Miracle-dz/UniFAR-FTUs huggingface dataset:
一項研究在伯利茲的紅樹林毀林從可樂華橋席本河 利用地理信息系統和遙感分析 ; A Study of Mangrove Deforestation in Belize from Haulover Bridge to the Sibun River Using GIS and Remote Sensing Analysis

Mangrove forests lie along the coast of Belize and one of the largest concentrations of mangroves is located around Belize City. Mangrove forests in this area consists of three types which are mainly short red mangrove (Rhizophora Mangle), medium black mangroves (Avicennia Germinans) and tall white mangroves (Laguncularia Racemosa) although the existence of buttonwood (Conocarpus erectus) can be found along the boundaries of wetlands, streams or swamps deeper inland. Mangrove forests on the coast of Belize provide estuaries for fishes and sanctuaries for birds. Mangrove forests of the study area are located within the Belize and Sibun river water sheds. The mangrove ecosystem in this area faces the threat of deforestation from rapid urban development, land reclamation, increase in tourism and natural disasters (global warming). Other major threats include changes in rainfall patterns which could affect distribution and species composition because rainfall regulates salt concentrations in soil and plants, as well as providing a source of fresh water for the mangroves (Neal et al., 2008). The area being studied lies between Haulover Bridge on the northern boundary and Sibun River on the southern boundary which covers an area of approximately 9495.52 hectares. The objective of this study is to use GIS and remote sensing analysis of December 27th, 1989, January 24th, 2000 and January 11th, 2010 Landsat images to verify that mangrove deforestation has been occurring over a period of 20 years. The method of unsupervised classification was applied to this study. Temporal analysis by using change detection was carried out to monitor change detection. Overall classification accuracy of 80% was obtained for 1989 Landsat image, 77% overall classification accuracy was obtained for 2000 Landsat image and 74% overall classification accuracy was obtained for 2010 Landsat image. Results obtained for temporal analysis by using change detection showed that mangrove forests declined by an average of 442 hectares (26%) over a 20 year period. During 1989 to 2000 mangrove forests declined by 207 hectares (11%) and during 2000 to 2010 mangroves declined by an average of 235 hectares (15%). Over a period of 20 years short mangroves increased by 86 hectares (5%), medium mangroves decreased by 857 hectares (49%) and tall mangroves decreased by 557 hectares (34%). This study shows that mangrove forest is rapidly declining and needs to be addressed urgently to deter further destruction on this valuable habitat.
Basic information on the sugarcane plant

Sugarcane is a major world crop supplying sugar and energy. This highly efficient crop from the grass family is grown in tropical and subtropical environments globally. The genome of modern cultivated sugarcane is large and complex originating from hybrids between two wild polyploid relatives, Saccharum officinarum and Saccharum spontaneum. This chapter introduces a volume that describes the genetics and genomics of sugarcane and consequences for sugarcane breeding.
A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

Despite the advances in computational literary analysis of Western literature, in-depth analysis of the South Asian literature has been lacking. Thus, social network analysis of the main characters in the Indian epic Mahabharata was performed, in which it was prepossessed into verses, followed by a term frequency–inverse document frequency (TF-IDF) transformation. Then, Latent Semantic Analysis (LSA) word vectors were obtained by applying compact Singular Value Decomposition (SVD) on the term–document matrix. As a novel innovation to this study, these word vectors were adaptively converted into a fully connected similarity matrix and transformed, using a novel locally weighted K-Nearest Neighbors (KNN) algorithm, into a social network. The viability of the social networks was assessed by their ability to (i) recover individual character-to-character relationships; (ii) embed the overall network structure (verified with centrality measures and correlations); and (iii) detect communities of the Pandavas (protagonist) and Kauravas (antagonist) using spectral clustering. Thus, the proposed scheme successfully (i) predicted the character-to-character connections of the most important and second most important characters at an F-score of 0.812 and 0.785, respectively, (ii) recovered the overall structure of the ground-truth networks by matching the original centralities (corr. > 0.5, p < 0.05), and (iii) differentiated the Pandavas from the Kauravas with an F-score of 0.749.
A First Time in Human, Microdose, Positron Emission Tomography Study of the Safety, Immunogenicity, Biodistribution and Radiation Dosimetry of 18 F-FB-A20FMDV2 for Imaging the Integrin αvβ6

