Miracle-dz/UniFAR-FTUs huggingface dataset:
RELACIONES ENTRE LOS MERCADOS HIPOTECARIO Y DE DEUDA PÚBLICA: EVIDENCIA EMPÍRICA EN EL CASO ESPAÑOL

The development of the Spanish mortgage market during the last decade has increased the concern about its integration with capital markets. In this context, this paper examines from an empirical perspective the hypothesis of integration between the primary mortgage market and the capital market in the Spanish case, the latter represented by the debt public market. With this aim, we use different time series analysis procedures in a cointegration context. The results show the validity of the initial hypothesis in Spain and suggest that the long term public debt market has a significant causal influence over the mortgage market, particularly over the mortgage indexes that represent the cost of mortgages. El importante desarrollo del mercado hipotecario espanol durante la ultima decada ha generado un creciente interes por la problematica de su integracion financiera con el mercado de capitales. En este contexto, el presente trabajo se centra en la contrastacion empirica en el caso espanol de la hipotesis de integracion entre el mercado primario hipotecario y el mercado de capitales, representado por el mercado de deuda publica anotada. Con tal fin, se utilizan diversos procedimientos econometricos de analisis de series temporales en un marco de cointegracion. Los resultados obtenidos muestran la validez de la hipotesis de partida en el caso espanol y ponen de relieve que el mercado de deuda publica a largo plazo ejerce una significativa influencia en terminos causales sobre el mercado hipotecario, particularmente relevante en lo que respecta a los indices de referencia hipotecarios representativos del coste en el mercado de los prestamos hipotecarios.
Interactions between habitat fragmentation and invasions: factors driving exotic plant invasions in native forest remnants, West Coast, New Zealand.

xi Acknowledgements xii Declaration xiv Chapter 1 – The effects of habitat fragmentation and biological invasions on biodiversity . 1 1.1 Background to research 1 1.1.1 Effects of habitat fragmentation on biodiversity 1 Edge effects 2 Area effects 4 Effects of landscape context 6 Interactions between edge, area and landscape effects 8 1.1.2 Biological invasions 9 Invasiveness and species traits 11 Invasibility 11 Interactions between invasiveness and invasibility 13 1.1.3 Interactions between habitat fragmentation and invasions 13 1.1. 4 Description of my study system: New Zealand lowland forests 16 History and pattern of deforestation in New Zealand 16 Characteristics of the New Zealand flora 17 Impacts of invasive plants in New Zealand 17 1.2 Thesis objectives 18 1.3 Thesis structure 19 Chapter 2 – Interactions between edge, area and landscape effects on exotic plant invasions in fragmented forests 21 2.
Géo-localisation et poursuite dans un réseau mobile

We propose and evaluate methods for the localization and tracking of non-cooperative targets from passive measurements of its radio emissions. This study is divided into two main parts: the first one concerns about the localization of mobile stations or base stations in wireless communication systems and the second one concerns about tracking a single or several targets moving along a road network in presence of clutter. More specifically, we propose novel algorithms based on statistical inference for the localization and use particle filters for the situations in which road constraints are incorporated into the dynamic model of the target. These issues are studied with the objective to propose a methodology able to take into account errors in the radio measurements as well as a priori information one may have about the target dynamics (tracking case). The result of this research has permitted the development of robust algorithms, some of which are already being used in the industry.
Prediction of FeO Content in Sintering Process Based on Knowledge Distillation

In view of the difficulty in online measurement of FeO content of sinter and the waste of computing resources caused by the deployment of complex models on edge computing devices, this paper proposes a prediction method of FeO content in the sintering process based on knowledge distillation. Firstly, a teacher network with ResNet-50 structure is pre-trained using large dataset and partial sintering image dataset, and some structures are improved on the basis of the original residual network. The student network with ResNet-18 structure is trained by knowledge distillation method, so that the small model learns the supervision information and good generalization ability of the large model. While preventing over-fitting of the model, the accuracy is improved. It is verified by experiments that the model after knowledge distillation is lighter, saves computing resources, and can accurately predict the FeO content in the sintering process, which has industrial practical value.
Spatio-Temporal Graph Neural Networks for Accurate Crime Prediction

As a matter of public safety and resource allocation, crime prediction is of paramount importance. As a result of applying data preprocessing techniques and a graph-based approach, this paper presents a crime prediction model. This study shows a comprehensive analysis of crime incidents reported in Chicago over five years, utilizing several preprocessing steps in preparing the dataset. Irrelevant features are eliminated, missing values are handled, and a new feature is extracted. An algorithm based on the k-nearest neighbors method is proposed for representing crime incidents by constructing a graph representation. Each crime incident is connected to its k nearest neighbors based on spatial coordinates. This study compares graph-based machine learning algorithms and conventional approaches, including logistic regression, decision trees, and the recently related work, Spatial-Temporal Meta-path Guided Explainable Crime Prediction (STMEC). The results reveal that the graph-based model demonstrates superior accuracy, recall, and F1 scores compared to traditional models, notably outperforming the recently related work, STMEC. Based on the current study, the graph-based approach effectively captures crime data's spatial dependencies and patterns. Incorporating additional features and fine-tuning hyperparameters of the model can provide valuable insights into crime prevention strategies and urban planning applications.
Full-Stokes image reconstruction without polarimetric calibration using a FIRNet

