Miracle-dz/UniFAR-FTUs huggingface dataset:
Adopting and using geospatial technologies for teaching geography in Latin American higher education

ABSTRACT The article presents the results of a regional study analysing the factors that predict Latin American faculty adoption and use of geospatial technologies (GST) for teaching geography. The research involved an online survey with 337 participants from 17 countries, who answered a set of items adapted from the Unified Theory of Acceptance and Use of Technology on five GST – desktop GIS, web-based GIS, remote sensing, GPS, and digital globes. The data was analysed using a structural equation modelling. The results confirmed that faculty who identified pedagogical benefits of using GST considered technologies as easy to master, and valued the opinion of people influential to them about using technologies were more likely to be motivated to use GST for teaching. In addition, faculty willing to use the GST and report adequate institutional conditions were more likely to be using the technologies for teaching. Additional statistical testing found that human geography faculty expressed an infrequent use of these technologies compared to their physical geography and geospatial technologies colleagues, who did report a more consistent use of GST. These findings suggest a divide among Latin American faculty regarding the motivation and institutional conditions to implement technologies, based on their field of expertise.
Can We Use the Water Budget to Infer Upland Catchment Behavior? The Role of Data Set Error Estimation and Interbasin Groundwater Flow

Water budgets are essential for characterizing water supplies from snow‐dominated upland catchments where data are sparse, groundwater systems are complex, and measurements are prone to error (ε). One solution is imposing water budget closure (CWB) by ignoring difficult‐to‐measure variables, including inter‐basin groundwater fluxes (G) and ε. However, conventional CWB‐based analyses, which derive evapotranspiration (ET) from precipitation (P) and streamflow (Q) (e.g., the Budyko hypothesis), are limited in their ability to take advantage of recent advances in ET products, physically‐based frameworks for improving inferences about G, or tools to statistically characterize ε (Triple Collocation [TC]); all of which offer promise for improved water supply predictions via open water budgets (OWB). We clarify the value of these advances in upland settings by comparing standard land surface model, Ensemble Mean, and TC‐Merged P and ET products in 114 upland catchments. When compared against a long‐term OWB, we find that the CWB assumptions are unsupported in 75%–100% of our 114 catchments, depending on the product. We then show how applying these CWB assumptions in snowy, steep catchments where ε is large can inflate inferences about streamflow response to climate change by up 9 times more than independent (OWB) estimates of ET using TC. Finally, we demonstrate how advances in OWB analysis reveal that high, arid settings with deep permeable substrate are groundwater exporters while most other basins are groundwater importers. Our results highlight the advantages of OWB analyses that harness new products, tools, and frameworks for characterizing inter‐basin groundwater fluxes in critical upland settings.
Drastic decline of extensive grassland species in Central Europe since 1950: Forester moths of the genus Jordanita (Lepidoptera, Zygaenidae) as a type example

