Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing uncertainty in climate change responses of inland fishes: A decision‐path approach

Climate change will continue to be an important consideration for conservation practitioners. However, uncertainty in identifying appropriate management strategies, particularly for understudied species and regions, constrains the implementation of science‐based solutions and adaptation strategies. Here, we share a decision‐path approach to reduce uncertainty in climate change responses of inland fishes to inform conservation and adaptation planning. With the Fish and Climate Change database (FiCli), a comprehensive, online, public database of peer‐reviewed literature on documented and projected climate impacts to inland fishes, users can identify relevant studies and associated management recommendations via geographic regions, response types (i.e., fish assemblage dynamics, demographic, distributional, evolutionary, phenological), fish taxa, and traits (e.g., thermal guilds, feeding type, parental care, habitat type) and use a suite of summary tools to make more informed decisions. For both data‐rich and data‐poor scenarios, we demonstrate that this approach can reduce uncertainty in understanding climate change responses. Using thermal sensitivity as an example, we also establish the utility of FiCli database to address other user‐defined, management‐relevant questions via supplementary analyses. This decision‐path approach can be applied to rapid assessments, management decisions, and policy development and may serve as a model for other conservation decision‐making processes.
Genotoxicity of Graphene-Based Materials

Graphene-based materials (GBMs) are a broad family of novel carbon-based nanomaterials with many nanotechnology applications. The increasing market of GBMs raises concerns on their possible impact on human health. Here, we review the existing literature on the genotoxic potential of GBMs over the last ten years. A total of 50 articles including in vitro, in vivo, in silico, and human biomonitoring studies were selected. Graphene oxides were the most analyzed materials, followed by reduced graphene oxides. Most of the evaluations were performed in vitro using the comet assay (detecting DNA damage). The micronucleus assay (detecting chromosome damage) was the most used validated assay, whereas only two publications reported results on mammalian gene mutations. The same material was rarely assessed with more than one assay. Despite inhalation being the main exposure route in occupational settings, only one in vivo study used intratracheal instillation, and another one reported human biomonitoring data. Based on the studies, some GBMs have the potential to induce genetic damage, although the type of damage depends on the material. The broad variability of GBMs, cellular systems and methods used in the studies precludes the identification of physico-chemical properties that could drive the genotoxicity response to GBMs.
Evaluation of Calcium Alginate Entrapped Nano Zinc Oxide to Reduce Gaseous Emissions from Liquid Dairy Manure

Direct application of nanoscale zinc oxide (nZnO) particles has shown promising results in controlling gaseous emissions (hydrogen sulfide-H2S, methane-CH4, and carbon dioxide-CO2) from livestock liquid manure under anaerobic storage conditions. However, application of bare (unmodified) nanoparticles (NPs) could raise environmental concerns as their fate and transport are not well documented. Keeping this in mind, an innovative method has been adopted where NPs were entrapped in biopolymer beads that ensured that NPs were not released to the environment and can be recovered. The objective of this research was to evaluate the performance of calcium (Ca)-alginate entrapped nZnO (alginate-nZnO beads) and associated mechanisms involved in controlling gaseous emission from liquid dairy manure. Experiments were conducted in 1 L Erlenmeyer flasks with a working volume of 500 mL, where alginate-nZnO beads containing 3 g L-1 of NPs were applied freely, as well as loosely contained in mosquito net bags. Headspace gas was collected every 2 to 5 days during the 33-day experiment and analyzed for H2S, CH4, and CO2 concentration. Bacterial growth analysis and sorption test were also carried out to see the effect of NPs on microbial survivability and gaseous sorption capability, respectively. The concentration of H2S, CH4, CO2, and total gas production was significantly reduced by 99%, 51%, 27%, and 43%, respectively, when beads were applied freely; while these reductions were 96%, 18%, 14%, and 20%, respectively, when beads were applied loosely in bags (P<0.05). Though both methods performed well, free beads application method outperformed the other application method.
CEO duality and firm performance: A systematic review and research agenda