The αvβ6 integrin is involved in the pathogenesis of cancer and fibrosis. A radio-labelled 20-amino acid αvβ6-binding peptide, derived from the foot and mouth virus (A20FMDV2), has been developed to image αvβ6 levels pre-clinically. This study was designed to translate these findings into a clinical PET imaging protocol to measure the expression of αvβ6 in humans.
Endothelial glycocalyx sensitivity to chemical and mechanical sub-endothelial substrate properties

Glycocalyx (GCX) is a carbohydrate-rich structure that coats the surface of endothelial cells (ECs) and lines the blood vessel lumen. Mechanical perturbations in the vascular environment, such as blood vessel stiffness, can be transduced and sent to ECs through mechanosensors such as GCX. Adverse stiffness alters GCX-mediated mechanotransduction and leads to EC dysfunction and eventually atherosclerotic cardiovascular diseases. To understand GCX-regulated mechanotransduction events, an in vitro model emulating in vivo vessel conditions is needed. To this end, we investigated the impact of matrix chemical and mechanical properties on GCX expression via fabricating a tunable non-swelling matrix based on the collagen-derived polypeptide, gelatin. To study the effect of matrix composition, we conducted a comparative analysis of GCX expression using different concentrations (60–25,000 μg/mL) of gelatin and gelatin methacrylate (GelMA) in comparison to fibronectin (60 μg/mL), a standard coating material for GCX-related studies. Using immunocytochemistry analysis, we showed for the first time that different substrate compositions and concentrations altered the overall GCX expression on human umbilical vein ECs (HUVECs). Subsequently, GelMA hydrogels were fabricated with stiffnesses of 2.5 and 5 kPa, representing healthy vessel tissues, and 10 kPa, corresponding to diseased vessel tissues. Immunocytochemistry analysis showed that on hydrogels with different levels of stiffness, the GCX expression in HUVECs remained unchanged, while its major polysaccharide components exhibited dysregulation in distinct patterns. For example, there was a significant decrease in heparan sulfate expression on pathological substrates (10 kPa), while sialic acid expression increased with increased matrix stiffness. This study suggests the specific mechanisms through which GCX may influence ECs in modulating barrier function, immune cell adhesion, and mechanotransduction function under distinct chemical and mechanical conditions of both healthy and diseased substrates.
Potential Denitrification Rate in Heavily Fertilized Kuroboku Tea Soil

黒ボク茶園土壌で21年間継続した四要素試験ほ場から対照区,窒素多量区,石灰多量区の土壌試料を採取,好気または嫌気条件下で30℃に7日間保ち,N2,N2O,NOの発生状況を追跡し,次の結果を得た。 1) 好気条件での窒素の揮散は少なく,その速度は0,02～0。 5mgN/1009・dであった。 2) 嫌気条件での脱窒速度は2～4mgN/1009・dで,同じ方法で測定した他の水田および畑土壌のデータと比較すると,かなり高かった。 3) 処理区間で比較すると,嫌気条件下での脱窒速度は窒素多量区＞対照区＞石灰多量区の順で,土壌のpHが高いほうが脱窒速度が高くはならなかった。 4) 初期の生成含窒素ガスの大部分はN2OとNOであった。 そして土壌のNO3-Nが多いほどN2O,NOのN2への還元がおくれた。 また,石灰多量区の土壌ではNOが微量しか生じなかった。 以上の結果から,少なくとも通気が阻害されたとぎには,強酸性の茶園土壌でも脱窒の可能性は充分あると考える。
Effects of Foods of Mesoamerican Origin in Adipose Tissue and Liver-Related Metabolism