Abstract The decline of biodiversity in general and of insect diversity in particular has been recognized as a major environmental problem in recent years. In this study, we analyze the distribution and the decline of populations of forester moths of the genus Jordanita in Central Europe since 1950 as a type example of the loss of grassland biodiversity, and discuss potential drivers causing this decline. Based on the extensive work in museums and private collections, a literature review and own observations, and including data as far back as 1834, this genus helps to understand the deeper reasons of insect population and biodiversity decline, as the well investigated six Central European species cover a broad range of extensive grassland habitats (fens to low‐production grassland and xerothermic steppes) from low altitudes to high alpine meadows. Therefore, they monitor processes relevant also to other, less investigated grassland species. Although there are differences in research intensity over time and in different natural areas, we show that in the whole of Central Europe, the populations of all six investigated Jordanita species broke down massively in the past decades, both in terms of number of populated habitats (about 400 recorded localities after the year 2000 compared with a total number of about 1600 at all times, cumulated for all six species) and in terms of number of individuals. On the other hand, some natural areas on a regional scale have more or less maintained their Jordanita populations, due to conservative land use and due to the early implementation of conservation and protection management plans. The reasons of decline are manifold and monitored in detail by the different species with their different habitat requirements. They comprise (1) loss of habitats due to land use changes (both intensification and abandonment), (2) loss of habitats due to urbanization and construction work, (3) loss of habitat networks to cope with small‐scale extinction events, (4) more intensive growth of grass at the expense of other plants in otherwise undisturbed habitats due to fertilization through the air (increased nitrogen levels due to human activities) and (5) use of pesticides.
A cancer stem cell associated gene signature for predicting overall survival of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent type of primary liver cancer characterized by high mortality and morbidity rate. The lack of effective treatments and the high frequency of recurrence lead to poor prognosis of patients with HCC. Therefore, it is important to develop robust prediction tools for predicting the prognosis of HCC. Recent studies have shown that cancer stem cells (CSC) participate in HCC progression. The aim of this study was to explore the prognostic value of CSC-related genes and establish a prediction model based on data from The Cancer Genome Atlas (TCGA) database. In this study, 475 CSC-related genes were obtained from the Molecular Signature Database and 160 differentially expressed CSC-related genes in HCC patients were identified using the limma R package in the TCGA database. A total of 79 CSC-related genes were found to be associated with overall survival (OS). Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regressions, a 3-gene signature (RAB10, TCOF1, and PSMD14) was constructed. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were constructed to test the prediction performance of the signature. Performance of the signature was validated using the International Cancer Genome Consortium (ICGC) dataset. In addition, immune feature and functional enrichment analyses were carried out to explore the underlying mechanisms. Moreover, a co-expression network was constructed using the weighted gene correlation network analysis (WGCNA) method to select genes significantly associated with risk scores in HCC in the TCGA dataset. The SGO2 gene was found to be significantly associated with risk scores of HCC. In vitro experiments revealed that it can promote HCC cell proliferation. Therefore, SGO2 may be a potential therapeutic target for HCC treatment. The constructed nomogram can help clinicians make decisions about HCC treatment.
Multiple optical traps from a single laser beam using a mechanical element

The use of a wire mesh facilitates creation of multiple optical traps for manipulation of small micron or sub-micron particles. Such an array of optical traps can be easily controlled. The trap that is formed in this manner is a continuous trap; it obviates the need to time share a laser beam among a set of positions, as is presently done in conventional multiple traps.
Application of Fuzzy Math Simulation to Quantitative Risk Assessment in Pork Production

The objective of this study was to evaluate the use of fuzzy math strategy to calculate variability and uncertainty in quantitative risk assessment. We compared the propagation of uncertainty using fuzzy math simulation with Monte Carlo simulation. The risk far Listeria monocytogenes contamination was estimated for carcass and processed pork by fuzzy math and Monte Carlo simulations, respectively. The data used in these simulations were taken from a recent report on pork production. In carcass, the mean values for the risk from fuzzy math and Monte Carlo simulations were -4.393 log and -4.589 log , respectively; in processed pork, they were -4.185 log and -4.466 log respectively. The distribution of values obtained using the fuzzy math simulation included all of the results obtained using the Monte Carlo simulation. Consequently, fuzzy math simulation was found to be a good alternative to Monte Carlo simulation in quantitative risk assessment of pork production.
AE 6: A model environment of trapped electrons for solar maximum

A projected inner zone electron model environment, AE 6, for the epoch 1980 is presented. It is intended to provide estimates of mission fluxes that spacecraft will encounter in the coming solar maximum years. AE 6 is presented by graphs of omnidirectional integral flux as a function of L shell, the ambient magnetic field B, and the energy E. Results of orbital integrations for altitudes from 150 n.m. to 18,000 n.m. are given for circular orbits with four different inclinations, using the AE 6 and the AE 4 solar maximum models for the inner and outer zones, respectively. The derivation of AE 6 is described, and a brief comparison is given of the radial profiles of equatorial fluxes from several related models. A short summary of the associated computer programs is included. (auth)
A Search for Z=-1 Dark Matter Annihilation Products in Cosmic Rays with AMS-01

