Miracle-dz/UniFAR-FTUs huggingface dataset:
MONITORING THE CAMPI FLEGREI CALDERA BY EXPLOITING SAR AND GEODETICAL DATA: RECENT RESULTS AND FUTURE APPLICATIONS

Geodetical monitoring of the Campi Flegrei caldera (Naples, Italy), has been historically carried out by ground networks giving an information related only to a certain number of measuring points; this limitation can be greatly relieved by exploiting the space-borne DInSAR which allows to extract the geodetic information on wide areas, with a good time coverage in comparison with the mean repetition time of the campaign measurements. In this work we will show recent results on Campi Flegrei, obtained by using all the ENVISAT ASAR available data from both ascending and descending orbits. The processed data revealed that the uplift phase of Campi Flegrei, which became very clear in summer 2005 with an average velocity of about 2.8 mm/year, has definitely reduced the uplift velocity since spring 2007. This conclusion is consistent with independent deformation measurements carried out by the Vesuvius Observatory (INGV-OV). Differences, in terms of limits and potentialities of DInSAR with respect to classical geodetic techniques and vice-versa and the way they can be compared/integrated, is still a very interesting matter of debate suggesting, as an optimal solution for monitoring purposes in active volcanic areas, the integration of all the available techniques.
Towards Applications of Deep Learning Techniques to Establish Surrogate Models for the Power Exhaust in Tokamaks

Surrogate Models for the Power Exhaust in Tokamaks M. Brenzke1, S. Wiesen1, M. Bernert2, D.P. Coster2, U. von Toussaint2, EUROfusion MST1 Team3 and the ASDEX Upgrade Team 1 Forschungszentrum Jülich, Institut für Energieund Klimaforschung, 52425 Jülich, Germany 2 Max Planck Institute for Plasma Physics, 85748 Garching, Germany 3 See the author list of H. Meyer et al., Nucl. Fusion 57 (2017) 102014
Innovations in the Next Generation Medical Accelerators for Therapy with Ion Beams

Modern hadron-therapy accelerators have to provide high intensity beams, for innovative dose-delivery modalities such as FLASH, pencil beams for 3D scanning, as well as multiple ions with radio-biological complementarity. They need to be compact, cheap and have a reduced energy footprint. At the same time, they need to be reliable, safe and simple to operate. Cyclotrons and compact synchrotrons are nowadays the standard for proton therapy. For heavier ions such as carbon, synchrotrons remain the most viable option, while alternative solutions based on linacs, FFAs or cyclotrons are being proposed. In this context, the European project HITRIplus studies the feasibility of an innovative super-conducting (SC) magnet synchrotron for carbon ions, with state-of-the-art multi-turn injection from a specially designed linac and advanced extraction modalities. A compact synchrotron optimized for helium ions, making use of proven normal-conducting technology, is also being designed.
Analysis of Speed, Power and Agility of Football Players Reviewed from Age Differences

Physical profile in soccer is one of the important things to determine a training program, but analysis that focuses on aspects of speed, power, and agility based on age categories has not been carried out by coaches. The purpose of this study was to analyze the speed, power, and agility of soccer players in terms of age differences. The research method is quantitative descriptive with a survey approach, participants are soccer players aged 14-15, 16-17, 18-19 years totaling 60 males, test instruments are 30-meter run, 80-meter run, illinois run, and standing broad jump. Survey and test data collection techniques, Kruskall Wallis test data analysis techniques and post hoc tests. The results found a variable speed of 80 meters there was a significant difference in age 14-15 and 18-19 years' sig value of 0.000<0.05, age groups 16-17 and 18-19 years there was a significant difference in sig value 0.013<0.05, while age 14-15 and 16-17 years there is no significant difference sig value 0.042>0.05. The agility variable has a significant difference in age 14-15 and 18-19 years sig value 0.000 <0.05, there is a significant difference in age 14-15 and 16-17 years sig value 0.000 <0.05, while age 16-17 and 18-19 years there is no significant difference sig value 0.458>0.05. It is concluded that chronological age affects the performance obtained, especially agility and speed. Hopefully, this research will be useful for coaches as a basis for evaluation and preparation of training programs. Further research is expected to look at other aspects such as gender differences, body mass index, and differences in player position.
Null model analyses of small mammal community structure in tropical islands

