Miracle-dz/UniFAR-FTUs huggingface dataset:
MODELLI G THE ABRASIVE WEAR OF CO CRETE FOR PROBABILISTIC SERVICE LIFE PREDICTIO OF HYDRAULIC STRUCTURES

Regarding the durability of hydraulic structures, the mechanical wear of the concrete edge zones represents a key exposure. Abrasive wear of concrete in hydraulic structures generally causes a reduction in service life as well as high costs due to necessary repair work and corresponding production downtimes. The knowledge about abrasive mechanisms of concrete and its time development is still insufficient. Furthermore no standardized test method exists so far for the realistic verification of the abrasion resistance of concrete. An appropriate method for testing the abrasion resistance of concrete was developed. In order to identify the relevant material parameters, which affect the abrasion resistance of concrete, typical hydraulic engineering concretes were investigated with respect to their abrasion behaviour. The time dependent abrasion of concrete has been experimentally analyzed in detail. These results formed the basis for the derivation of a sophisticated degradation model. With the help of the developed model and the use of statistical tools, it is now possible to predict the service life of hydraulic structures under abrasion loads. 1. I TRODUCTIO Abrasive wear damage results from the abrasive effects of waterborne sand, gravel, rocks, and other debris, which circulate over concrete surfaces. These damages on the hydraulic structures are ranging from a few centimetres to several decimetres [1, 2]. The rate of abrasion wear is dependent on the kind of exposure and on the durability of concrete. The main factors regarding the exposure are quantity, hardness, shape and size of the rubble and the velocity of the water. The concrete durability is influenced by the hardness, shape and size of the aggregates, the aggregate/paste bond and the hardness and elasticity of the paste [2, 3]. Abrasive wear is a complex process consisting of rubbing and impact action. In respect to standard test methods, there is no procedure which can reproduce the natural abrasive wear conditions on hydraulic structures completely. For example, the standard test methods [4, 5] 4 2nd International RILEM Workshop on Concrete Durability and Service Life Planning (ConcreteLife’09) 7-9 September 2009, Haifa, Israel
Blind In/On-Path Attacks and Applications to VPNs

Protecting network protocols within an encrypted tunnel, using technologies such as Virtual Private Networks (VPNs), is increasingly important to millions of users needing solutions to evade censorship or protect their trafﬁc against in/on-path observers/attackers. In this paper, we present a series of attacks from two threat models: an attacker that can inject spoofed packets into the network stack of a VPN client (called client-side), and an attacker that can spoof packets on the Internet and send them to a VPN server (called server-side). In both cases, we assume that the attacker is in/on-path, and can count encrypted bytes or packets over time. In both threat models, we demonstrate attacks to infer the existence of, interfere with, or inject data into TCP connections forwarded through the encrypted VPN tunnel. In the server-side threat model, we also demonstrate an attack to hijack tunneled DNS queries and completely remove the protections of the VPN tunnel. For the attacks presented in this paper, we (1) assess their feasibility in terms of packet rates and timing; (2) test their applicability against a broad range of VPN technologies, types, and vendors; and (3) consider practical issues with respect to real-world attacks. We followed an ethical disclosure process for all attacks presented in this paper. Client-side attacks were addressed with two CVEs and partially mitigated by a series of updates from some operating system and VPN client vendors. Server-side attacks have not been addressed and are still feasible with all operating systems and VPN servers that we tested.
Identiﬁcation of Fake News by Contradiction Detection in Texts Identiﬁcaci´on de Noticias Falsas mediante Detecci´on de Contradicciones en Textos

: The dissemination of fake news through digital media has increased sig-niﬁcantly in recent years. The volume of generation of this kind of news is so high that it is impossible to verify them manually, being necessary to use technologies that allow automating the veriﬁcation process. This work is focus on creating models and technologies that allow supporting the fake news detection process. The main advances in the area are showed, as well as planning to carry out the research. Finally, the results obtained so far in the research are explained.
A Framework at the Economic Growth Nexus, E-Government Portals: “Would It Help?”

: This paper presents a model-derived framework to explain the existing relationship between the eGovernment domain and the Economic Growth domain. The aim is to provide a strategic framework that can support decision-makers when promoting digital transformation. The existing empirical literature shows that aspects of this relationship can be realized through the competitiveness index as a strategic mediator. The Global Competitiveness Index parameters are used for matching indicators from both domains. In this work, the composite indexes of the eGovernment Develop0ment Index (EGDI) are studied in the context of selected parameters for measuring the Economic Growth. The population for the statistical analysis is done on all state members of the UN eGov used in the biannual eGovernment survey. It presents a genuine econometric model where results are pulled out from cross-sectional data analysis for 193 observations over the year 2018. The GDP per capita is selected as the output function, whereas the right-hand side is selected parameters for three main factors; Trade, Labor, and Technology. Trade predictors are selected to be Inflation –as a GDP deflator - FDI, number of days to start a business, and Import, and Export. As for Labor, predictors are selected to be HCI -Infant Mortality, Adult Literacy, and School enrollment for Tertiary education. Whereas for Technology predictors are selected to be Fixed and Mobile (Cellular) Telephone Subscribers, Fixed and Wireless Broadband subscribers, Internet users, EGDI, eParticipation. Findings agree with the literature and the model proves the positive influence of eGov on economic growth. The contribution of this research work is bi-fold. Firstly, the new fitness function
MEDINA: Improving Cloud Services trustworthiness through continuous audit-based certification

