to “we” appeals. Implications for research and practice are discussed.
Habitat Specialization by Wildlife Reduces Pathogen Spread in Urbanizing Landscapes

Urban areas are expanding globally with far-reaching ecological consequences, including for wildlife-pathogen interactions. Wildlife show tremendous variation in their responses to urbanization; even within a single population, some individuals can specialize on urban or natural habitat types. This specialization could alter pathogen impacts on host populations via changes to wildlife movement and aggregation. Here, we build a mechanistic model to explore how habitat specialization in urban landscapes affects interactions between a mobile host population and a density-dependent specialist pathogen that confers no immunity. We model movement on a network of resource-stable urban sites and resource-fluctuating natural sites, where hosts are urban specialists, natural specialists, or generalists that use both patch types. We find that for generalists, natural and partially urban landscapes produce the highest infection prevalence and mortality, driven by high movement rates at natural sites and high densities at urban sites. However, habitat specialization protects hosts from these negative effects of partially urban landscapes by limiting movement between patch types. These findings suggest that habitat specialization can benefit populations by reducing infectious disease transmission, but by reducing movement between habitat types it could also carry the cost of reducing other movement-related ecosystem functions, such as seed dispersal and pollination.
Burstiness-Aware Web Search Analysis on Different Levels of Evidences

Personalizing the analysis for web search potentially improves the search experience. A good analytical model for web search should leverage not only collective wisdom but also individual characteristics. Most of the existing analytical models, however, such as the click graph and its variants, focus on how to utilize the collective wisdom, from a crowd, for instance. In this paper, we address the problem of user-specific web search analysis by considering the so-called burstiness in web search, which captures the behavior of rare words appearing many times in a single document. We go beyond click graph and propose two probabilistic topic models, namely, Topic Independence Model and Topic Dependence Model. The former adopts the assumption that the generation of query terms and URLs are topically independent, and the latter captures the coupling between search queries and URLs. We also capture the temporal burstiness of topics by utilizing the continuous Beta distribution. Based on the two proposed models, we propose a novel burstiness-aware search topic rank. Through a large-scale analysis of a real-life search query log, we observe that each user's web search trail enjoys multiple kinds of user-based unique characteristics. On a massive search query log, the new models achieve a better held-out likelihood than standard LDA, DCMLDA and TOT, and they can also effectively reveal the latent evolution of topics on the corpus level and user-based level.
Colored Shade Nets Can Relieve Abnormal Fruit Softening and Premature Leaf Senescence of “Jumeigui” Grapes during Ripening under Greenhouse Conditions

High temperature causes premature grape leaf senescence, abnormal berry softening, and shortening of the fruiting period. Furthermore, the fruit quality and yield are severely affected. Here, the “Jumeigui” grape quality and leaf senescence were evaluated under shading; green, blue, black, and gray nets were used for shading, and their spectra were measured. At the same density, the shade-net color significantly affected cooling and shading efficiencies, with gray nets showing the best light transmission and cooling effect. Shading significantly alleviated abnormal heat-induced grape softness. The total soluble solids (TSS) content and grape coloration were affected under gray, blue, and green shade nets. Nonetheless, TSS exceeded 18 °Brix under gray, blue, and green nets, as required of first-class high-quality fruit. The peel color was not significantly affected under gray or blue shade nets, whereas unshaded grapes showed clear heat-stress damage, especially on the edges of unshaded bottom leaves, in which the net photosynthesis rate was significantly lower than that under shading, indicating that high light intensity and heat caused premature leaf senescence. Colored shade nets reduced greenhouse temperature and light intensity, thereby alleviating the premature senescence of grape plants. Grape quality under black shade nets was poor, whereas superior quality was achieved using gray or blue shade nets.
NLP-assisted Web Element Identification Toward Script-free Testing

End-to-end test automation is important in modern web application development. However, existing test automation techniques have challenges in implementing and maintaining test scripts. It is difficult to keep correct locators, which test scripts require to identify web elements on web pages. The reason is that locators depend on the metadata in web elements or the structure of each web page. One efficient way to solve the problem of locators is to make test cases written in natural language executable without test scripts. As the first step of script-free testing, we propose a technique to identify web elements to be operated and to determine test procedures by interpreting test cases. The test cases are written in a domain-specific language without relying on the metadata of web elements or the structural information of web pages. We leverage natural language processing techniques to understand the semantics of web elements. We also create heuristic search algorithms to find promising test procedures. To evaluate our proposed technique, we applied it to two open-source web applications. The experimental results show that our technique successfully identified 94% of web elements to be operated in the test cases.
Convolutional Neural Networks Progress: Architectural and Optimization Methods Survey