Peer-reviewed papers on the community structure of small mammals (Rodentia and Soricomorpha) from the tropical islands across the world were reviewed. In order to detect non-random patterns in the various assemblages under study, the original datasets were re- analyzed using null models (two independent randomization algorithms for niche overlap) and Monte Carlo simulations. Non-random habitat niche partitioning was rarely observed in rodents, whereas it was the rule in soricomorphs. Aggregated use of habitat resources was occasionally detected in rodents. Thus, in agreement with expectations, our review revealed clear evidence for interspecific competition along the habitat niche axis in tropical island soricomorphs but not in rodents. Resumen: Se revisaron articulos arbitrados sobre la estructura de las comunidades de mamiferos pequenos (Rodentia y Soricomorpha) de islas tropicales en todo el mundo. Para detectar patrones no aleatorios en los diferentes ensambles estudiados, los conjuntos de datos originales fueronreanalizados usando modelos nulos (dos algoritmos independientes de aleatorizacion para el solapamiento de nicho) y simulaciones de Monte Carlo. Entre los roedores se observo muy raramente una reparticion no aleatoria del nicho de habitat, mientras que esta fue la norma en los soricomorfos. Ocasionalmente se detecto para los roedores el uso agregado de los recursos del habitat. Por lo tanto, yen coincidencia con las expectativas, la revision revelo una evidencia clara de la competencia interespecifica a lo largo del eje del nicho de habitat en los soricomorfos de islas tropicales pero no en los roedores. Resumo: Analisaram-se artigos cientificos, revistos por pares, sobre a estrutura da comunidade de pequenos mamiferos (Rodentia e Soricomorpha) nas ilhas tropicais, em todo o mundo. Afim de detectar padroes nao-aleatorios nas varias associacoes em estudo, os conjuntos de dados originais foram reanalisadas usando modelos nulos (dois algoritmos de casualizacao independentes para sobreposicao de nicho) e simulacoes de Monte Carlo. A particao nao- aleatoria do nicho de habitat foi raramente observada em roedores, enquanto que era a regra nas toupeiras. O uso agregado dos recursos do habitat foi ocasionalmente detectado nos roedores. Assim, de acordo com as expectativas, a nossa analise revelou evidencias claras para a competicao interespecifica ao longo do eixo do nicho habitat em toupeiras nas ilhas tropicais, mas nao em roedores.
KDALDL: Knowledge Distillation-Based Adaptive Label Distribution Learning Network for Bone Age Assessment

Deep learning-based bone age assessment (BAA) approaches have certain drawbacks, such as ignoring the correlation of age labels and simply assuming that bone development is linearly related to bone age, which can affect the accuracy of predictions. To solve these problems, a knowledge distillation-based adaptive label distribution learning method called KDALDL is proposed. The KDALDL framework comprises a teacher model and a student model, both consisting of modules for multi-scale feature extraction, feature refinement, and label distribution learning. First, a multi-scale feature extraction module is designed based on the swin transformer to extract feature information at various scales. Subsequently, these features are fed into the feature refinement module to capture the optimal image features. Then, the discrete labels obtained from the age labels through the Gaussian formula are used to train the teacher model. Finally, the teacher model’s outputs are used to train the student model through the knowledge distillation technique, which enables the student model to achieve improved results by learning from the teacher model. The proposed method is validated using the Radiological Society of North America (RSNA) dataset, which exhibits outstanding results.
Predictive Modelling Of Hydrodynamics AndMarine Water Quality: Three Applications AlongThe South African Coastline