Adipose tissue and liver metabolism play a key role in maintaining body homeostasis; therefore, their impairment conduces a pathological state. Nowadays, occidental lifestyle is a common etiological issue among a variety of chronic diseases, while diet is a unique strategy to prevent obesity and liver metabolism impairment and is a powerful player in the treatment of metabolic-related diseases. Mesoamerican foods are rich in bioactive molecules that enhance and improve adipose tissue and liver performance and represent a prophylactic and therapeutic alternative for disorders related to the loss of homeostasis in the metabolism of these two important tissues.
Origin of the current stress field in the western/central Alps: role of gravitational re-equilibration constrained by numerical modelling

Abstract We interpret the strain and stress fields of the western/central Alpine arc on the basis of 2.5D finite element modelling and a recent seismotectonic synthesis. Models have fixed boundary forces and different crustal geometries, so that they respond to buoyancy forces (variations in gravitational potential energies). The seismotectonic regime, characterized by orogen-perpendicular extension in the high topographic core of the belt and local orogenperpendicular compressional/transpressional deformation in the external zones, appears to be very close to the modelled gravitational regime. Rotation of Apulia has a minor effect on the current strain or stress fields of the Alpine realm. Nevertheless, it could help to explain the orogen-parallel dextral faulting that is observed all along external zones, from the northern Valais to the Argentera external crystalline massif. Our results highlight the consequences for the Alpine realm of ongoing convergence between the African and European plates. Our interpretation is that collision is no longer ongoing and that buoyancy-driven stresses dominate the present-day geodynamics of the western/central Alps.
Financial Development and Growth in the Short-and Long-Run

We analyze the relationship between financial development and inter-industry resource allocation in the short-and long-run. We suggest that in the long-run, economies with high rates of financial development will devote relatively more resources to industries with a 'natural' reliance on outside finance, due to a comparative advantage in these industries. By contrast, in the short-run we argue that financial development facilitates the reallocation of resources to industries with good growth opportunities, regardless of their reliance on outside finance. To test these predictions, we use a measure of industry-level 'technological' financial dependence based on the earlier work of Rajan and Zingales (1998), and develop new proxies for shocks to (short run) industry growth opportunities. We find differential effects of these measures on industry growth and composition in countries with different levels of financial development. We obtain results that are consistent with financially developed economies specializing in 'financially dependent' industries in the long-run, and allocating resources to industries with high growth opportunities in the short-run. Zingales for extremely helpful conversations and advice. Economists have long been interested in the role of financial development in resource allocation. The hypothesis that financial development facilitates the efficient allocation of resources dates back to at least Schumpeter (1912), who conjectured that banks identify entrepreneurs with good growth prospects, and therefore help to reallocate resources to their most productive uses. More recently, Levine (1997) describes a number of channels through which financial development may affect allocative efficiency, including information generation, risk-sharing, financing, and monitoring. Rajan and Zingales (1998) point out that allocation may be differentially affected by industry characteristics: those that require a lot of upfront outside financing (relative to generated cash flow), such as drugs and pharmaceuticals (perhaps due to R&D costs), will be less likely to grow in the presence of capital market imperfections than other industries where investment more closely coincides with cash generation. More recently, a number of other researchers have used a similar approach to look at the interaction of various 'fixed' industry characteristics and different aspects of financial development in predicting sectoral growth. In this paper, we suggest that there is an important theoretical distinction in considering the role of financial development on industry growth in the short-and long-run that has heretofore gone largely unrecognized. In the short-run, we emphasize the role of financial institutions in reallocating resources to any industry that has experienced a positive shock to growth opportunities. We …
Distance sampling as an effective method for monitoring 5 feral pigeon ( Columba livia f . domestica ) 6 urban populations 7