One of the reasons of the still limited adoption of Cloud Computing in the EU is the EU customers’ perceived lack of security and transparency in this technology. Cloud service providers (CSPs) usually rely on security certifications as a mean to improve transparency and trustworthiness, however European CSPs still face multiple challenges for certifying their services (e.g., fragmentation in the certification market, and lack of mutual recognition). In this context, the EU Cybersecurity Act (EU CSA) proposes improving customer's trust in the European ICT market through a European certification scheme (EUCS). The proposed cloud security certification scheme conveys new technological challenges including the notion of automated monitoring for the whole supply chain, which needs to be solved in order to bring all the expected benefits to EU cloud providers and customers. In this context, MEDINA proposes a framework for supporting a continuous audit-based certification for CSPs based on EU CSA’s scheme for cloud security cer tification. MEDINA will tackle challenges in areas like security validation/ testing, machine-readable certification language, cloud security performance, and audit evidence management. MEDINA will provide and empirically validate sustainable outcomes in order to benefit EU adopters.
Impossible Intentions

Philosophers are divided on whether it is possible to intend believed-impossible outcomes. Several thought experiments in the action theory literature suggest that this is conceptually possible, though they have not been tested in ordinary social cognition. We conducted three experiments to determine whether, on the ordinary view, it is conceptually possible to intend believed-impossible outcomes. Our findings indicate that participants firmly countenance the possibility of intending believed-impossible outcomes, suggesting that it is conceptually possible to intend to do something that one believes is impossible.
A Flamelet/Progress-variable Approach for the Simulation of Turbulent Combustion of Real Gas Mixtures

The industrial and scientific communities are devoting major research efforts to identify and assess critical technologies for new advanced propulsive concepts: combustion at high pressure has been assumed as a key issue to achieve better propulsive performance and lower environmental impact, as long as the replacement of hydrogen with a hydrocarbon, to reduce the costs related to ground operations (propellant handling, infrastructure and procedures) and increase flexibility. For the class of engines of interest in this work, namely, liquid-propellant rocket engines, the pressure is always supercritical, whereas the temperature could be either sub- or super-critical; however, propellants are typically injected into an environment that exceeds the critical temperature and pressure for both fuel and oxidizer, therefore a fast transition to a supercritical state is observed. In such a condition, it is possible to neglect the liquid phase and treat the liquid as a “dense” gaseous jet. However, the ideal gas equation of state is not suitable for computing the correct p − v − T relationship for oxygen and fuel at the operating pressure and temperature typical of LOx/HC rocket combustion chambers. Therefore, a suitable equation of state together with adequate model equations for the transport properties are employed. Starting from this background, the current work provides a model for the numerical simulation of highpressure turbulent combustion employing detailed chemistry description, embedded in a Reynolds averaged Navier-Stokes equations solver with a Low Reynolds number k − ω turbulence model.
A causal model for health-related quality of life among Thai adolescents with congenital heart disease

Purpose – The objective of this study was to examine the causal relationships among sex, social support, disease severity, symptoms, physical activity, general health perception and health-related quality of life (HRQOL) in Thai adolescents with congenital heart disease (CHD). Design/methodology/approach – A cross-sectional study was conducted using convenience sampling to recruit 200 Thai adolescents with CHD aged between 13 and18 years from four tertiary university hospitals in Bangkok. Interview method and self-administered questionnaires were used for data collection held between November 2018 and February 2019. Data were analyzed using path analysis to test the hypothesized model of the relationships. Findings – The causal model of HRQOL revealed a good fit with the data. This model of relationship could explain 48% of the variances in HRQOL. Only direct effects of disease severity, symptoms and social support on HRQOL were found while indirect effects were not. Originality/value –Due to the lack of study focusing on factors influencing HRQOL in Thai adolescents with CHD, this empirical study provides the evidence in Thai literature. The modifiable influencing factors for HRQOL found in this study can be manipulated through psycho-educational intervention given to the adolescents and significant others, especially parents, to help increase quality of life in adolescents with CHD.
Outbreak induction from floral stem orchid Phalaenopsis spp. (Blume) in vitro