The predictive capability of hydrodynamic and water quality models is illustrated by three applications of the DelftSD modelling suite from WL|Delft Hydraulics along the South African coastline. The first application involves dredging-induced turbidity in Saldanha Bay on the west coast. Modelling of the hydrodynamics and the dispersion and deposition of the turbidity plumes was performed to assess the impact of the plumes on the sensitive ecology in the bay. The second application investigates the feasibility of using seawater for industrial cooling at Saldanha Bay. Since this study involves a thermal discharge into a highly stratified environment, the Del A3 D model was applied in three-dimensional baroclinic mode including airsea heat fluxes. The model outputs of absolute temperature, increase in temperature due the thermal discharge and dilution enabled the ecological impacts to be assessed and the optimum discharge location to be identified. The final application is the design of a marine outfall discharging a buoyant effluent near Durban on the east coast. Since the initial dilution process exhibited significant temporal and spatial variability, a dynamic coupling of nearand far-field hydrodynamic models was developed to accurately simulate the impact of the outfall. The results indicated that, under stratified conditions, a local optimum in the required pipeline length with respect to the impact of the effluent at the shore exists the so-called "pipeline paradox".
Introduction to the future of sequence stratigraphy: evolution or revolution?

Sequence stratigraphy was first developed as concepts and methods from the late 19th to mid 20th century by geologists such as Barrell and Sloss. It is now at the forefront of how sedimentary geologists observe, record, model and predict strata. The Geological Society William Smith meeting in Burlington House in September 2014 considered the current state and future developments in sequence stratigraphy, and focused particularly on how the subject should progress, either by evolution or by revolution. We feel that this is a relevant question because of continuing debate, and in some cases controversy, about what sequence stratigraphy is, how we do it and what it can be used for. For example, one of the major hindrances to progress has been the interweaving of description and interpretation, mixing method with model. This has been pointed out and discussed by various researchers (Helland-Hansen & Gjelberg 1994; Burgess & Prince in press). This thematic set of papers presents a sample of the state-of-the-art in sequence stratigraphy, representing how the method and model is now being debated and developed, from a plea to retain the earliest version of the concept to the inclusion of more radical ideas stimulated from experimental stratigraphy.
Risk of New Energy Investment: A Case Study of BYD

Amid diminishing traditional energy sources and escalating environmental challenges, the new energy sector flourishes, presenting numerous investment avenues. However, increased opportunities are often synonymous with heightened risks, necessitating protective measures for investors through robust risk identification and mitigation. This paper leverages the Capital Asset Pricing Model (CAPM), a revered financial analysis instrument, to study investment risks in the emerging new energy realm, spotlighting China's BYD. Employing a decade's SZSC index data and China's risk-free interest rate, the research evaluates BYD's investment landscape across varying phases, notably during the COVID-19 era, and contrasts it with contemporaries like XPENG, NIO, and others. The findings aim to discern the new energy sector's risk portfolio, culminating in actionable insights for prospective investors.
System call frequency analysis-based generative adversarial network model for zero-day detection on mobile devices

In today's digital age, mobile applications have become essential in connecting people from diverse domains. They play a crucial role in enabling communication, facilitating business transactions, and providing access to a range of services. Mobile communication is widespread due to its portability and ease of use, with an increasing number of mobile devices projected to reach 18.22 billion by the end of 2025. However, this convenience comes at a cost, as cybercriminals are constantly looking for ways to exploit security vulnerabilities in mobile applications. Among the several varieties of malicious applications, zero-day malware is particularly dangerous since it cannot be removed by antivirus software. To detect zero-day Android malware, this paper introduces a novel approach based on generative adversarial networks (GANs), which generates new frequencies of feature vectors from system calls. In the proposed approach, the generator is fed with a mixture of real samples and noise, and then trained to create new samples, while the discriminator model aims to classify these samples as either real or fake. We assess the performance of our model through different measures, including loss functions, the Frechet Inception distance, and the inception score evaluation metrics.
Measuring the Influence of Financial Literation and Inclusion on Investment Interest with Social Background as Moderating Variables