The reconstruction accuracy of polarization images is limited by the performance of the modulators such as polarizers/retarders etc. and mostly requires calibration. Mathematical models are usually used to find the optimal modulation conditions, and involve trade-offs between measurement time and accuracy. In this paper, we propose a full-Stokes image reconstruction (FIR) network to reconstruct polarization images from random modulated images. The network is constructed based on a cycled pixel-to-pixel conditional adversarial network, which has clear advantages in learning the mapping from input images to output images. The network with losses that utilize the physical features between the Stokes vectors is trained, and the reconstruction of complete polarization information is achieved without polarimetric calibration by our method. Simulations and experiments demonstrate the network’s wavelength independence and modulation independence, proving the effectiveness and robustness of the FIRNet in this paper.
A hybrid model of CNN-BiLSTM and XGBoost for HVAC systems energy consumption prediction

Predicting energy consumption in Heating, Ventilating and Air Conditioning(HVAC) systems is of great significance for optimizing energy consumption, as well as for energy saving and emission reduction in modern buildings. However, using a single energy consumption prediction method has inherent limitations. In this paper, we propose a new hybrid model of CNN-BiLSTM and XGBoost for predicting energy consumption in HVAC systems. The new model extracts time series features of the data through a CNN network, and simultaneously mines forward and backward information using a BiLSTM network. Then, the information extracted by the two networks is integrated to obtain a pre-training model. Finally, the model is optimized by XGBoost to obtain the final prediction result. Comparing the proposed new model with the prediction results of CNN, LSTM, BiLSTM, CNN-LSTM and CNN-BiLSTM models, it can be concluded that the proposed model has higher accuracy and lower error value. Therefore, the hybrid model shows promising potential for predicting energy consumption in HVAC systems.
Conversion Carbon Capture and Storage Factors in Temperate Human Controlled Wetland

: This paper provides guidance for the quantification and reporting of blue carbon removals in the temperate coastal ecosystems, “Italian valli da pesca” or H.C. W. (Human Controlled Wetland, Lat. 45° Lon. 12°), where some pools as seagrasses, and salt marshes, are highly efficient at capturing and storing carbon dioxide (CO 2 ) from the atmosphere. Halophyte salt marsh plants were found to have a %C on Dry Weight (D.W.) of 32.26 ± 3.91 (mean ± standard deviation), macrophytes 33.65 ± 7.99, seagrasses 29.23 ± 2.23, tamarisk 48.42 ± 2.80, while the first 5 centimetres of wetland mud, on average, had a %C of 8.56 ± 0.94. Like the ISO (International Organization for Standardization) 14064 guideline to quantify the GHG (Greenhouse Gas) emission, we have studied the different conversion factors to be used as a practical tool for measurement the CO 2 sink activity. These factors are essential to calculate the overall carbon reduction in a project located in temperate wetland using a method as the ISO 14064.2, UNI-BNeutral, VCS VERRA or other that will come.
A Review on Failure Analysis of Date Palm Fiber Reinforced Polymer Composites

Date palm fibre (DPF)/polymer composites are being employed extensively in many significant applications recently, including home furnishings, sports equipment, automobile parts, and building insulation systems. The failure analysis of the date palm fibre reinforced polymer composite is the main topic of this review. The failure of the date palm fibre reinforced polymer composite was examined by the researchers in relation to a number of parameters. These variables include the kind of polymer matrix, manufacturing processes, fibre treatment, and composite hybridization with date palm fibre and other fibre. The literature also examined the impact of date palm fiber size on the failure of date palm fibre reinforced polymer composites. Recent research has demonstrated that the chemical treatment of DPF enhances the mechanical characteristics of the polymer/DPF composite and strengthens the link between the polymer and fibre, hence lowering the likelihood of composite failure. Reinforcing polymer matrices with a hybrid of DPF and other fiber improved the composite's mechanical qualities and decreased the chance of failure. DPF may be used to reinforce a wide variety of polymer matrices using various production processes, therefore lowering the likelihood of failure and selecting the optimal polymer composite qualities. As the DPF diameter shrank, the mechanical properties of polymer/DPF composites improved and the likelihood of failure dropped. As the length of the fiber rose to 12 mm, its mechanical properties improved and its likelihood of failure reduced.
Diagnostic potential of energy metabolism-related genes in heart failure with preserved ejection fraction