13 Abstract Current methods for estimating feral pigeon (Columba livia) population size and 14 for monitoring population trends are mainly based on indices, which according to the 15 current literature on wildlife census methods often produce biased results. Distance 16 Sampling techniques have never been used in this context, even though they could 17 theoretically produce absolute abundance estimates at relatively low costs. The aim of this 18 paper was to investigate the performance of Distance Sampling to census feral pigeons, and 19 to compare these results with those obtained by using Quadrate Counts, a widespread 20 method for monitoring these birds. Surveys were performed in Pisa (Italy) in two different 21 periods of the year 2004 (end of January–beginning of February, and November), which 22 correspond to a minimum (January–February) and a maximum (November) numbers for 23 pigeon populations. We conducted 40 line transects each about 250 m long for Distance 24 Sampling, and 40 250×250 m cells for Quadrate Counts. In both cases, sampling units were 25 randomized in a stratified design. In contrast to Quadrate Counts, Distance Sampling 26 detected the predicted increase of abundance from January–February to November with an 27 acceptable precision and no increase of costs per survey. Even though the possible biases 28 (due to the not rigorously random distribution of transects and to the spiked nature of 29 collected distances) should be further investigated, results suggest that Distance Sampling is 30 a viable and efficient alternative to the traditional methods used to estimate feral pigeons 31 population size and to monitor trends.
J ENA E CONOMIC R ESEARCH P APERS

: The aim of this paper is to analyse the effects of knowledge management on the innovation success of firms in Germany. Using a matching procedure on data from the German Innovation Survey of 2003 (“Mannheim Innovation Panel”), we pair firms applying knowledge management with twin firms with similar characteristics not applying knowledge management. Our focus is on investigating the effects of knowledge management techniques on the economic success of firms with product and process innovations. The results of our matching analysis reveal that firms which apply knowledge management perform better in terms of higher-than-average shares of turnover with innovative products compared to their twins. We do not find a significant effect of knowledge management on the share of cost reductions with process innovation.
Active Datawarehouse Loading by GUI Based ETL Procedure

Over the years, DataWarehousing has gone through a number of evolutions . from a relatively simple reporting database to sophisticated analytical applications such as analyzing customer lifetime values, market basket analyses, potentially defecting customers, fraud patterns, inventory churns, and so on. In all, though, these static sets of data could not give us the most current and recent changes necessary to act upon the results of Business Intelligence analyses. In this paper we present a methodology on how to adapt data warehouse schemas and loading the fresh data with the help of the GUI based ETL [12][13] (Extraction, Transformation and Loading) procedure for Active Data Warehouse. To accomplish this, we use techniques such as table structure replication and query predicate restrictions for selecting data, to enable continuously loading data in the data warehouse with minimum impact in query execution time.
Comparing Single- and Multi-Species Water Quality Modeling Approaches for Assessing Contamination Exposure in Drinking Water Distribution Systems

New software such as EPANET-MSX enables water quality models that account for multiple reactive species in the distribution system. This allows for a more complete analysis of network water quality, including processes such as adsorption and biological inactivation by a disinfectant. For each of these reaction processes, three models are presented: single-specie conservative, single-specie reactions modeled by wall demand or bulk decay, and multi-species. The implications of model selection on the resulting exposure to contaminants that undergo these reaction processes are investigated for a hypothetical intentional contamination event and a simple single pipe system.
A multi-modal table tennis robot system

In recent years, robotic table tennis has become a popular research challenge for perception and robot control. Here, we present an improved table tennis robot system with high accuracy vision detection and fast robot reaction. Based on previous work, our system contains a KUKA robot arm with 6 DOF, with four frame-based cameras and two additional event-based cameras. We developed a novel calibration approach to calibrate this multimodal perception system. For table tennis, spin estimation is crucial. Therefore, we introduced a novel, and more accurate spin estimation approach. Finally, we show how combining the output of an event-based camera and a Spiking Neural Network (SNN) can be used for accurate ball detection.
Rare Event Probability Learning by Normalizing Flows

A rare event is defined by a low probability of occurrence. Accurate estimation of such small probabilities is of utmost importance across diverse domains. Conventional Monte Carlo methods are inefficient, demanding an exorbitant number of samples to achieve reliable estimates. Inspired by the exact sampling capabilities of normalizing flows, we revisit this challenge and propose normalizing flow assisted importance sampling, termed NOFIS. NOFIS first learns a sequence of proposal distributions associated with predefined nested subset events by minimizing KL divergence losses. Next, it estimates the rare event probability by utilizing importance sampling in conjunction with the last proposal. The efficacy of our NOFIS method is substantiated through comprehensive qualitative visualizations, affirming the optimality of the learned proposal distribution, as well as a series of quantitative experiments encompassing $10$ distinct test cases, which highlight NOFIS's superiority over baseline approaches.
TorchProbe: Fuzzing Dynamic Deep Learning Compilers