The lack of Sharia financial literacy leads to a lack of understanding among the public regarding the potential investment opportunities available in the Sharia financial system, causing them to be reluctant to invest. Furthermore, limited Sharia financial inclusion also hinders the increase in investment interest. Limited access to Sharia financial products and services, such as Sharia banking accounts, sharia insurance or sukuk, makes it difficult for the public to engage in investments that align with Sharia principles. This research aims to measure the influence of Sharia financial literacy, Sharia financial inclusion, and social background on the investment interest of Sharia Economics students at IAIN Syekh Nurjati Cirebon. The research population consists of students from the Economics Study Program. A quantitative approach is used in this study. The research sample consists of 88 randomly selected respondents from the population. The results of the analysis show that Sharia financial literacy has a positive and significant influence on the investment interest of Sharia Economics students. Similarly, Sharia financial inclusion also has a positive and significant influence on investment interest. Additionally, both Sharia financial literacy and Sharia financial inclusion have a simultaneous positive and significant influence on investment interest. However, the social background variable does not moderate the relationship between Sharia financial literacy, Sharia financial inclusion, and investment interest.
Approaches to Asian Option Pricing with Discrete Dividends

The method and characteristics of several approaches to the pricing of discretely monitored arithmetic Asian options on stocks with discrete, absolute dividends are described. The contrast between method behaviors for options with an Asian tail and those with monitoring throughout their lifespan is emphasized. Rates of convergence are confirmed, but greater focus is put on actual performance in regions of accuracy which are realistic for use by practitioners. A hybrid approach combining Curran's analytical approximation with a two-dimensional finite difference method is examined with respect to the errors caused by the approximating assumptions. For Asian tails of equidistant monitoring dates, this method performs very well, but as the scenario deviates from the method's ideal conditions, the errors in the approximation grow unfeasible. For general monitoring straightforward solution of the full three-dimensional partial differential equation by finite differences is highly accurate but suffers from rapid degradation in performance as the monitoring interval increases. For options with long monitoring intervals a randomized quasi-Monte Carlo method with control variate variance reduction stands out as a powerful alternative.
The Inclusion of the Volume-Price-Product Factor for the Trend Forecasting of Futures Time Series Data

Predicting time series data involves extracting features and forecasting trends from observed phenomena. Although deep learning algorithms are widely used in this field, their emphasis on prediction accuracy may not be optimal for futures time series data. For a futures time series, achieving high prediction accuracy alone is not sufficient. This is because, in some cases, ten accurate predictions may not compensate for a single loss. Therefore, a high accuracy rate does not necessarily translate into good returns. Existing methods have yet to provide practical and reliable approaches for predicting futures time series data. The primary contributions of this study are as follows: First, we employ the Vapnik-Chervonenkis (VC) dimension and error function from the perspective of binary classification for futures time series data to elucidate the generalization ability of the simple moving average model. Furthermore, we offer theoretical guidance to enhance predictive performance by introducing effective factors (i.e., features) that positively impact prediction results. By incorporating influential features, the discrimination of the loss function can be enhanced, making it easier to adjust the parameters and minimize the overall loss function value. Consequently, this improves the overall return rate, which is achieved by introducing additional factors to minimize the error values in the loss function. This explains why the proposed moving average model, enhanced by the introduction of the volume-price-product factor, achieves good prediction performance.
Survivin inhibition ameliorates liver fibrosis by inducing hepatic stellate cell senescence and depleting hepatic macrophage population

