Miracle-dz/UniFAR-FTUs huggingface dataset:
Geochemical Requirements of the anaerobic oxidation of methane in the Eel River Basin

Although the anaerobic oxidation of methane (AOM) is a widely studied process, many of the geochemical requirements for it remain a mystery, in part because the responsible organisms are not in pure culture. It has been shown that freshwater AOM proceeds with nitrite and nitrate. However, before this study the only known electron acceptor in marine AOM was sulfate. The work of this study helps to illuminate some of the requirements of marine AOM in the Eel River Basin (ERB), CA, focusing on the methane source and electron acceptors which allow for this globally significant process to proceed. In Chapter 2, I use a finite difference thermal history model to indicate areas within the ERB that are capable of thermogenic methane production. Using the model results, I propose a correlation between areas with high rates of hydrocarbon production, methane seep location, and thus the areas within the ERB where high rates of AOM occur. The results of this study not only provide a potential link between geophysics/tectonics and microbiology, but also provide target areas within the ERB that could be used for microbiologic studies. Chapters 3 and 4 are incubation studies, targeted at understanding the role of electron acceptors, using sediment from methane seeps in the ERB. Methane oxidation is monitored by measuring the incorporation of C, from CH4, into the carbon dioxide in the headspace. In Chapter 3, I examine how the rate of AOM changes at varying sulfate concentrations, with a focus on concentrations lower than 1 mM. Although it is often stated that methane oxidation occurs in a 1:1 ratio with sulfate reduction, I find that at these low sulfate concentrations, methane oxidation and
THE ROLE OF PROJECT MANAGEMENT IN THE FORMATION OF HISTORICAL TOURISM ATTRACTIVENESS FACTORS

The paper analyse the possibility of using the project management for reenactment activities as a tool for building attractiveness factors in the counties of Romania according to their historical heritage. A problem that is obvious in Romanian tourism is the low degree of occupation of hotel rooms during the whole year. It is estimated to be around an average of 31.5 % degree of occupation of hotel rooms situating. Romanian tourism is on the last places in Europe and very few attractive from the point of view of profitability. For a good efficiency of putting into practice the solving of the up mentioned problem Project Management will be used for Reenactment Developing Projects in order to create the factors of attractiveness. In the paper we consider the following methods for the valuation of the potential for re-enactment activities and investments in rehabilitation of old historical sites and buildings: The contingent value method, The travel cost method, Hedonic Price Method. In order to relate the creation of attractiveness factors by re-enactment activities and tourism investments we propose an econometric model. This model is useful for the establishing of a relation between BETA variable that quantify the income from re-enactment tourism and the development of the local development due to historical tourism and antique (ancient) souvenir production expressed by four variables. Using statistical model and ANOVA simulation, we link the value of regression parameters.
Orogen-parallel flow during continental convergence: Numerical experiments and Archean field examples

Using triaxial numerical experiments, we investigated the evolution of the state of stress and that of the bulk instantaneous and fi nite strain during ongoing convergence and subsequent progressive tectonic unloading of a warm and buoyant continental lithosphere. Various unloading histories of the driving tectonic force were considered. As the tectonic force progressively declines, the instantaneous strain evolves from plane strain to horizontal constriction in a direction perpendicular to that of convergence, and fi nally to horizontal fl attening. During the progressive unloading of the tectonic force driving convergence, bulk constrictional strain accommodates the release of accumulated gravitational stress. The decline of the triaxial strain rates to low values reduces the potential for the orogen-parallel linear fabric to be erased by horizontal fl attening. This is confi rmed by the fi nite strain ellipsoid that evolves toward plane strain with a long axis parallel to the orogen. In the ca. 2.5 Ga Gawler and Terre Adelie cratons, we have identifi ed a well-preserved and widespread horizontal linear fabric. As suggested by our numerical experiments, we associate the development of this linear fabric with the waning stages of late Archean convergence.
A Fast Non-Linear Coupled Tensor Completion Algorithm for Financial Data Integration and Imputation

Missing data imputation is crucial in finance to ensure accurate financial analysis, risk management, investment strategies, and other financial applications. Recently, tensor factorization and completion have gained momentum in many finance data imputation applications, primarily due to recent breakthroughs in applying deep neural networks for nonlinear tensor analysis. However, one limitation of these approaches is that they are prone to overfitting sparse tensors that contain only a small number of observations. This paper focuses on learning highly reliable embedding for the tensor imputation problem and applies orthogonal regularizations for tensor factorization, reconstruction, and completion. The proposed neural network architecture for sparse tensors, called “RegTensor”, includes multiple components: an embedding learning module for each tensor order, MLP (multilayer perception) to model nonlinear interactions among embeddings, and a regularization module to minimize overfitting problems due to the large tensor rank. Our algorithm is efficient in factorizing both single and multiple tensors (coupled tensor factorization) without incurring high training and optimization costs. We have applied this algorithm in a variety of practical scenarios, including the imputation of bond characteristics and financial analyst EPS forecast data. Experimental results demonstrate its superiority with significant performance improvements: 40%-74% better than linear tensor completion models and 2%-52% better than the state-of-the-art nonlinear models.
Bayesian Denoising of SAR Image