The majority of mass in the universe has not been observed optically and is termed dark matter. The supersymmetric neutralino provides an interesting dark matter candidate, which may self-annihilate in our galaxy, producing particles visible in the cosmic ray spectrum. During a ten day space shuttle flight, the AMS-01 detector recorded over 100 million cosmic ray events. This analysis searches for the products of neutralino annihilation in the AMS-01 Z=-1 spectrum, and uses the results to place limits on which supersymmetric and dark matter halo distribution models are compatible.
Tratamento de Lesões de carie em dentina e analise do perfil microbiano associado

O fator mais importante para o desenvolvimento de lesoes de carie e a atividade metabolica do biofilme sobre os tecidos dentais. A partir desta definicao e do desenvolvimento de novos materiais restauradores, uma abordagem mais conservadora sobre o tratamento de lesoes de carie em dentina vem sendo pesquisada. Capitulo 1 – Objetivo: Comparar os resultados do acompanhamento clinico e radiografico apos 24 meses, de molares permanentes com lesoes de carie em dentina, com extensao igual ou maior que o terco medio, apos a remocao total ou parcial de dentina cariada. Metodologia: 18 molares permanentes foram estudados de 11 adolescentes com idade entre 12 a 17 anos. Os dentes foram randomizados em dois grupos com 9 voluntarios em cada um deles. No grupo experimental foi realizada a remocao parcial de dentina cariada e o forramento da cavidade com cimento de ionomero de vidro, e no grupo controle foi realizada a remocao total de dentina cariada e forramento com hidroxido de calcio e ionomero de vidro. Ambos os tratamentos foram realizados em uma unica sessao e o material restaurador utilizado foi a resina composta fotopolimerizavel. Os dentes foram avaliados por meio de testes de vitalidade pulpar e exame radiografico durante dois anos. Resultados: Nenhum dente estudado apresentou imagem radiografica sugerindo lesao periapical em ambos os grupos, e nenhum voluntario sentiu dor sem estimulo durante o tempo de acompanhamento. Conclusao: Conclui-se que a manutencao de dentina infectada sob restauracoes pode ser indicada em dentes permanentes de adolescentes e o tratamento pode ser realizado em uma unica sessao sem a necessidade de reabertura do dente. Capitulo 2 – Objetivo: comparar o perfil microbiano de lesoes de carie em dentina nao detectadas clinicamente (NCD) e de lesoes detectadas clinicamente (CD), por meio da tecnica de Reacao em Cadeia da Polimerase baseada na Eletroforese em Gel com Gradiente Desnaturante (PCR-DGGE). Metodologia: 8 amostras de dentina cariada nao detectadas clinicamente (NCD) e 8 detectadas clinicamente (CD), coletadas previamente ao procedimento restaurador (Capitulo 1), foram analisadas pela tecnica de PCR–DGGE. Lesoes em dentina sob esmalte integro foram consideradas lesoes nao detectadas clinicamente. Foi realizada a extracao de DNA e em seguida a PCR com um conjunto de primers universais do 16S rRNA. Os fragmentos do 16S rDNA amplificados pela PCR foram separados pelo gel de DGGE. Os perfis microbianos foram comparados utilizando os programas Bionumerics e Primer5. Resultados: A heterogeneidade dos perfis demonstrou a nitida separacao entre os grupos com baixa de similaridade entre eles (45% R>0,5). Nao houve diferenca estatisticamente significante entre a media do numero de amplicons (bandas) das lesoes NCD e CD (p=0,40). Conclusao: Os resultados sugerem que existem especies bacterianas claramente diferenciadas entre lesoes NCD e CD, porem com algumas sobreposicoes entre elas, e lesoes de carie CD possuem uma microbiota tao complexa quanto as lesoes NCD. Abstr
Dependence of the optical brightness on the gamma and X-ray properties of GRBs