This paper systematically reviews 314 empirical studies that examine the relationship between board leadership structure and firm performance. The results show that the mixed research findings are due to different firm performance measurements, research designs, sampling practices and approaches of dealing with endogeneity issues. Studies utilizing multiple-country data, multinational companies, small firms or regions covering Africa, the Middle East, Eastern Europe and South America are under-represented. It critiques the methodological weaknesses of some empirical studies and proposes that future researchers should use multiple firm performance measurements, a longitudinal research design and an integrative framework to better control the direct and moderating factors. Future researchers may advance the robustness of research by using multiple theoretical lenses to consider how various governance factors moderate the relationship between board leadership structure and firm performance, particularly the different ownership structures, managerial discretion contexts and national institutional factors.
Recon3D-and constraint-based tissue-speciﬁc metabolic model reconstruction

Figure 1: Overview of the mCADRE algorithm [5]. Since the sequencing and annotation of the human genome, the reconstruction of human genome-scale metabolic models (GEMs) has been of great interest. GEMs allows for an in-depth insight into the human metabolism, which involves thousands of reactions and metabolites. However, being complex organisms, humans embodied diverse cell types with different metabolic functions and phenotypes. Thus, a tissue-specific reconstruction remains a key challenge in the field of computational systems biology. In 2012, Wang et al. developed a novel computational method to build tissue-specific metabolic models, called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE) [5]. mCADRE constructs tissuespecific models based on human gene expression data and metabolic network topology. Similarly, to the already existing state-of-the-art method, namely the Model Building Algorithm (MBA), mCADRE initially assigns a ubiquity score (i.e, how often a gene is expressed) to each reaction establishing two sets of reactions: core and non-core. Based on a threshold, reactions with higher scores are considered as core reactions, while the remaining ones are the non-core reactions. The latter ones are then ranked based on expression data and connectivity to other reactions. One of the main goals of the algorithm is to remove all non-core reactions while keeping the model consistent and able to produce key metabolites at the same time. The elimination is done based on the rankings and in inverse order [6, 5].
Domain Adaptation for Unconstrained Ear Recognition with Convolutional Neural Networks

Ear recognition has gained attention within the biometrics community recently. Ear images can be captured from a distance without contact, and the explicit cooperation of the subject is not needed. In addition, ears do not suffer extreme change over time and are not affected by facial expressions. All these characteristics are convenient when implementing surveillance and security applications. At the same time, applying any Deep Learning (DL) algorithm usually demands large amounts of samples to train networks. Thus, we introduce a large-scale database and explore fine-tuning pre-trained Convolutional Neural Networks (CNN) to adapt ear domain images taken under uncontrolled conditions. We built an ear dataset from the VGGFace dataset by profiting the face recognition field. Moreover, according to our experiments, adapting the VGGFace model to the ear domain leads to a better performance than using a model trained on general image recognition. The efficiency of the trained models has been tested on the UERC dataset achieving a significant improvement of around 9\% compared to approaches in the literature. Additionally, a score-level fusion technique was explored by combining the matching scores of two models. This fusion resulted in an improvement of around 4\% more. Open-set and close-set experiments have been performed and evaluated using Rank-1 and Rank-5 recognition rate metrics
Designing a Diagnosis Instrument to Determine e-Commerce Readiness for Micro and Small Enterprises in Rural Areas

Electronic commerce, as a new way of commercialization, is not only a business decision, since not all companies, nor all consumers in a region, are technologically prepared to adopt electronic commerce. To implement this type of trade, companies should carry out a diagnostic study, both to know if the technological resources are available internally, and to determine if the potential clients are also ready and willing to use the channel. From this perspective, this work seeks to design an instrument that allows diagnosing the e-Commerce readiness of micro and small businesses, and their potential consumers in rural areas. An iterative process to create the diagnosis instruments was used. It involved iterations of design and evaluation stages. Academic experts with extensive experience in instrument design carried out the first evaluation; the second iteration was evaluated in a pilot field study where 6 companies and 10 consumers from similar regions participated. Finally, in a case study in the canton of Río Cuarto de Alajuela, 29 companies and 261 consumers from the region participated in this study. The diagnosis highlights that most of these companies express that they are not technologically prepared to implement e-commerce, while most of the participating consumers indicated that they do feel technologically prepared to use these platforms.
A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