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. The speckle noise is one severe obstacle for automatic mapping from radar images. Image de-speckling is used to remove the multiplicative speckle while retaining as much as possible the important signal features. I. Introduction Synthetic aperture radar systems are all-weather, night and day, imaging systems. Automatic interpretation of information in SAR images is very difficult because SAR images are corrupted by a noise called speckle that arises from an imaging device and strongly hinders data interpretation. The speckle noise in SAR images can be removed using an image restoration technique called despeckling. The goal of despeckling is to remove speckle-noise from SAR images and to preserve all images' textural features. The statistical modeling of SAR images has been intensively investigated over recent years. The SAR imaging technique has become popular because of its usability under varied weather conditions, its ability to penetrate through clouds and soil, and due to the independence of SAR image resolution with regard to sensor height. Since SAR systems rely upon coherence properties of the scattered signals, they are highly susceptible to interference effects. A SAR image is a mean intensity estimate of the radar reflectivity of the region being imaged. The difference between a particular measurement and the true mean value is referred to as speckle noise.
Beauty or brains? The braincase of Pachyrhinosaurus perotorum and its utility for species-level distinction in the centrosaurine ceratopsid Pachyrhinosaurus

ABSTRACT The centrosaurine ceratopsid taxon Pachyrhinosaurus is the most speciose of centrosaurines, being represented by at least three species (P. canadensis, P. lakustai, and the recently described P. perotorum) from the late Campanian and early Maastrichtian of North America. The species are readily distinguished from one another by details of easily visible cranio-facial and frill ornamentation, features commonly used to differentiate ceratopsid taxa. Braincase material is also known for all three taxa. We describe the braincase of P. perotorum based on specimens from the Kikak–Tegoseak Quarry of the North Slope of Alaska. We then compare it to braincase and endocranial descriptions of the other Pachyrhinosaurus taxa to test whether there may be useful species-level differences present in these robust parts of the ceratopsid skull. Braincase morphology, including cranial nerve paths through the braincase walls in P. lakustai and P. perotorum, were found to be very similar. Two potential diagnostic differences between taxa were found, although tests based on larger sample sizes will be necessary to verify them. This reinforces the importance of highly visual cranio-facial and frill ornamentation as the best tool for species recognition and phylogenetic reconstruction in ceratopsid dinosaurs.
Interspecific association of rare and endangered Pinus bungeana community in Xiaolongshan of Gansu

Through field investigation,20 common plant species were selected from 50 plots of Pinus bungeana community in Xiaolongshan to analyze their interspecific associations by using multi-species relevance(variance ratio,VR),χ2-test,Ochiai index,Dice index,Jaccard index,AC value,PC value,Pearson's correlation coefficient,and Spearman's rank correlation coefficient.The VR analysis showed that the associations between the main components of the community were mainly of positive,demonstrating that the nature of the community was consistent with the characteristics of its living environment,and the community was adapted to its outside environment.The species-pair associations among the 20 common species had different properties,which showed the inner environment of the community and was caused by the heterogeneity of the environment within the community.Considering the ecological characteristics of plants,the community was divided into three ecological species groups.It was found that the high proportion of negative association in the community was caused by the shade shrubs under the trees,and the high independence of the community was caused by the plants from the forest edge.The study of interspecific association could reflect the organic link formed by the ecological characteristics of plant species,and the results would have certain practical significance for the protection and restoration of P.bungeana community.
Characterization of maize inbred lines for drought and heat tolerance