Static and dynamic computational graphs represent two distinct approaches to constructing deep learning frameworks. The former prioritizes compiler-based optimizations, while the latter focuses on programmability and user-friendliness. The recent release of PyTorch 2.0, which supports compiling arbitrary deep learning programs in Python, signifies a new direction in the evolution of deep learning infrastructure to incorporate compiler techniques in a more dynamic manner and support more dynamic language features like dynamic control flows and closures. Given PyTorch's seamless integration with Python, its compiler aims to support arbitrary deep learning code written in Python. However, the inherent dynamism of Python poses challenges to the completeness and robustness of the compiler. While recent research has introduced fuzzing to test deep learning compilers, there is still a lack of comprehensive analysis on how to test dynamic features. To address this issue, we propose several code transformations to generate test cases involving dynamic features. These transformations preserve the program's semantics, ensuring that any discrepancy between the transformed and original programs indicates the presence of a bug. Through our approach, we have successfully identified twenty previously unknown bugs in the PyTorch compiler and its underlying tensor compiler Triton.
DPATD: Dual-Phase Audio Transformer for Denoising

Recent high-performance transformer-based speech enhancement models demonstrate that time-domain methods could achieve similar performance as time-frequency domain methods. However, time-domain speech enhancement applications typically accept audio inputs that are composed of a large number of time steps, making it challenging to model extremely long sequences and train models to perform adequately. In this paper, we utilize smaller audio chunks as input to achieve efficient utilization of audio information to overcome the aforementioned challenges. We present a dual-phase audio transformer for denoising named DPATD, a novel deep model to organize transformer layers to learn clean audio samples for denoising. Using DPATD, audio input sequences are divided into manageable audio chunks, and these short chunks can be effectively and efficiently processed by our memory-compressed explainable attention. It also converges faster compared to the frequently used self-attention module. Extensive experimental results show that our proposed DPATD model is better than state-of-the-art models.
Virtual reality exposure therapy for social phobia

This thesis presents researches and experiments performed in collaboration with a psychiatrist in order to validate and improve the use of virtual reality in social phobia psychotherapy. Cognitive and behavioral therapies are strongly based on the exposure to anxiety provoking stimuli. Virtual reality seems to be appropriate for such exposures as it allows for on-demand reproduction of reality. The idea has been validated for the treatment of various phobias but is more delicate in the case of social phobia; whereas the sense of presence provoked by the immersion in a virtual environment supports the emergence of fears linked to a location, we had to verify that we can reproduce social phobia related anxiety-provoking stimuli by simulating virtual humans. Therefore, and in order to provide therapists with an efficient virtual reality system dedicated to the exposure to social situations, we have developed software solutions supporting different immersion setups and enabling realistic simulations of inhabited virtual environments. We have experimented with public speaking scenarios within a preliminary study, three clinical case studies and a validation study on 200 subjects. We have been able to confirm that our virtual reality platform fulfilled therapeutic exposure requirements for social phobia. Moreover, we have been able to show that virtual reality exposure has additional advantages such as the possibility to improve clinical assessment with embedded monitoring tools. Our experiments with physiological measurements and eye tracking technology during immersion leaded to the validation of systems for objective and reliable assessment of patients' safety behaviors. The observation of such phobic reactions has confirmed the simulation impact and may provide therapists with enhanced pathological progression monitoring. During our experiments, we have also been able to observe that subjects' reactions during immersion were so much influenced by their sensitivity to fearful stimuli that their cognitive reactions were 'overloaded' by the arousal of anxiety and emotions. This has allowed us to consider that the sense of presence was more importantly related to the subjective impact of the content than to the technological process.