The induction of outbreaks in vitro is a strategy of the vegetable micropropagation used to increase the production of orchids, in comparison with the traditional crop that requires a long period of time to obtain a substantial population for commercialization. The objective of this study was to induce in vitro outbreak formation from the flower outbreaks of the orchid Phalaenopsis spp. , using an organogenesis route. Four mineral salt formulations (MS 100%, MS 50%, Knudson and NPK Fertilizer 18-9-18/15-30-15) and four varieties of Phalaenopsis spp. to determine the viability of explants. Two growth regulators (6.2-benzylaminopurine BAP 20.2 μM alone and combined with naphthalenacetic acid ANA 5.37 μM) were evaluated to determine outbreak induction. The results indicate that oxidation in explants depends on the mineral salts of the culture media, as well as the variety of orchid; outbreak induction depends on the growth regulator used. BAP/ANA regulators produced 2.4 outbreaks per outbreak against 2 in BAP. The MS 50% medium proved to be the best option to maintain the viability of the flower outbreaks.
Development and Validation of an Immune-Related Gene Pair Signature in Skin Cutaneous Melanoma

signature can noticeably divide SKCM patients into high-risk group and low-risk group with significantly different prognoses. Many biological activities such as gene family were enriched among the genes in our IRGP signature. While analyzing the risk signature and clinical characteristics, there was a large difference in the risk score between T stage and tumor stage grouping. Finally, we constructed a nomogram and forest plots of the risk score and clinical features. Conclusion: In summary, we developed a robust 13-IRGP prognostic signature in SKCM, which can identify and provide new insights into immunological biomarkers.
Deep Parametric Time-to-Event Regression with Time-Varying Covariates

Time-to-event regression in healthcare and other domains, such as predictive maintenance, require working with time-series (or time-varying) data such as continuously monitored vital signs, electronic health records, or sensor readings. In such scenarios, the event-time distribution may have temporal dependencies at different time scales that are not easily captured by classical survival models that assume training data points to be independent. In this paper, we describe a fully parametric approach to model censored time-to-event outcomes with time varying covariates. It involves learning representations of the input temporal data using Recurrent Neural Networks such as LSTMs and GRUs, followed by describing the conditional event distribution as a fixed mixture of parametric distributions. The use of the recurrent neural networks allows the learned representations to model long-term dependencies in the input data while jointly estimating the Time-to-Event. We benchmark our approach on MIMIC III: a large, publicly available dataset collected from Intensive Care Unit (ICU) patients, focusing on predicting duration of their ICU stays and their short term life expectancy, and we demonstrate competitive performance of the proposed approach compared to established time-to-event regression models.
Case Based Reasoning for managing urban infrastructure complex technological objects

Modern urban infrastructure systems are complex technological objects. The stability of their work is important not only for life support systems, but also for the safety of people and nature. The systems are supported by monitoring and prompt troubleshooting. Dangerous situations at complex technological facilities can have fatal consequences for humans, nature and infrastructure. A high level of responsibility, together with a variety of possible situations at a complex technological facility, determines the relevance of the tasks of intellectual support for decision-making. At the same time, there are not enough data volumes for machine learning of such systems. The use of hybrid artificial intelligence models that combine both machine learning and knowledge-based inference methods is promising. The authors of the article investigate the possibilities of creating hybrid models based on the general idea of the case based reasoning (CBR) method. To implement the CBR method, an ontological model of a complex technological object is proposed in the work. On this basis, a formalized representation of situations on a complex object has been developed, an approach has been proposed for identifying and selecting situations, which takes into account their structural and parametric proximity. The work provides a basis for further development of algorithmic and software for the in-demand systems for intelligent management of urban infrastructure facilities using CBR, fuzzy logic and neural networks.
Closed-crack characterization using sparse decomposition based on nonlinear Lamb waves: a numerical study

It is known that the nonlinear interaction between closed cracks and primary elastic waves can lead to the generation of a second-harmonic component. In this work, the temporal S0-mode Lamb wave and closed-crack-interaction-induced second harmonic (abbr. closed-crack-induced second harmonic, CCISH) Lamb waves that were extracted for closed-crack characterization through a weighted and structured sparse decomposition (WSSD) algorithm. A 3D finite-element model of a thin aluminum plate based on the bilinear stiffness model is established to numerically study the nonlinear interaction between closed cracks and primary Lamb waves. Under S0-mode Lamb wave excitation, the temporal S0-mode CCISH signals are extracted from the original response using the pulse-inversion technique and a band-pass filter. A dictionary, whose columns are the normalized predicted CCISH waveforms generated by the proposed analytical CCISH generation and propagation model, is built to sparsely decompose the extracted array CCISH signals using adaptive weights. Images are generated using sparse coefficients to define the pixel values after solving the WSSD problem. Closed cracks with different locations and orientations are simulated, and imaging results show that the proposed method can characterize their location and orientation but not their length from the generated images.
Application of AFM, TEM, and NTA for characterization of exosomes produced by placenta-derived mesenchymal cells