People's lives are influenced by social media. It is an essential source for sharing news, awareness, detecting events, people's interests, etc. Social media covers a wide range of topics and events to be discussed. Extensive work has been published to capture the interesting events and insights from datasets. Many techniques are presented to detect events from social media networks like Twitter. In text mining, most of the work is done on a specific dataset, and there is the need to present some new datasets to analyse the performance and generic nature of Topic Detection and Tracking methods. Therefore, this paper publishes a dataset of real-life event, the Oscars 2018, gathered from Twitter and makes a comparison of soft frequent pattern mining (SFPM), singular value decomposition and k-means (K-SVD), feature-pivot (Feat-p), document-pivot (Doc-p), and latent Dirichlet allocation (LDA). The dataset contains 2,160,738 tweets collected using some seed words. Only English tweets are considered. All of the methods applied in this paper are unsupervised. This area needs to be explored on different datasets. The Oscars 2018 is evaluated using keyword precision (K-Prec), keyword recall (K-Rec), and topic recall (T-Rec) for detecting events of greater interest. The highest K-Prec, K-Rec, and T-Rec were achieved by SFPM, but they started to decrease as the number of clusters increased. The lowest performance was achieved by Feat-p in terms of all three metrics. Experiments on the Oscars 2018 dataset demonstrated that all the methods are generic in nature and produce meaningful clusters.
A Retrospective, Monocentric Study Comparing Co and Secondary Infections in Critically Ill COVID-19 and Influenza Patients

Few data are available on infectious complications in critically ill patients with different viral infections. We performed a retrospective monocentric study including all of the patients admitted to the intensive care unit (ICU) with confirmed COVID-19 (as of 13 March 2020) or Influenza A and/or B infections (as of 1 January 2015) until 20 April 2020. Coinfection and secondary infections (occurring within and after 48 h from admission, respectively) were recorded. Fifty-seven COVID-19 and 55 Influenza patients were included. Co-infections were documented in 13/57 (23%) COVID-19 patients vs. 40/55 (73%) Influenza patients (p < 0.001), most of them being respiratory (9/13, 69% vs. 35/40, 88%; p = 0.13) and of bacterial origin (12/13, 92% vs. 29/40, 73%; p = 0.25). Invasive aspergillosis infections were observed only in Influenza patients (8/55, 15%). The COVID-19 and Influenza patients presented 1 (0–4) vs. 0 (0–4) secondary infections (p = 0.022), with comparable sites being affected (lungs: 35/61, 57% vs. 13/31, 42%; p = 0.16) and causative pathogens occurring (Gram-negative bacteria: 51/61, 84% vs. 23/31, 74%; p > 0.99). The COVID-19 patients had longer ICU lengths of stay (15 (–65) vs. 5 (1–89) days; p = 0.001), yet the two groups had comparable mortality rates (20/57, 35% vs. 23/55, 41%; p = 0.46). We report fewer co-infections but more secondary infections in the ICU COVID-19 patients compared to the Influenza patients. Most of the infectious complications were respiratory and of bacterial origin.
Site Characterization vis-à-vis Probabilistic Seismic Hazard and Disaster Potential Modelling in the Himalayan and Sub-Himalayan Tectonic Ensemble from Kashmir Himalaya to Northeast India at the backdrop of the updated Seismic Hazard of the Indian Subcontinent