: Since the start of the Convolutional Neural Networks (CNN) paradigm, they were applied in a wide range of computer vision tasks such as image classification, object detection, localization, tracking and action recognition where they were able to show breakthrough performance and generate a new state of the art results. This paper surveys the progress of CNN from an architectural and optimization perspectives. While many CNN reviews exist in the literature, most of them had focused on providing a survey either from a network architecture prospective or an application one, unlike this one which provides a brief general overview for the key features of CNN, followed by reviewing the progress of the state of the art architectures and finally considers the change in the merit of figure of how the CNN are evaluated to include the optimization methods to provide practical CNN that can be deployed on today’s hardware infrastructure without significantly impacting the achieved accuracy.
FORMULATION, OPTIMIZATION, AND IN VITRO CHARACTERIZATION OF DASATINIB LOADED POLYMERIC NANOCARRIERS TO EXTEND THE RELEASE OF THE MODEL DRUG

Objective: The present research aims to design, develop, optimize, characterize and evaluate dasatinib (DSB) loaded polymeric nanocarriers to treat chronic myeloid leukaemia (CML) by adopting a quality by design (QbD) approach. Methods: Risk assessment was performed by using failure modes and effects analysis, and optimization of nanoformulation was done by adopting 23 full factorial design. The optimized nanoformulation was characterized by different characterization techniques and evaluated by various in vitro studies. Results: Surface morphology and shape were found to be smooth and spherical. Stability study results revealed that the nanoformulation could be stored in all three storage conditions for safe and long-term use since it retained its pharmaceutical properties. Drug release was 32.06 % in the first 4 h and 79.34 % by the end of 48 h which infers a sustained-release pattern. The hemocompatibility results showed no sign of hemolysis. Cellular uptake study showed approximately 10 to 20-fold much higher intracellular fluorescence intensities of nanoformulation than DSB. Cytotoxicity results confirmed that when compared to the pure drug, the optimized nanoformulation have a potential cytotoxic effect in the treatment of CML since it exhibited a significantly more % growth inhibition. Cell apoptosis assay revealed that the nanoformulation could provide significant antileukaemia activity against K562 cells and further induce K562 cell death with a dose and time-dependent manner. Conclusion: The results of the characterization and evaluation studies showed that the developed nanoformulation offered significant advantages, making it a potential delivery system of DSB for more effective treatment of CML.
Sustainably Processed Waste Wool Fiber-Reinforced Biocomposites for Agriculture and Packaging Applications

In the EU, sheep bred for dairy and meat purposes are of low quality, their economic value is not even enough to cover shearing costs, and their wool is generally seen as a useless by-product of sheep farming, resulting in large illegal disposal or landfilling. In order to minimize environmental and health-related problems considering elemental compositions of discarded materials such as waste wool, there is a need to recycle and reuse waste materials to develop sustainable innovative technologies and transformation processes to achieve sustainable manufacturing. This study aims to examine the application of waste wool in biocomposite production with the help of a sustainable hydrolysis process without any chemicals and binding material. The impact of superheated water hydrolysis and mixing hydrolyzed wool fibers with kraft pulp on the performance of biocomposite was investigated and characterized using SEM, FTIR, tensile strength, DSC, TGA, and soil burial testing in comparison with 100% kraft pulp biocomposite. The superheated water hydrolysis process increases the hydrophilicity and homogeneity and contributes to increasing the speed of biodegradation. The biocomposite is entirely self-supporting, provides primary nutrients for soil nourishment, and is observed to be completely biodegradable when buried in the soil within 90 days. Among temperatures tested for superheated water hydrolysis of raw wool, 150 °C seems to be the most appropriate for the biocomposite preparation regarding physicochemical properties of wool and suitability for wool mixing with cellulose. The combination of a sustainable hydrolysis process and the use of waste wool in manufacturing an eco-friendly, biodegradable paper/biocomposite will open new potential opportunities for the utilization of waste wool in agricultural and packaging applications and minimize environmental impact.
A meta-analysis of semantic classification of citations

Abstract The aim of this literature review is to examine the current state of the art in the area of citation classification. In particular, we investigate the approaches for characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, preprocessing methods, extraction of contextual and noncontextual features, and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process, and the existing research gaps in this field.
Point Cloud Registration Based on Multiparameter Functional

The registration of point clouds in a three-dimensional space is an important task in many areas of computer vision, including robotics and autonomous driving. The purpose of registration is to find a rigid geometric transformation to align two point clouds. The registration problem can be affected by noise and partiality (two point clouds only have a partial overlap). The Iterative Closed Point (ICP) algorithm is a common method for solving the registration problem. Recently, artificial neural networks have begun to be used in the registration of point clouds. The drawback of ICP and other registration algorithms is the possible convergence to a local minimum. Thus, an important characteristic of a registration algorithm is the ability to avoid local minima. In this paper, we propose an ICP-type registration algorithm (λ-ICP) that uses a multiparameter functional (λ-functional). The proposed λ-ICP algorithm generalizes the NICP algorithm (normal ICP). The application of the λ-functional requires a consistent choice of the eigenvectors of the covariance matrix of two point clouds. The paper also proposes an algorithm for choosing the directions of eigenvectors. The performance of the proposed λ-ICP algorithm is compared with that of a standard point-to-point ICP and neural network Deep Closest Points (DCP).