Exosomes and microvesicles usually require characterization at the single-particle level. The most commonly used methods for this purpose are transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). However, atomic force microscopy (AFM) is also a promising method for the characterization of exosomes, and its opportunities seem underestimated. Here we describe the characterization of exosomes isolated from the conditioned media of human placenta cells using AFM, TEM, and NTA; we focus on the technical aspects of AFM imaging and data processing.
An intelligent fault diagnosis method for an electromechanical actuator based on sparse feature and long short-term network

Electromechanical actuators (EMAs), as the new generation of actuators, have an important impact on the safety of aircraft. With the development of measurement technology, a large amount of data provides a broad prospect for the data-based fault diagnosis method. However, the existence of redundant data increases the burden of software and hardware. Therefore, a semi-supervised sparse auto-encoder (SSAE) is employed to prune observed data based on sparsity analysis. Moreover, temporal and spatial relationships are explored by a multi-channel long short-term network to build a time series model, so as to perform fault detection and isolation based on the difference between its estimated and observed values. Due to its sparse feature extraction capability, the SSAE can improve the fault isolation accuracy while pruning observed data. Verification results confirm that the proposed method can effectively diagnose EMA faults.
Temperature compensated differential acoustic sensor for CO2 sensing

Temperature must be accounted for in order to provide accurate measurements in acoustic wave resonator based gas sensors. We present a flexible printed flexural plate wave (FPW) sensor for CO2 monitoring employing temperature compensation. The FPW sensor is based on a differential interdigitated transducers (IDTs) structure consisting of an input IDT and two identical output IDTs with same distance from the input IDT. Among the two outputs of the differential structure, one is sensitive to the target gas CO2 and the other one is insensitive to the target gas. The difference between the two output signals’ resonant frequencies is considered as the sensor’s differential response. Experiments at 25 ∘C and 45 ∘C show that the sensor’s differential response changes linearly with CO2 concentration over 20–3000 ppm range. The differential responses at both temperatures are very similar showing the ability of the sensor’s structure to compensate for the temperature change. Sensitivity and limit of detection for CO2 sensing are measured to be 543 Hz ppm−1 and 5.6 ppm, respectively. Further, the sensor shows good repeatability and low hysteresis. Sensor’s responses are mostly measured by measuring the scattering parameter, forward transmission (S21) between each output IDT and input IDT of the sensor in frequency domain. In the last section, a prototype time domain measurement system is demonstrated to show the feasibility of the time domain measurements with the sensors. The sensor with the potential to be printed with roll-to-roll printing can be a good candidate for CO2 monitoring where temperature varies.
Climate sensitivity indices and their relation with projected temperature change in CMIP6 models

Equilibrium climate sensitivity (ECS) and transient climate response (TCR) are both measures of the sensitivity of the climate system to external forcing, in terms of temperature response to CO2 doubling. Here it is shown that, of the two, TCR in current-generation coupled climate models is better correlated with the model projected temperature change from the pre-industrial state, not only on decadal time scales but throughout much of the 21st century. For strong mitigation scenarios the difference persists until the end of the century. Historical forcing on the other hand has a significant degree of predictive power of past temperature evolution in the models, but is not relevant to the magnitude of temperature change in their future projections. Regional analysis shows a superior predictive power of ECS over TCR during the latter half of the 21st century in areas with slow warming, illustrating that although TCR is a better predictor of warming on a global scale, it does not capture delayed regional feedbacks, or pattern effects. The transient warming at CO2 quadrupling (T140) is found to be correlated with global mean temperature anomaly for a longer time than TCR, and it also better describes the pattern of regional temperature anomaly at the end of the century. Over the 20th century, there is a weak correlation between total forcing and ECS, contributing to, but not determining, the model agreement with observed warming. ECS and aerosol forcing in the models are not correlated.
Practitioners’ views on new teaching material for introducing quantum optics in secondary schools

In an earlier contribution in Physics Education (Bitzenbauer and Meyn 2020 Phys. Educ. 55 055031), we presented a new teaching sequence on quantum optics on the secondary school level, and we reported on promising results of a first pilot study concerning its learning effectiveness. In the sense of design-based research, the developed teaching material is now being revised in several iteration steps and optimised through feedback from teachers from the field in order to favour the implementation of the new teaching approach to quantum physics in secondary schools. We present the design principles from the literature that our teaching material’s development is based on and report on a survey of physics teachers’ practical experiences with our teaching material.