The Indian landmass comprising of three broad morphotectonic provinces, namely the Himalaya and Tertiary 10 mobile belts, Indo-Gangetic Foredeep and Peninsular shield have been jolted time and again by catastrophic earthquakes. The Socio-economic Risk Map of India generated by integrating vulnerable exposures with the IBC-compliant surface-consistent Probabilistic Seismic Hazard through an Analytic Hierarchy Process and expert judgement places the entire Himalayan stretch comprising of Kashmir Himalaya, Northwest India, Nepal together with Indo-Gangetic Foredeep, Bengal Basin, Darjeeling-Sikkim Himalaya, Northeast India and Bhutan in ‘High’ to ‘Severe’ Risk 15 regime thus presenting this Tectonic Ensemble a typical case for site-specific study. Combined surface and downhole Geophysical and Geotechnical measurements classify this Tectonic Ensemble into site classes F/E, D4, D3, D2, D1, C4, C3, C2, C1, B and A with spectral site amplifications of 6.2, 4.8, 4.2, 3.9, 3.3, 2.58, 2.2, 1.87, 1.81, 1.4 and 1.2 respectively at 0.73 - 8.5 Hz frequency range thus facilitating surface-consistent probabilistic seismic hazard assessment in this Tectonic Ensemble exhibiting a PGA variation of 0.06 to 1.99g whose structural impact is exhibited through 20 SELENA-based building damage modelling using capacity spectrum method on the prevalent building types as ‘none’, ‘slight’, ‘moderate’, ‘extensive’ and ‘complete’ for all cities in the Ensemble. Models seismicity model assigned of 0.36 the respective ±1 standard deviation assigned weight 90 equal to 0.32. Similar weight allotments for M max as the GMPEs and NGAs have gone through efficacy test using log-likelihood, computations for ranking and weight assignments. The probability density functions for the magnitudes, epicentre distances and GMPEs follow standard probability density functions as used and Thingbaijam hybrid seismic hazard-disaster model for pre-disaster preparedness in the form of updated urban by-laws and post-disaster rehabilitation and future disaster management.
Future teachers' communicative culture forming by means of drama-based pedagogy

© Serials Publications. The relevance of the paper is conditioned by the transformation of the educational paradigm, from the "educated person" to the "man of a culture ", who integrates different cultures, is oriented to dialogue with the society, capable of creating the image of the world and is able its own image in this world. The purpose of the paper is to disclose the technological reserves for the formation of the communication culture of the future teacher as a "man of culture". As such a reserve, the authors define the use of the technologies of drama pedagogy, based on the communicative nature of the theater as a socio-cultural form of creative communication. The authors consider the communication effects of rhetorical theatrical tools: The effect of visual image, the first phrases, information quantum ejection, relaxation, imagination, discussion, ellipse; functions of pedagogical communication according to the laws of the theatrical action; prove the effectiveness of their use in the process of forming the communicative culture of the future teacher. The paper is intended for educators and psychologists dealing with the problems of researching the culture of the future teachers, including their communication culture.
The impacts of secondary ice production on microphysics and dynamics in tropical convection

Abstract. Secondary ice production (SIP) is an important physical phenomenon that results in an increase in the ice particle concentration and can therefore have a significant impact on the evolution of clouds. In this study, idealized simulations of a mesoscale convective system (MCS) were conducted using a high-resolution (250 m horizontal grid spacing) mesoscale model and a detailed bulk microphysics scheme in order to examine the impacts of SIP on the microphysics and dynamics of a simulated tropical MCS. The simulations were compared to airborne in situ and remote sensing observations collected during the “High Altitude Ice Crystals – High Ice Water Content” (HAIC-HIWC) field campaign in 2015. It was found that the observed high ice number concentration can only be simulated by models that include SIP processes. The inclusion of SIP processes in the microphysics scheme is crucial for the production and maintenance of the high ice water content observed in tropical convection. It was shown that SIP can enhance the strength of the existing convective updrafts and result in the initiation of new updrafts above the melting layer. Agreement between the simulations and observations highlights the impacts of SIP on the maintenance of tropical MCSs in nature and the importance of including SIP parameterizations in models.
Frozen-soil hydrological modeling for a mountainous catchment 1 at northeast of the Tibetan Plateau