Drought and high temperature are two major environmental factors that severely limit plant productivity in the United States and worldwide, often causing extensive economic loss to agriculture. As global climate change progresses, agricultural production worldwide faces serious threats from frequent extreme weather conditions. Integrated approaches that improve the efficiency of agricultural water use and development of plant varieties that can alleviate the negative impacts of environmental stresses to maintain yield stability are essential to sustain and increase agriculture production. Maize (Zea mays L.) is a major crop in the United States and worldwide. Its production and yield stability are greatly affected by drought and high temperature stresses. Improving drought and heat tolerance in maize has become one of the top priorities for maize breeding programs in both private and public sectors. Identification of maize germplasm with superior drought and/or heat tolerance is essential and prerequisite for such propose. In this report, we evaluated a selection of maize inbred lines for drought and heat stress tolerance under field conditions in 2009 and 2010 and identified several inbred lines that showed high tolerance to drought. Tolerant inbred lines (Tx205, C2A554-4, and B76) were able to maintain relatively high leaf relative water content when subjected to drought stress, while sensitive lines (B73 and C273A) showed a rapid reduction in leaf relative water content at very early stage of drought. The tolerant lines also showed significantly greater ability to maintain vegetative growth and alleviate damage to reproductive tissues under drought conditions compared to the sensitive lines. Maize inbred lines and hybrids were also evaluated for tolerance to high temperature under well-watered conditions through field observations following the occurrence of major heat events. Maize inbred lines of distinct heat tolerance phenotype were identified. Furthermore, genetic and phenotypic analysis showed that maize hybrids made from inbred lines with superior heat tolerance inherited an enhanced tolerance to elevated temperatures. The tolerant germplasm accessions, like those identified in this study, are essential materials for breeding drought- and/or heat-tolerant maize hybrids. Study for the potential use of such materials to produce maize hybrids that are able to alleviate the negative impacts of drought and heat stress on the growth and development of maize plants is underway.
Nonparametric Assessment of Aftershock Clusters of the Maule Earthquake Mw = 8.8

so Abstract: We study the spatial distribution of clusters associated to the aftershocks of the megathrust Maule earthquake MW 8.8 of 27 February 2010. We used a recent clustering method which hinges on a nonparametric estimation of the underlying probability density function to detect subsets of points forming clusters associated with high density areas. In addition, we estimate the probability density function using a nonparametric kernel method for each of these clusters. This allows us to identify a set of regions where there is an association between frequency of events and coseismic slip. Our results suggest that high coseismic slip is spatially related to high aftershock frequency.
Ecological disturbance reduces genomic diversity across an Alpine whitefish adaptive radiation

Abstract Genomic diversity is associated with the adaptive potential of a population and thereby impacts the extinction risk of a species during environmental change. However, empirical data on genomic diversity of populations before environmental perturbations are rare and hence our understanding of the impact of perturbation on diversity is often limited. We here assess genomic diversity utilising whole‐genome resequencing data from all four species of the Lake Constance Alpine whitefish radiation. Our data covers a period of strong but transient anthropogenic environmental change and permits us to track changes in genomic diversity in all species over time. Genomic diversity became strongly reduced during the period of anthropogenic disturbance and has not recovered yet. The decrease in genomic diversity varies between 18% and 30%, depending on the species. Interspecific allele frequency differences of SNPs located in potentially ecologically relevant genes were homogenized over time. This suggests that in addition to the reduction of genome‐wide genetic variation, the differentiation that evolved in the process of adaptation to alternative ecologies between species might have been lost during the ecological disturbance. The erosion of substantial amounts of genomic variation within just a few generations in combination with the loss of potentially adaptive genomic differentiation, both of which had evolved over thousands of years, demonstrates the sensitivity of biodiversity in evolutionary young adaptive radiations towards environmental disturbance. Natural history collections, such as the one used for this study, are instrumental in the assessment of genomic consequences of anthropogenic environmental change. Historical samples enable us to document biodiversity loss against the shifting baseline syndrome and advance our understanding of the need for efficient biodiversity conservation on a global scale.
Application of Smart Farming Technologies in Sustainable Agriculture Development: A Comprehensive Review on Present Status and Future Advancements

The pressure on agricultural food production to produce high-quality and sustainable food has increased dramatically as a result of global urbanization, population growth and climate change. Food shortage and population expansion are two of the world's major challenges to long-term sustainable development. So, in recent years technological development such as smart farming has resulted substantial changes in the agricultural production allowing for 3rd green revolution and progression in farming methods. Smart farming (SF) is a method of agricultural production that integrates information and communication technologies to maximise resource efficiency. Smart farming technologies such as automated tractors, artificial agricultural intelligence (AAI), automated irrigation and weather forecasting, Internet of Things (IoT), kisan drones and remote sensing (GIS and GPS) can provide realistic solutions to the world's challenges. The accurate application of this technology in conjunction with the Internet of Things (IoT) is expected to be a beneficial for the farmers to improve their living standards through increased production and profit which is a good indicator for sustainable food security. However, due to lack of internet access, high costs and lack of application knowledge there are still barriers to the adaptation and conversion of smart farming. As a result, more research is needed in this area.
Investigating Approaches for Optimizing Agricultural Yield: A Comprehensive Review of the Crucial Role of Micronutrients in Enhancing Plant Growth and Maximizing Production