Background Heart failure with preserved ejection fraction (HFpEF) is associated with changes in cardiac metabolism that affect energy supply in the heart. However, there is limited research on energy metabolism-related genes (EMRGs) in HFpEF. Methods The HFpEF mouse dataset (GSE180065, containing heart tissues from 10 HFpEF and five control samples) was sourced from the Gene Expression Omnibus database. Gene expression profiles in HFpEF and control groups were compared to identify differentially expressed EMRGs (DE-EMRGs), and the diagnostic biomarkers with diagnostic value were screened using machine learning algorithms. Meanwhile, we constructed a biomarker-based nomogram model for its predictive power, and functionality of diagnostic biomarkers were conducted using single-gene gene set enrichment analysis, drug prediction, and regulatory network analysis. Additionally, consensus clustering analysis based on the expression of diagnostic biomarkers was utilized to identify differential HFpEF-related genes (HFpEF-RGs). Immune microenvironment analysis in HFpEF and subtypes were performed for analyzing correlations between immune cells and diagnostic biomarkers as well as HFpEF-RGs. Finally, qRT-PCR analysis on the HFpEF mouse model was used to validate the expression levels of diagnostic biomarkers. Results We selected 5 biomarkers (Chrna2, Gnb3, Gng7, Ddit4l, and Prss55) that showed excellent diagnostic performance. The nomogram model we constructed demonstrated high predictive power. Single-gene gene set enrichment analysis revealed enrichment in aerobic respiration and energy derivation. Further, various miRNAs and TFs were predicted by Gng7, such as Gng7-mmu-miR-6921-5p, ETS1-Gng7. A lot of potential therapeutic targets were predicted as well. Consensus clustering identified two distinct subtypes of HFpEF. Functional enrichment analysis highlighted the involvement of DEGs-cluster in protein amino acid modification and so on. Additionally, we identified five HFpEF-RGs (Kcnt1, Acot1, Kcnc4, Scn3a, and Gpam). Immune analysis revealed correlations between Macrophage M2, T cell CD4+ Th1 and diagnostic biomarkers, as well as an association between Macrophage and HFpEF-RGs. We further validated the expression trends of the selected biomarkers through experimental validation. Conclusion Our study identified 5 diagnostic biomarkers and provided insights into the prediction and treatment of HFpEF through drug predictions and network analysis. These findings contribute to a better understanding of HFpEF and may guide future research and therapy development.
Brain-derived neurotrophic factor – a key player in the gastrointestinal system

Brain-derived neurotrophic factor (BDNF) is highly expressed throughout the gastrointestinal (GI) tract and plays a critical role in the regulation of intestinal motility, secretion, sensation, immunity, and mucosal integrity. Dysregulation of BDNF signalling has been implicated in the pathophysiology of various GI disorders including inflammatory bowel disease, irritable bowel syndrome, functional dyspepsia, and diabetic gastroenteropathy. This review provides a comprehensive overview of BDNF localization, synthesis, receptors, and signalling mechanisms in the gut. In addition, current evidence on the diverse physiologic and patho-physiologic roles of BDNF in the control of intestinal peristalsis, mucosal transport processes, visceral sensation, neuroimmune interactions, gastrointestinal mucosal healing, and enteric nervous system homeostasis are discussed. Finally, the therapeutic potential of targeting BDNF for the treatment of functional GI diseases is explored. Advancing knowledge of BDNF biology and mechanisms of action may lead to new therapies based on harnessing the gut trophic effects of this neurotrophin.
in patients

Purpose X-ray coronary angiography (XCA) is the current gold standard for the assessment of lumen encroaching coronary stenosis but XCA does not allow for early detection of rupture-prone vulnerable plaques, which are thought to be the precursor lesions of most acute myocardial infarctions (AMI) and sudden death. The aim of this study was to investigate the potential of delayed contrast-enhanced magnetic resonance coronary vessel wall imaging (CE-MRCVI) for the detection of culprit lesions in the coronary arteries. CE-MRCVI allows the selective visualization of culprit lesions in patients immediately after myocardial infarction (MI). The pronounced contrast uptake in ruptured plaques may represent a surrogate biomarker of plaque activity and/or vulnerability.
A study on the forecast quality of the mediterranean cyclones