: 17 Increased attention directed at frozen-soil hydrology has been prompted by climate 18 change. In spite of an increasing number of field measurements and modeling studies, the 19 impact of frozen-soil on hydrological processes at the catchment scale is still unclear. 20 However, frozen-soil hydrology models have mostly been developed based on a “bottom - 21 up” approach, i.e. by aggregating prior knowledge at pixel scale, which is an approach 22 notoriously suffering from equifinality and data scarcity. Therefore, in this study, we explore 23 the impact of frozen-soil at catchment-scale, following a “top - down” approach , implying: 24 expert-driven data analysis → qualitative perceptual model → quantitative conceptual 25 model → testing of model realism. The complex mountainous Hulu catchment, northeast of 26 the Tibetan Plateau, was selected as the study site. Firstly, we diagnosed the impact of 27 frozen-soil on catchment hydrology, based on multi-source field observations, model 28 discrepancy, and our expert knowledge. Two new typical hydrograph properties were 29 identified: the low runoff in the early thawing season (LRET) and the discontinuous baseflow 30 recession (DBR). Secondly, we developed a perceptual frozen-soil hydrological model, to 31 explain the LRET and DBR properties. Thirdly, based on the perceptual model and a 32 landscape-based modeling framework (FLEX-Topo), a semi-distributed conceptual frozen- 33 soil hydrological model (FLEX-Topo-FS) was developed. The results demonstrate that the 34 FLEX-Topo-FS model can represent the effect of soil freeze/thaw processes on hydrologic connectivity and groundwater discharge and significantly improve hydrograph simulation, 36 including the LRET and DBR events. Furthermore, its realism was confirmed by alternative 37 multi-source and multi-scale observations, particularly the freezing and thawing front in the 38 soil, the lower limit of permafrost, and the trends in groundwater level variation. To the best 39 of our knowledge, this study is the first report of LRET and DBR processes in a mountainous 40 frozen-soil catchment. The FLEX-Topo-FS model is a novel conceptual frozen-soil 41 hydrological model, which represents these complex processes and has potential for wider 42 use in the vast Tibetan Plateau and other cold mountainous regions. 43 DBR events. The FLEX-Topo-FS results were also verified by observed freeze/thaw depth 755 variation, groundwater level, and lower limit of permafrost. We believe this study is able to 756 give us new insights into further implications to understand the impact of frozen soil on 757 hydrology, projecting the impacts of climate change on water resources in vast cold regions, 758 which is one of the 23 major unsolved scientific problems in hydrology community.
Indices of extremes: geographic patterns of change in extremes and associated vegetation impacts under climate intervention

Abstract. Extreme weather events have been demonstrated to be increasing in frequency and intensity across the globe and are anticipated to increase further with projected changes in climate. Solar climate intervention strategies, specifically stratospheric aerosol injection (SAI), have the potential to minimize some of the impacts of a changing climate while more robust reductions in greenhouse gas emissions take effect. However, to date little attention has been paid to the possible responses of extreme weather and climate events under climate intervention scenarios. We present an analysis of 16 extreme surface temperature and precipitation indices, as well as associated vegetation responses, applied to the Geoengineering Large Ensemble (GLENS). GLENS is an ensemble of simulations performed with the Community Earth System Model (CESM1) wherein SAI is simulated to offset the warming produced by a high-emission scenario throughout the 21st century, maintaining surface temperatures at 2020 levels. GLENS is generally successful at maintaining global mean temperature near 2020 levels; however, it does not completely offset some of the projected warming in northern latitudes. Some regions are also projected to cool substantially in comparison to the present day, with the greatest decreases in daytime temperatures. The differential warming–cooling also translates to fewer very hot days but more very hot nights during the summer and fewer very cold days or nights compared to the current day. Extreme precipitation patterns, for the most part, are projected to reduce in intensity in areas that are wet in the current climate and increase in intensity in dry areas. We also find that the distribution of daily precipitation becomes more consistent with more days with light rain and fewer very intense events than currently occur. In many regions there is a reduction in the persistence of long dry and wet spells compared to present day. However, asymmetry in the night and day temperatures, together with changes in cloud cover and vegetative responses, could exacerbate drying in regions that are already sensitive to drought. Overall, our results suggest that while SAI may ameliorate some of the extreme weather hazards produced by global warming, it would also present some significant differences in the distribution of climate extremes compared to the present day.