The Swift satellite made a real break through with measuring simultaneously the gamma X-ray and optical data of GRBs, effectively. Although, the satellite measures the gamma, X-ray and optical properties almost in the same time a significant fractions of GRBs remain undetected in the optical domain. In a large number of cases only an upper bound is obtained. Survival analysis is a tool for studying samples where a part of the cases has only an upper (lower) limit. The obtained survival function may depend on some other variables. The Cox regression is a way to study these dependencies. We studied the dependence of the optical brightness (obtained by the UVOT) on the gamma and X-ray properties, measured by the BAT and XRT on board of the Swift satellite. We showed that the gamma peak flux has the greatest impact on the afterglow's optical brightness while the gamma photon index and the X-ray flux do not. This effect probably originates in the energetics of the jet launched from the central engine of the GRB which triggers the afterglow.
Recent progress in thin film processing by magnetron sputtering with plasma diagnostics

The precise control of the structure and related properties becomes crucial for sophisticated applications of thin films deposited by magnetron sputtering in emerging industries including the flat panel display, digital electronics and nano- and bio-industries. The film structure is closely related to the total energy delivered to the substrate surface for nucleation and growth during all kinds of thin film processes, including magnetron sputtering. Therefore, the energy delivered to the surface for nucleation and growth during magnetron sputtering should be measured and analysed by integrated diagnostics of the plasma parameters which are closely associated with the process parameters and other external process conditions. This paper reviews the background of thin film nucleation and growth, the status of magnetron sputtering technology and the progress of plasma diagnostics for plasma processing. The evolution of the microstructure during magnetron sputtering is then discussed with respect to the change in the process variables in terms of the plasma parameters along with empirical data of the integrated plasma diagnostics for various magnetron sputtering conditions with conventional dc, pulsed dc and high power pulsed dc sputtering modes. Among the major energy terms to be discussed are the temperature change in the top surface region and the energies of ions and neutral species.
Dual-Peak Lorentzian CEST MRI for antiretroviral drug brain distribution

Abstract Objectives Spatial–temporal biodistribution of antiretroviral drugs (ARVs) can now be achieved using MRI by utilizing chemical exchange saturation transfer (CEST) contrasts. However, the presence of biomolecules in tissue limits the specificity of current CEST methods. To overcome this limitation, a Lorentzian line-shape fitting algorithm was developed that simultaneously fits CEST peaks of ARV protons on its Z-spectrum. Case presentation This algorithm was tested on the common first line ARV, lamivudine (3TC), that has two peaks resulting from amino (–NH2) and hydroxyl (–OH) protons in 3TC. The developed dual-peak Lorentzian function fitted these two peaks simultaneously, and used the ratio of –NH2 and –OH CEST contrasts as a constraint parameter to measure 3TC presence in brains of drug-treated mice. 3TC biodistribution calculated using the new algorithm was compared against actual drug levels measured using UPLC-MS/MS. In comparison to the method that employs the –NH2 CEST peak only, the dual-peak Lorentzian fitting algorithm showed stronger correlation with brain tissue 3TC levels, signifying estimation of actual drug levels. Conclusions We concluded that 3TC levels can be extracted from confounding CEST effects of tissue biomolecules resulting in improved specificity for drug mapping. This algorithm can be expanded to measure a variety of ARVs using CEST MRI.
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations

Recent studies show the benefits of reformulating common machine learning models through the concept of prototypes – representatives of the underlying data, used to calculate the prediction score as a linear combination of similarities of a data point to prototypes. Such prototype-based formulation of a model, in addition to preserving (sometimes enhancing) the performance, enables explainability of the model’s decisions, as the prediction can be linearly broken down into the contributions of distinct definable prototypes. Following this direction, we extend the idea of prototypes to the recommender system domain by introducing ProtoMF, a novel collaborative filtering algorithm. ProtoMF learns sets of user/item prototypes that represent the general consumption characteristics of users/items in the underlying dataset. Using these prototypes, ProtoMF then represents users and items as vectors of similarities to the corresponding prototypes. These user/item representations are ultimately leveraged to make recommendations that are both effective in terms of accuracy metrics, and explainable through the interpretation of prototypes’ contributions to the affinity scores. We conduct experiments on three datasets to assess both the effectiveness and the explainability of ProtoMF. Addressing the former, we show that ProtoMF exhibits higher Hit Ratio and NDCG compared to other relevant collaborative filtering approaches. As for the latter, we qualitatively show how ProtoMF can provide explainable recommendations and how its explanation capabilities can expose the existence of statistical biases in the learned representations, which we exemplify for the case of gender bias.
Emotion Ontology Studies: A Framework for Expressing Feelings Digitally and its Application to Sentiment Analysis

Emotion ontologies have been developed to capture affect, a concept that encompasses discrete emotions and feelings, especially for research on sentiment analysis, which analyzes a customer's attitude towards a company or a product. However, there have been limited efforts to adapt and employ these ontologies. This research surveys and synthesizes emotion ontology studies to develop a Framework of Emotion Ontologies that can be used to help a user select or design an appropriate emotion ontology to support sentiment analysis and increase the user's understanding of the roles of affect, context, and behavioral information with respect to sentiment. The framework, which is derived from research on emotion ontologies, psychology, and sentiment analysis, classifies emotion ontologies as discrete emotion or one of two hybrid ontologies that are combinations of the discrete, dimensional, or componential process emotion paradigms. To illustrate its usefulness, the framework is applied to the development of an emotion ontology for a sentiment analysis application.
Two-Layer Bandit Optimization for Recommendations

Online commercial app marketplaces serve millions of apps to billions of users in an efficient manner. Bandit optimization algorithms are used to ensure that the recommendations are relevant, and converge to the best performing content over time. However, directly applying bandits to real-world systems, where the catalog of items is dynamic and continuously refreshed, is not straightforward. One of the challenges we face is the existence of several competing content surfacing components, a phenomenon not unusual in large-scale recommender systems. This often leads to challenging scenarios, where improving the recommendations in one component can lead to performance degradation of another, i.e., “cannibalization”. To address this problem we introduce an efficient two-layer bandit approach which is contextualized to user cohorts of similar taste. We mitigate cannibalization at runtime within a single multi-intent content surfacing platform by formalizing relevant offline evaluation metrics, and by involving the cross-component interactions in the bandit rewards. The user engagement in our proposed system has more than doubled as measured by online A/B testings.
Fighting False Information from Propagation Process: A Survey

The recent serious cases of spreading false information have posed a significant threat to the social stability and even national security, urgently requiring all circles to respond adequately. Therefore, this survey illustrates how to fight against false information from its propagation process by (1) exploring the drivers of information infectivity from the content, media, user, structural, and temporal dimensions; (2) describing the propagation modeling approaches from macro (global), meso (community), and micro (individual) levels; and (3) discussing the governance strategies from both technical and application aspects. The potential data sources and the future directions of fighting are also given, hoping to facilitate more comprehensive solutions.
Multiobjective Evaluation of Reinforcement Learning Based Recommender Systems

Movielens dataset has become a default choice for recommender systems evaluation. In this paper we analyze the best strategies of a Reinforcement Learning agent on Movielens (1M) dataset studying the balance between precision and diversity of recommendations. We found that trivial strategies are able to maximize ranking quality criteria, but useless for users of the recommendation system due to the lack of diversity in final predictions. Our proposed method stimulates the agent to explore the environment using the stochasticity of Ornstein-Uhlenbeck processes. Experiments show that optimization of the Ornstein-Uhlenbeck process drift coefficient improves the diversity of recommendations while maintaining high nDCG and HR criteria. To the best of our knowledge, the analysis of agent strategies in recommendation environments has not been studied excessively in previous works.