The main general objective of MEDEX is stated to be the improvement of knowledge and forecasting of cyclones that produce high impact weather in the Mediterranean area. So, for this scope one of the intermediate goals of the project concerns the development of an objective method to evaluate the quality of the forecast of the cyclones. The topic of the present study is to investigate the cyclone's forecast errors in that area and to propose an objective methodology to quantify them. An investigation on the performance of the HIRLAM(INM)-0.5 model in the forecast of cyclonic centres has been done. Databases of analysed and forecasted cyclones for the Western Mediterranean have been used in this study. The "distance" between the analysed and forecasted cyclone has been measured calculating the differences in the value of the parameters chosen to describe them at the sea level surface. Results on the characteristics of the errors are shown. An index constructed by means of these differences has been introduced to evaluate the ability of the model forecasting cyclones, and to quantify it. From this index, two others indexes have been derived in order to discriminate if the forecast has overestimated or underestimated some magnitudes in the description of the cyclone. Three different time forecast ranges, H+12,H+24 and H+48, have been considered to investigate temporal trend in their quality. Finally, to check this methodology, it has been applied to some MEDEX cases.
A água como bem comum: Uma revisão integrativa da literatura internacional

O presente trabalho tem como objetivo analisar o entendimento da água como bem comum em estudos internacionais a partir de uma revisão integrativa. Na metodologia utilizou-se a revisão integrativa da literatura por meio de buscas nas bases de dados Web of Science e Scopus. A princípio foram encontrados 188 trabalhos publicados nos últimos 10 anos. Considerando, contudo, os critérios preestabelecidos, foram analisados 25 artigos, os quais permitiram atender ao objetivo proposto. Nos resultados apresentou-se uma síntese de como os autores têm tratado a água a partir da perspectiva do bem comum na literatura internacional. Além disso, emergiram sete categorias de análise da leitura dos artigos, a saber: 1 – Bens comuns; 2 – Governança hídrica; 3 – Gestão de recursos hídricos; 4 – Direito à água; 5 – Privatização da água; 6 – Movimentos sociais e ação coletiva e 7 – Políticas públicas. As categorias revelaram entendimentos e disputas em torno das águas em diversos países e as diferentes perspectivas do bem comum.
The Tolerance Model of Non-Inflammatory Immune Competence in Acute Pediatric Malnutrition: Origins, Evidence, Test of Fitness and Growth Potential

The tolerance model rests on the thesis of a physiologically regulated, albeit unsustainable, systemic attempt to adapt to the catabolic challenge posed by acute prepubescent malnutrition even in its severe forms. The model centers on the immunological component of the attempt, positing reorientation toward a non-inflammatory form of competence in place of the classic paradigm of immunological attrition and exhaustion. The foundation of the model was laid in 1990, and sixteen years later it was articulated formally on the basis of a body of evidence centered on T cell cytokines and interventions with cytokine and hormonal mediators. The benefit originally suggested was a reduced risk of autoimmune pathologies consequent to the catabolic release of self-antigens, hence the designation highlighting immune tolerance. Herein, the emergence of the tolerance model is traced from its roots in the recognition that acute malnutrition elicits an endocrine-based systemic adaptive attempt. Thereafter, the growth of the evidence base supporting the model is outlined, and its potential to shed new light on existing information is tested by application to the findings of a published clinical study of acutely malnourished children. Finally, some knowledge gaps pertinent to the model are identified and its potential for growth consonant with evolving perceptions of immunobiology is illustrated.
Does the Association between Preschool Media Use and Language Difficulties at School Entry Vary by First Language of the Child and Parental Education?

Background: Both media use and social background affect children’s language development. The aim of this study was to explore the association between media use and different aspects of language difficulties (grammar, auditory memory, articulation) and social background (first language (FL), parental education (PE)) in more detail. Methods: We analyzed data from 4015 children from the 2013/14 school entry examination in a Western German city. Media use, FL, and PE were assessed by social history, and language difficulties by sociopediatric screening. We calculated Prevalence Ratios with a 95% Confidence Interval for language difficulties by media use and FL/PE; compared models with and without interaction terms; and estimated the predicted prevalence of language difficulties by media use and FL/PE. Results: Children with non-German FL/low PE had a higher media use and more language difficulties. However, the gradual association between media use and grammatical abilities/recommendation of training was more pronounced in children with German FL and medium/high PE. e.g., especially in the preposition subtest. Conclusions: The association between media use and language difficulties varies regarding different aspects of social background and language difficulties. Still, extensive media use is linked with language difficulties for all children. The media competence of young families remains crucial in times of digitalization.
Relationship Between Work-Related Stress and Accidents: Moderating Role of Safety Factors

Work-related stress has been identified as a risk factor for accidents according to the inadaptability theory of accident causation. The same theory says that increasing adaptability can reduce accidents. So, safety culture can be expected to be a moderator in the relationship between work-related stress and errors or accidents (as a composite construct). A survey was conducted among 431 employees from various industries using the job stress index, safety culture scale, and workplace error-accident history scale with the aim of testing if safety culture mitigates the relationship between workplace stress and errors/accidents. Work-related stress and errors/accidents correlated significantly, r=. 18, p<. 01. However, safety culture was not found to moderate their relationship. The conclusion is that a safety culture may not reduce the accidents caused because of work-related stress, and alternatives need to be sought. However, other studies with more methodological rigor or more objective data are needed to verify this conclusion.