Crop requires proper plant nutrition; one of the most important factors governing plant growth and development is the integrated supply of micronutrients and macronutrients in adequate amounts and appropriate proportions; although micronutrients are required in minute quantities, they play an important role in plant growth. Micronutrients must be used wisely in crop production to ensure the optimum output of high-quality products. Plant metabolism, nutrition management, chlorophyll synthesis, reproductive growth, flower retention, and fruit and seed development are all performed by micronutrients. Boron, chlorine, copper, iron, zinc, manganese, molybdenum, and nickel are micronutrients that are required by all higher plants. Chlorine, copper, iron, and manganese are engaged in diverse photosynthetic activities, whereas zinc, copper, iron, and manganese are involved in various metabolic processes. Molybdenum is exclusively found in nitrate reductase. Boron is related with glucose metabolism, plant reproduction, photosynthesis, and enzymatic activity. The review focuses on the primary roles of micronutrients in crop development and gives an overview of recent research discoveries linked to the role of micronutrients in plants, helping to improve knowledge of their importance.
Terminus Ante Quem Constraint of Pueblo Occupation Periods in the Jemez Province, New Mexico

Using dendroecological and archaeological methods and data we investigated the temporal dynamics of forest regeneration and fire history following depopulation of four large Pueblo IV period (1300-1600) villages on the Jemez Plateau, New Mexico. With tree rings we reconstructed the timing of reforestation on village footprints after depopulation – a novel approach to terminus ante quem dating of site occupation. Our tree-ring based forest age structure and fire history chronologies enabled us to reduce by 51 to 70 years the range of previous estimates of village depopulation dates derived primarily from terminal ceramic assemblages. One of the four village sites we investigated was depopulated in 1696, two were depopulated between 1625 and 1700 CE, while the fourth village was depopulated earlier (pre 1500), but the area was likely in continued use for agriculture or other seasonal purposes until the mid1600s. Our results indicate that the Jemez were highly influential ecological agents. Forest structure and fire regime dynamics changed greatly after the departure of most people from these landscapes after circa 1650 CE. The terminus ante quem methods that we demonstrate in the Jemez Mountains have strong potential to constrain and refine low temporal resolution chronologies of human occupation at archaeological sites within other forested ecosystems of the Southwest and elsewhere.
Face Recognition Systems: A Survey of Techniques and Analysis

-Face recognition has recently received a lot of attention as one of the best applications of image analysis and comprehension, especially during the previous considerable amount of time. This tendency can be explained by no fewer than two factors: the first is the wide range of corporate and legal authorization applications, and the second is the accessibility of realisable improvements after 30 years of research. Even though current machine recognition systems have reached a certain level of progress, their success is limited by the constraints imposed by several real-world applications. For instance, it is still quite difficult to accurately identify faces in photos taken outside with changing lighting and/or posture. In the end, present frameworks are still far from ideal. In this paper we have presented a survey and challenges on face recognition systems.
Structural Feature Extraction and Engineering for Sentiment Analysis

The subject of this work is the sentiment analysis of Greek-speaking tweets. We use natural language processing (NLP) methods and neural networks to create three diﬀerent classiﬁcation models. The ﬁrst model processes single, independent tweets and decides if their sentiment is positive or not, or if it is negative or not. The second model considers a tweet paired with its textual context, meaning the tweet that it responds to. With the third neural model we attempt to do sentiment analysis with the tweet, textual context and some additional, structural features, as input. These structural features are extracted from the Twitter graph, and give us information about the authors of the tweet and textual context. Our experiments show that the additional text context improves our prediction by a small percentage in some cases. However, we ﬁnd no correlation between the predicted tweet sentiment and the Twitter graph structural features.
Temporal variation of seismic velocity and anisotropy before the 2009 MW 6.3 L'Aquila earthquake, Italy

We describe a set of seismological observations of the foreshock sequence preceding the 6 April 2009 M w 6.3 L9Aquila earthquake. The dense configuration of the seismic network in the epicenter area and the occurrence of a long foreshock sequence provide the opportunity for a detailed reconstruction of the preparatory phase of the main shock. Approaching the earthquake, we observed clear variations in the seismic wave propagation properties. The elastic properties of rocks in the fault region underwent a sharp change about a week before the earthquake. From our observations, we infer that a complex sequence of dilatancy-diffusion processes takes place and that fluids play a key role in the fault failure process.