Abstract Persistent activation of hepatic stellate cells (HSCs) in the injured liver leads to the progression of liver injury from fibrosis to detrimental cirrhosis. In a previous study, we have shown that survivin protein is upregulated during the early activation of HSCs, which triggers the onset of liver fibrosis. However, the therapeutic potential of targeting survivin in a fully established fibrotic liver needs to be investigated. In this study, we chemically induced hepatic fibrosis in mice using carbon tetrachloride (CCl4) for 6 weeks, which was followed by treatment with a survivin suppressant (YM155). We also evaluated survivin expression in fibrotic human liver tissues, primary HSCs, and HSC cell line by histological analysis. αSMA+ HSCs in human and mice fibrotic liver tissues showed enhanced survivin expression, whereas the hepatocytes and quiescent (qHSCs) displayed minimal expression. Alternatively, activated M2 macrophage subtype induced survivin expression in HSCs through the TGF‐β‐TGF‐β receptor‐I/II signaling. Inhibition of survivin in HSCs promoted cell cycle arrest and senescence, which eventually suppressed their activation. In vivo, YM155 treatment increased the expression of cell senescence makers in HSCs around fibrotic septa such as p53, p21, and β‐galactosidase. YM155 treatment in vivo also reduced the hepatic macrophage population and inflammatory cytokine expression in the liver. In conclusion, downregulation of survivin in the fibrotic liver decreases HSC activation by inducing cellular senescence and modulating macrophage cytokine expression that collectively ameliorates liver fibrosis.
Moving towards the semantic web: enabling new technologies through the semantic annotation of social contents

La Web Social ha causat un creixement exponencial dels continguts disponibles deixant enormes quantitats de recursos textuals electronics que sovint aclaparen els usuaris. Aquest volum d’informacio es d’interes per a la comunitat de mineria de dades. Els algorismes de mineria de dades exploten caracteristiques de les entitats per tal de categoritzar-les, agrupar-les o classificar-les segons la seva semblanca. Les dades per si mateixes no aporten cap mena de significat: han de ser interpretades per esdevenir informacio. Els metodes tradicionals de mineria de dades no tenen com a objectiu “entendre” el contingut d’un recurs, sino que extreuen valors numerics els quals esdevenen models en aplicar-hi calculs estadistics, que nomes cobren sentit sota l’analisi manual d’un expert. Els darrers anys, motivat per la Web Semantica, molts investigadors han proposat metodes semantics de classificacio de dades capacos d’explotar recursos textuals a nivell conceptual. Malgrat aixo, normalment aquests metodes depenen de recursos anotats previament per poder interpretar semanticament el contingut d’un document. L’us d’aquests metodes esta estretament relacionat amb l’associacio de dades i el seu significat. Aquest treball es centra en el desenvolupament d’una metodologia generica capac de detectar els trets mes rellevants d’un recurs textual descobrint la seva associacio semantica, es a dir, enllacant-los amb conceptes modelats a una ontologia, i detectant els principals temes de discussio. Els metodes proposats son no supervisats per evitar el coll d’ampolla generat per l’anotacio manual, independents del domini (aplicables a qualsevol area de coneixement) i flexibles (capacos d’analitzar recursos heterogenis: documents textuals o documents semi-estructurats com els articles de la Viquipedia o les publicacions de Twitter). El treball ha estat avaluat en els ambits turistic i medic. Per tant, aquesta dissertacio es un primer pas cap a l'anotacio semantica automatica de documents necessaria per possibilitar el cami cap a la visio de la Web Semantica.
Research on Minimizing Payment Cost of Multiple Cloud Service Provider

— Todays many businesses are shifting their workloads to cloud storage to save capital costs for building and maintaining hardware infrastructures and avoid the complexity of managing data centers. Cloud computing has become a popular commercial service. The CSP (Cloud Service Provider) provides data storage services in which includes Get and Put function, using this geographically distributed data centers around the world. Selection of different CSPs datacenters and cloud customers facing two challenges, first one is how to allocating data to the datacenters in worldwide to satisfy application Service level objectives (SLO) requirement which includes both data availability and retrieval latency and second is how to allocate reserve resources & data in the datacenters which belongs to different CSP to minimizing payment cost. Find out the solution of these challenges firstly used integer-programming techniques for handles cost minimization problems. Here used three techniques for reducing service latency and payment cost first multicast-based data transferring second coefficient based data reallocation and third is request redirection based congestion and using PPM-C (Prediction by Partial Matching-Cloud) data compression technique, this helps to reduce storage cost and data transfer computing time